survey

Evolutionary Large-Scale Multi-Objective Optimization: A Survey

Authors:

Yaochu JinAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 8

Article No.: 174, Pages 1 - 34

https://doi.org/10.1145/3470971

Published: 04 October 2021 Publication History

Abstract

Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in solving various optimization problems, but their performance may deteriorate drastically when tackling problems containing a large number of decision variables. In recent years, much effort been devoted to addressing the challenges brought by large-scale multi-objective optimization problems. This article presents a comprehensive survey of stat-of-the-art MOEAs for solving large-scale multi-objective optimization problems. We start with a categorization of these MOEAs into decision variable grouping based, decision space reduction based, and novel search strategy based MOEAs, discussing their strengths and weaknesses. Then, we review the benchmark problems for performance assessment and a few important and emerging applications of MOEAs for large-scale multi-objective optimization. Last, we discuss some remaining challenges and future research directions of evolutionary large-scale multi-objective optimization.

References

[1]

Yousef Abdi and Mohammad-Reza Feizi-Derakhshi. 2020. Hybrid multi-objective evolutionary algorithm based on search manager framework for big data optimization problems. Applied Soft Computing 87 (2020), 105991.

[2]

Luigi Amoroso. 1938. Vilfredo pareto. Econometrica 6, 1 (1938), 1–21.

[3]

Luis Miguel Antonio and Carlos A. Coello Coello. 2013. Use of cooperative coevolution for solving large scale multiobjective optimization problems. In Proceedings of the 2013 IEEE Congress on Evolutionary Computation. IEEE, Los Alamitos, CA, 2758–2765.

[4]

Luis Miguel Antonio and Carlos A. Coello Coello. 2016. Decomposition-based approach for solving large scale multi-objective problems. In Proceedings of the International Conference on Parallel Problem Solving from Nature. 525–534.

[5]

Luis Miguel Antonio, Carlos A. Coello Coello, Silvia González Brambila, Josué Figueroa González, and Guadalupe Castillo Tapia. 2019. Operational decomposition for large scale multi-objective optimization problems. In Proceedings of the 2019 Annual Genetic and Evolutionary Computation Conference. ACM, New York, NY, 225–226.

Digital Library

[6]

Golnoosh Babaei and Shahrooz Bamdad. 2020. A multi-objective instance-based decision support system for investment recommendation in peer-to-peer lending. Expert Systems with Applications 150, 15 (2020), 113278.

[7]

Hiba Bederina and Mhand Hifi. 2018. A hybrid multi-objective evolutionary optimization approach for the robust vehicle routing problem. Applied Soft Computing 71 (2018), 980–993.

[8]

Irwan Bello, Barret Zoph, Vijay Vasudevan, and Quoc V. Le. Neural architecture search with reinforcement learning. In Proceedings of the 5th International Conference on Learning Representations.

Digital Library

[9]

Nicola Beume, Boris Naujoks, and Michael Emmerich. 2007. SMS-EMOA: Multiobjective selection based on dominated hypervolume. European Journal of Operational Research 181, 3 (2007), 1653–1669.

[10]

Urvesh Bhowan, Mark Johnston, and Mengjie Zhang. Ensemble learning and pruning in multi-objective genetic programming for classification with unbalanced data. In Proceedings of the 24th Australasian Joint Conference on Artificial Intelligence.

Digital Library

[11]

Julian Blank, Kalyanmoy Deb, and Sanaz Mostaghim. Solving the bi-objective traveling thief problem with multi-objective evolutionary algorithms. In Proceedings of the 2017 International Conference on Evolutionary Multi-Criterion Optimization.

Digital Library

[12]

J. Branke, B. Scheckenbach, M. Stein, K. Deb, and H. Schmeck. 2009. Portfolio optimization with an envelope-based multi-objective evolutionary algorithm. European Journal of Operational Research 199, 3 (2009), 684–693.

[13]

Doina Bucur, Giovanni Iacca, Andrea Marcelli, Giovanni Squillero, and Alberto Tonda. Multi-objective evolutionary algorithms for influence maximization in social networks. In Proceedings of the 2017 European Conference on the Applications of Evolutionary Computation.

[14]

Jose Caceres-Cruz, Pol Arias, Daniel Guimarans, Daniel Riera, and Angel A. Juan. 2015. Rich vehicle routing problem: Survey. ACM Computing Surveys 47, 2 (2015), 1–28.

Digital Library

[15]

Qing Cai, Lijia Ma, and Maoguo Gong. 2014. A survey on network community detection based on evolutionary computation. International Journal of Bio-Inspired Computation 8, 2 (2014), 84–98.

Digital Library

[16]

Xinye Cai, Yexing Li, Zhun Fan, and Qingfu Zhang. 2015. An external archive guided multiobjective evolutionary algorithm based on decomposition for combinatorial optimization. IEEE Transactions on Evolutionary Computation 19, 4 (2015), 508–523.

Digital Library

[17]

Bin Cao, Jianwei Zhao, Yu Gu, Yingbiao Ling, and Xiaoliang Ma. 2020. Applying graph-based differential grouping for multiobjective large-scale optimization. Swarm and Evolutionary Computation 53 (2020), 100626.

[18]

Bin Cao, Jianwei Zhao, Zhihan Lv, and Xin Liu. 2017. A distributed parallel cooperative coevolutionary multiobjective evolutionary algorithm for large-scale optimization. IEEE Transactions on Industrial Informatics 13, 4 (2017), 2030–2038.

[19]

Huangke Chen, Ran Cheng, Jinming Wen, Haifeng Li, and Jian Weng. 2020. Solving large-scale many-objective optimization problems by covariance matrix adaptation evolution strategy with scalable small subpopulations. Information Sciences 509 (2020), 457–469.

Digital Library

[20]

Huangke Chen, Xiaomin Zhu, Witold Pedrycz, Shu Yin, Guohua Wu, and Hui Yan. 2018. PEA: Parallel evolutionary algorithm by separating convergence and diversity for large-scale multi-objective optimization. In Proceedings of the 2018 IEEE International Conference on Distributed Computing Systems. IEEE, Los Alamitos, CA, 223–232.

[21]

Wenxiang Chen, Thomas Weise, Zhenyu Yang, and Ke Tang. 2010. Large-scale global optimization using cooperative coevolution with variable interaction learning. In Proceedings of the 2010 International Conference on Parallel Problem Solving from Nature. 300–309.

Digital Library

[22]

Fan Cheng, Jiabin Chen, Jianfeng Qiu, and Lei Zhang. 2020. A subregion division based multi-objective evolutionary algorithm for SVM training set selection. Neurocomputing 394 (2020), 70–83.

[23]

Ran Cheng. 2016. Nature Inspired Optimization of Large Problems. Ph.D. Dissertation. University of Surrey.

[24]

Ran Cheng and Yaochu Jin. 2015. A competitive swarm optimizer for large scale optimization. IEEE Transactions on Cybernetics 45, 2 (2015), 191–204.

[25]

Ran Cheng, Yaochu Jin, Kaname Narukawa, and Bernhard Sendhoff. 2015. A multiobjective evolutionary algorithm using Gaussian process-based inverse modeling. IEEE Transactions on Evolutionary Computation 19, 6 (2015), 838–856.

Digital Library

[26]

Ran Cheng, Yaochu Jin, Markus Olhofer, and Bernhard Sendhoff. 2017. Test problems for large-scale multiobjective and many-objective optimization. IEEE Transactions on Cybernetics 47, 12 (2017), 4108–4121.

[27]

Tsung-Che Chiang and Wei-Huai Hsu. 2014. A knowledge-based evolutionary algorithm for the multiobjective vehicle routing problem with time windows. Computers & Operations Research 45 (2014), 25–37.

Digital Library

[28]

Tinkle Chugh, Karthik Sindhya, Jussi Hakanen, and Kaisa Miettinen. 2019. A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms. Soft Computing 23 (2019), 3137–3166.

Digital Library

[29]

Tinkle Chugh, Karthik Sindhya, Kaisa Miettinen, Yaochu Jin, Tomas Kratky, and Pekka Makkonen. 2017. Surrogate-assisted evolutionary multiobjective shape optimization of an air intake ventilation system. In Proceedings of the 2017 IEEE Congress on Evolutionary Computation. IEEE, Los Alamitos, CA.

[30]

Carlos Alberto de Araújo Padilha, Dante Augusto Couto Barone, and Adriao Duarte Dória Neto. 2016. A multi-level approach using genetic algorithms in an ensemble of least squares support vector machines. Knowledge-Based Systems 106 (2016), 85–95.

Digital Library

[31]

Kalyanmoy Deb and Ram Bhusan Agrawal. 1995. Simulated binary crossover for continuous search space. Complex Systems 9, 4 (1995), 115–148.

[32]

Kalyanmoy Deb and Mayank Goyal. 1996. A combined genetic adaptive search (GeneAS) for engineering design. Computer Science and Informatics 26, 4 (1996), 30–45.

[33]

Kalyanmoy Deb and Himanshu Jain. 2013. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation 18, 4 (2013), 577–601.

[34]

Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and T. Meyarivan. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 2 (2002), 182–197.

Digital Library

[35]

Kalyanmoy Deb, Lothar Thiele, Marco Laumanns, and Eckart Zitzler. 2005. Scalable test problems for evolutionary multiobjective optimization. In Evolutionary Multiobjective Optimization. Springer, 105–145.

[36]

Kalyanmoy Deb and Santosh Tiwari. 2008. Omni-optimizer: A generic evolutionary algorithm for single and multi-objective optimization. European Journal of Operational Research 185 (2008), 1062–1087.

[37]

Yepeng Deng, Chunkai Zhang, and Xuan Wang. A multi-objective examples generation approach to fool the deep neural networks in the black-box scenario. In Proceedings of the 2019 IEEE International Conference on Data Science in Cyberspace. IEEE, Los Alamitos, CA.

[38]

Imen Harbaoui Dridi, Ryan Kammarti, Mekki Ksouri, and Pierre Borne. 2011. Multi-objective optimization for the m-PDPTW: Aggregation method with use of genetic algorithm and lower bounds. International Journal of Computers, Communications & Control 6, 2 (2011), 246–257.

[39]

Wei Du, Le Tong, and Yang Tang. 2018. A framework for high-dimensional robust evolutionary multi-objective optimization. In Proceedings of the 2018 Annual Genetic and Evolutionary Computation Conference. ACM, New York, NY, 1791–1796.

Digital Library

[40]

Wei Du, Weimin Zhong, Yang Tang, Wenli Du, and Yaochu Jin. 2018. High-dimensional robust multi-objective optimization for order scheduling: A decision variable classification approach. IEEE Transactions on Industrial Informatics 15, 1 (2018), 293–304.

[41]

R. Eberhart and J. Kennedy. 1995. A new optimizer using particle swarm theory. In Proceedings of the 6th International Symposium on Micro Machine and Human Science. IEEE, Los Alamitos, CA, 39–43.

[42]

Matthias Ehrgott. 2005. Multicriteria Optimization. Springer Science & Business Media.

[43]

M. T. M. Emmerich, K. C. Giannakoglou, and B. Naujoks. 2006. Single- and multi-objective evolutionary optimization assisted by Gaussian random field metamodels. IEEE Transactions on Evolutionary Computation 10, 4 (2006), 421–439.

Digital Library

[44]

P. Shahsamandi Esfahani and A. Saghaei. 2017. A multi-objective approach to fuzzy clustering using ITLBO algorithm. Journal of AI and Data Mining 5, 2 (2017), 307–317.

[45]

Jesús Guillermo Falcón-Cardona and Carlos A. Coello Coello. 2020. Indicator-based multi-objective evolutionary algorithms: A comprehensive survey. ACM Computing Surveys 53, 2 (2020), 1–35.

Digital Library

[46]

Zhun Fan, Yi Fang, Wenji Li, Jiewei Lu, and Xinye Cai. 2017. A comparative study of constrained multi-objective evolutionary algorithms on constrained multi-objective optimization problems. In Proceedings of the 2017 IEEE Congress on Evolutionary Computation. IEEE, Los Alamitos, CA.

[47]

Zhun Fan, Wenji Li, Xinye Cai, Li Hui, and Erik D. Goodman. 2019. Push and pull search for solving constrained multi-objective optimization problems. Swarm and Evolutionary Computation44 (2019), 665–679.

[48]

Jonathan E. Fieldsend, John Matatko, and Ming Peng. Cardinality constrained portfolio optimisation. In Proceedings of the 5th International Conference on Intelligent Data Engineering and Automated Learning.

[49]

Asja Fischer and Christian Igel. 2012. An introduction to restricted Boltzmann machines. In Proceedings of the Iberoamerican Congress on Pattern Recognition. 14–36.

[50]

Abel García-Nájera and Antonio López-Jaimes. 2018. An investigation into many-objective optimization on combinatorial problems: Analyzing the pickup and delivery problem. Swarm and Evolutionary Computation 38 (2018), 218–230.

[51]

Mario Garza-Fabre, Julia Handl, and Joshua Damian Knowles. 2018. An improved and more scalable evolutionary approach to multiobjective clustering. IEEE Transactions on Evolutionary Computation 22, 4 (2018), 515–535.

[52]

Michelle Girvan and Mark E. J. Newman. 2002. Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the United States of America 99, 12 (2002), 7821–7826.

[53]

Maoguo Gong, Qing Cai, Xiaowei Chen, and Lijia Ma. 2014. Complex network clustering by multiobjective discrete particle swarm optimization based on decomposition. IEEE Transactions on Evolutionary Computation 18, 1 (2014), 82–97.

[54]

Maoguo Gong, Jia Liu, Hao Li, Qing Cai, and Linzhi Su. 2015. A multiobjective sparse feature learning model for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 26, 12 (2015), 3263–3277.

[55]

Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in Neural Information Processing Systems. Curran Associates, 2672–2680.

Digital Library

[56]

L. Grandinetti, F. Guerriero, F. Pezzella, and O. Pisacane. 2014. The multi-objective multi-vehicle pickup and delivery problem with time windows. Procedia: Social and Behavioral Sciences 111, 5 (2014), 203–212.

[57]

Emrah Hancer, Bing Xue, and Mengjie Zhang. 2020. A survey on feature selection approaches for clustering. Artificial Intelligence Review 53 (2020), 4519–4545.

Digital Library

[58]

J. Handl and Joshua Damian Knowles. 2007. An evolutionary approach to multiobjective clustering. IEEE Transactions on Evolutionary Computation 11, 1 (2007), 56–76.

Digital Library

[59]

Nikolaus Hansen and Andreas Ostermeier. 2001. Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation 9, 2 (2001), 159–195.

Digital Library

[60]

Cheng He, Ran Cheng, Ye Tian, and Xingyi Zhang. 2020. Iterated problem reformulation for evolutionary large-scale multiobjective optimization. In Proceedings of the 2020 IEEE Congress on Evolutionary Computation. IEEE, Los Alamitos, CA.

[61]

Cheng He, Ran Cheng, and Danial Yazdani. 2020. Adaptive offspring generation for evolutionary large-scale multiobjective optimization. IEEE Transactions on Systems, Man, and Cybernetics: Systems. Early access, July 10, 2020.

[62]

Cheng He, Ran Cheng, Chuanji Zhang, Ye Tian, Qin Chen, and Xin Yao. 2018. Evolutionary large-scale multiobjective optimization for ratio error estimation of voltage transformers. IEEE Transactions on Evolutionary Computation 24, 5 (2018), 868–881.

[63]

Cheng He, Shihua Huang, Ran Cheng, Kay Chen Tan, and Yaochu Jin. 2021. Evolutionary multiobjective optimization driven by generative adversarial networks (GANs). IEEE Transactions on Cybernetics 51, 6 (2021), 3129–3142.

[64]

Cheng He, Lianghao Li, Ye Tian, Xingyi Zhang, Ran Cheng, Yaochu Jin, and Xin Yao. 2019. Accelerating large-scale multiobjective optimization via problem reformulation. IEEE Transactions on Evolutionary Computation 23, 6 (2019), 949–961.

Digital Library

[65]

Shan He, Guanbo Jia, Zexuan Zhu, Daniel A. Tennant, Qiang Huang, Ke Tang, Jing Liu, Mirco Musolesi, John K. Heath, and Xin Yao. 2016. Cooperative co-evolutionary module identification with application to cancer disease module discovery. IEEE Transactions on Evolutionary Computation 20, 6 (2016), 874–891.

[66]

Mardé Helbig and Andries P. Engelbrecht. 2014. Benchmarks for dynamic multi-objective optimisation algorithms. ACM Computing Surveys 46, 3 (2014), 37.

Digital Library

[67]

John H. Holland. 1992. Adaptation in Natural and Artificial Systems. MIT Press, Cambridge, MA.

Digital Library

[68]

Wenjing Hong, Ke Tang, Aimin Zhou, Hisao Ishibuchi, and Xin Yao. 2018. A scalable indicator-based evolutionary algorithm for large-scale multiobjective optimization. IEEE Transactions on Evolutionary Computation 23, 3 (2018), 525–537.

[69]

Seyedmohsen Hosseini and Abdullah Al Khaled. 2014. A survey on the imperialist competitive algorithm metaheuristic: Implementation in engineering domain and directions for future research. Applied Soft Computing 24 (2014), 1078–1094.

Digital Library

[70]

Peiqiu Huang and Yong Wang. 2020. A framework for scalable bilevel optimization: Identifying and utilizing the interactions between upper-level and lower-level variables. IEEE Transactions on Evolutionary Computation 24, 6 (2020), 1150–1163.

Digital Library

[71]

Simon Huband, Philip Hingston, Luigi Barone, and Lyndon While. 2006. A review of multiobjective test problems and a scalable test problem toolkit. IEEE Transactions on Evolutionary Computation 10, 5 (2006), 477–506.

Digital Library

[72]

E. Jabir, Vinay V. Panicker, and R. Sridharan. 2015. Multi-objective optimization model for a green vehicle routing problem. Procedia: Social and Behavioral Sciences 189, 15 (2015), 33–39.

[73]

Anil Kumar Jain, M. Narasimha Murty, and P. J. Flynn. 1999. Data clustering: A review. ACM Computing Surveys 31, 3 (1999), 264–323.

Digital Library

[74]

T. Jayabarathi, T. Raghunathan, and A. H. Gandomi. 2018. The bat algorithm, variants and some practical engineering applications: A review. In Nature-Inspired Algorithms and Applied Optimization. Studies in Computational Intelligence, Vol. 744. Springer, 313–330.

[75]

Jun-Rong Jian, Zhi-Hui Zhan, and Jun Zhang. 2020. Large-scale evolutionary optimization: A survey and experimental comparative study. International Journal of Machine Learning and Cybernetics 11 (2020), 729–745.

[76]

Yaochu Jin and J. Branke. 2005. Evolutionary optimization in uncertain environments—A survey. IEEE Transactions on Evolutionary Computation 9, 3 (2005), 303–317.

Digital Library

[77]

Yaochu Jin and Bernhard Sendhoff. 2008. Pareto-based multiobjective machine learning: An overview and case studies. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 38, 3 (2008), 397–415.

Digital Library

[78]

Nicolas Jozefowiez, Fred Glover, and Manuel Laguna. 2008. Multi-objective meta-heuristics for the traveling salesman problem with profits. Journal of Mathematical Modelling and Algorithms 7 (2008), 177–195.

[79]

Nicolas Jozefowiez, Frédéric Semet, and El-Ghazali Talbi. 2002. Parallel and hybrid models for multi-objective optimization: Application to the vehicle routing problem. In Proceedings of the 2002 International Conference on Parallel Problem Solving from Nature.

Digital Library

[80]

Nicolas Jozefowiez, Frédéric Semet, and El-Ghazali Talbi. 2008. Multi-objective vehicle routing problems. European Journal of Operational Research 189, 2 (2008), 293–309.

[81]

David Kempe, Jon Kleinberg, and Éva Tardos. Maximizing the spread of influence through a social network. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY.

Digital Library

[82]

Kedar V. Khandeparkar, Shreevardhan A. Soman, and Gopal Gajjar. 2017. Detection and correction of systematic errors in instrument transformers along with line parameter estimation using PMU data. IEEE Transactions on Power Systems 32, 4 (2017), 3089–3098.

[83]

Shreya Khare, Rahul Aralikatte, and Senthil Mani. 2018. Adversarial black-box attacks on automatic speech recognition systems using multi-objective evolutionary optimization. arXiv:1811.01312.

[84]

Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv:1412.6980.

[85]

Saku Kukkonen and Jouni Lampinen. 2005. GDE3: The third evolution step of generalized differential evolution. In Proceedings of the 2005 IEEE Congress on Evolutionary Computation. IEEE, Los Alamitos, CA, 443–450.

[86]

Mohammed Lalou, Mohammed Amin Tahraoui, and Hamamache Kheddouci. 2018. The critical node detection problem in networks: A survey. Computer Science Review 28 (2018), 92–117.

[87]

Bingdong Li, Jinlong Li, Ke Tang, and Xin Yao. 2015. Many-objective evolutionary algorithms: A survey. ACM Computing Surveys 48, 1 (2015), 13.

Digital Library

[88]

Chengtao Li, David Alvarez-Melis, Keyulu Xu, Stefanie Jegelka, and Suvrit Sra. 2017. Distributional adversarial networks. arXiv:1706.09549.

[89]

Hui Li, Qingfu Zhang, and Jingda Deng. 2016. Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47, 1 (2016), 52–66.

[90]

Hui Li, Qingfu Zhang, Jingda Deng, and Zong-Ben Xu. 2018. A preference-based multiobjective evolutionary approach for sparse optimization. IEEE Transactions on Neural Networks and Learning Systems 29, 5 (2018), 1716–1731.

[91]

Jundong Li, Kewei Cheng, Suhang Wang, Fred Morstatter, Robert P. Trevino, Jiliang Tang, and Huan Liu. 2018. Feature selection: A data perspective. ACM Computing Surveys 50, 6 (2018), 1–45.

Digital Library

[92]

Ke Li, Renzhi Chen, Guangtao Fu, and Xin Yao. 2018. Two-archive evolutionary algorithm for constrained multi-objective optimization. IEEE Transactions on Evolutionary Computation 23, 2 (2018), 303–315.

[93]

Kaiwen Li, Tao Zhang, and Rui Wang. 2021. Deep reinforcement learning for multiobjective optimization. IEEE Transactions on Cybernetics 51, 6 (2021), 3103–3114.

[94]

Minghan Li and Jingxuan Wei. 2018. A cooperative co-evolutionary algorithm for large-scale multi-objective optimization problems. In Proceedings of the 2018 Annual Genetic and Evolutionary Computation Conference. ACM, New York, NY, 1716–1721.

Digital Library

[95]

Xiaodong Li, Ke Tang, Mohammmad Nabi Omidvar, Zhenyu Yang, and Kai Qin. 2013. Benchmark Functions for the CEC’2013 Special Session and Competition on Large-Scale Global Optimization. Technical Report. RMIT University.

[96]

Yangyang Li, Yang Wang, Jing Chen, Licheng Jiao, and Ronghua Shang. 2015. Overlapping community detection through an improved multi-objective quantum-behaved particle swarm optimization. Journal of Heuristics 21, 4 (2015), 549–575.

Digital Library

[97]

Zhangtao Li, Jing Liu, and Kai Wu. 2017. A multiobjective evolutionary algorithm based on structural and attribute similarities for community detection in attributed networks. IEEE Transactions on Cybernetics 48, 7 (2017), 1963–1976.

[98]

Zhihong Li, Lanteng Wu, and Hongting Tang. 2018. Optimizing the borrowing limit and interest rate in P2P system: From borrowers’ perspective. Scientific Programming 2018 (2018), 2613739.

Digital Library

[99]

Zhenyu Liang, Yunfan Li, and Zhongwei Wan. 2020. Large scale many-objective optimization driven by distributional adversarial networks. arXiv:2003.07013.

[100]

D. Lin, S. Wang, and H. Yan. A multiobjective genetic algorithm for portfolio selection. In Proceedings of the 5th International Conference on Optimization: Techniques and Applications.

[101]

Qiuzhen Lin, Jianqiang Li, Zhihua Du, Jianyong Chen, and Zhong Ming. 2015. A novel multi-objective particle swarm optimization with multiple search strategies. IEEE Transactions on Evolutionary Computation 247, 3 (2015), 732–744.

[102]

Hanxiao Liu, Karen Simonyan, and Yiming Yang. DARTS: Differentiable architecture search. In Proceedings of the 7th International Conference on Learning Representations.

[103]

Jing Liu, Yaxiong Chi, Chen Zhu, and Yaochu Jin. 2017. A time series driven decomposed evolutionary optimization approach for reconstructing large-scale gene regulatory networks based on fuzzy cognitive maps. BMC Bioinformatics 18 (2017), 241.

[104]

Jia Liu, Maoguo Gong, Qiguang Miao, Xiaogang Wang, and Hao Li. 2017. Structure learning for deep neural networks based on multiobjective optimization. IEEE Transactions on Neural Networks and Learning Systems 29, 6 (2017), 2450–2463.

[105]

Ruochen Liu, Jin Liu, Yifan Li, and Jing Liu. 2020. A random dynamic grouping based weight optimization framework for large-scale multi-objective optimization problems. Swarm and Evolutionary Computation 55 (2020), 100684.

[106]

Ruochen Liu, Rui Ren, Jin Liu, and Jing Liu. 2020. A clustering and dimensionality reduction based evolutionary algorithm for large-scale multi-objective problems. Applied Soft Computing 89 (2020), 106120.

Digital Library

[107]

Yiping Liu, Gary G. Yen, and Dunwei Gong. 2019. A multimodal multiobjective evolutionary algorithm using two-archive and recombination strategies. IEEE Transactions on Evolutionary Computation 23, 4 (2019), 660–674.

[108]

Zhichao Lu, Ian Whalen, Vishnu Boddeti, Yashesh Dhebar, Kalyanmoy Deb, Erik Goodman, and Wolfgang Banzhaf. NSGA-Net: Neural architecture search using multiobjective genetic algorithm. In Proceedings of the 2019 Annual Genetic and Evolutionary Computation Conference. ACM, New York, NY.

Digital Library

[109]

Thibaut Lust and Jacques Teghem. 2009. Two-phase Pareto local search for the biobjective traveling salesman problem. Journal of Heuristics 16 (2009), 475–510.

Digital Library

[110]

Xiaoliang Ma, Fang Liu, Yutao Qi, Xiaodong Wang, Lingling Li, Licheng Jiao, Minglei Yin, and Maoguo Gong. 2016. A multiobjective evolutionary algorithm based on decision variable analyses for multiobjective optimization problems with large-scale variables. IEEE Transactions on Evolutionary Computation 20, 2 (2016), 275–298.

Digital Library

[111]

Yannis Marinakis and Magdalene Marinaki. 2008. A particle swarm optimization algorithm with path relinking for the location routing problem. Journal of Mathematical Modelling and Algorithm 7 (2008), 59–78.

[112]

Harry Markowitz. 1952. Portfolio selection. Journal of Finance 7, 1 (1952), 77–91.

[113]

Iris Abril Martínez-Salazar, Julian Molina, Francisco Ángel Bello, Trinidad Gómez, and Rafael Caballero. 2014. Solving a bi-objective transportation location routing problem by metaheuristic algorithms. European Journal of Operational Research 234 (2014), 25–36.

[114]

Yosef Masoudi-Sobhanzadeh, Yadollah Omidi, Massoud Amanlou, and Ali Masoudi-Nejad. 2019. Trader as a new optimization algorithm predicts drug-target interactions efficiently. Scientific Reports 9 (2019), 9348.

[115]

Robert Miikkulainen, Cesare Alippi, Yoonsuck Choe, Francesco, and Carlo Morabito. 2019. Evolving deep neural networks. In Artificial Intelligence in the Age of Neural Networks and Brain Computing. Academic Press, 293–312.

[116]

Seyedali Mirjalili. 2015. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems 89 (2015), 228–249.

Digital Library

[117]

Seyedali Mirjalili. 2016. SCA: A sine cosine algorithm for solving optimization problems. Knowledge-Based Systems 96 (2016), 120–133.

Digital Library

[118]

Seyedali Mirjalili and Andrew Lewis. 2016. The whale optimization algorithm. Advances in Engineering Software 95 (2016), 51–67.

Digital Library

[119]

Azadeh Mohammadi and Mohamad Saraee. 2018. Finding influential users for different time bounds in social networks using multi-objective optimization. Swarm and Evolutionary Computation 40 (2018), 158–165.

[120]

Deyvid Heric Moraes, Danilo Sipoli Sanches, Josimar da Silva Rocha, Jader Maikol, Caldonazzo Garbelini, and Marcelo Favoretto Castoldi. 2019. A novel multi-objective evolutionary algorithm based on subpopulations for the bi-objective traveling salesman problem. Soft Computing 23 (2019), 6157–6168.

Digital Library

[121]

P. Larra Naga, C. M. H. Kuijpers, R. H. Murga, I. Inza, and S. Dizdarevic. 1999. Genetic algorithms for the travelling salesman problem: A review of representations and operators. Artificial Intelligence Review 13, 2 (1999), 129–170.

Digital Library

[122]

Antonio J. Nebro, Juan José Durillo, Jose Garcia-Nieto, C. A. Coello Coello, Francisco Luna, and Enrique Alba. 2009. SMPSO: A new PSO-based metaheuristic for multi-objective optimization. In Proceedings of the 2009 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making. IEEE, Los Alamitos, CA, 66–73.

[123]

N. Nekooghadirli, R. Tavakkoli-Moghaddam, V. R. Ghezavati and S. Javanmard. 2014. Solving a new bi-objective location-routing-inventory problem in a distribution network by meta-heuristics. Computers & Industrial Engineering 76 (2014), 204–221.

Digital Library

[124]

Bach Hoai Nguyen, Bing Xue, Peter Andreae, Hisao Ishibuchi, and Mengjie Zhang. 2020. Multiple reference points-based decomposition for multiobjective feature selection in classification: Static and dynamic mechanisms. IEEE Transactions on Evolutionary Computation 24, 1 (2020), 170–184.

Digital Library

[125]

Kyong Joo Oh, Tae Yoon Kim, and Sungky Min. 2005. Using genetic algorithm to support portfolio optimization for index fund management. Expert Systems with Applications 28 (2005), 371–379.

Digital Library

[126]

Mohammad Nabi Omidvar, Xiaodong Li, Yi Mei, and Xin Yao. 2014. Cooperative co-evolution with differential grouping for large scale optimization. IEEE Transactions on Evolutionary Computation 18, 3 (2014), 378–393.

[127]

Aytug Onan, Serdar Korukoǧlu, and Hasan Bulut. 2016. A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification. Expert Systems with Applications 62 (2016), 1–16.

Digital Library

[128]

Qingfu Zhang, Aimin Zhou, Shizheng Zhao, Ponnuthurai Nagaratnam Suganthan, Wudong Liu, and Santosh Tiwari. 2008. Multiobjective Optimization Test Instances for the CEC 2009 Special Session and Competition. Technical Report. University of Essex, Colchester, UK.

[129]

Andrea Paoli, Farid Melgani, and Edoardo Pasolli. 2009. Clustering of hyperspectral images based on multiobjective particle swarm optimization. IEEE Transactions on Geoscience and Remote Sensing 47, 12 (2009), 4175–4188.

[130]

Vivek K. Patel and Vimal J. Savsani. 2015. Heat transfer search (HTS): A novel optimization algorithm. Information Sciences 324 (2015), 217–246.

Digital Library

[131]

Romaric Pighetti, Denis Pallez, and Frédéric Precioso. Improving SVM training sample selection using multi-objective evolutionary algorithm and LSH. In Proceedings of the 2015 IEEE Symposium on Computational Intelligence and Data Mining. IEEE, Los Alamitos, CA.

[132]

Clara Pizzuti. 2012. A multiobjective genetic algorithm to find communities in complex networks. IEEE Transactions on Evolutionary Computation 16, 3 (2012), 418–430.

Digital Library

[133]

Clara Pizzuti and Annalisa Socievole. 2020. Multiobjective optimization and local merge for clustering attributed graphs. IEEE Transactions on Cybernetics 50, 12 (2020), 4997–5009.

[134]

Yutao Qi, Zhanting Hou, He Li, Jianbin Huang, and Xiaodong Li. 2015. A decomposition based memetic algorithm for multi-objective vehicle routing problem with time windows. Computers & Operations Research 62 (2015), 61–77.

Digital Library

[135]

Chao Qian, Yang Yu, and Zhihua Zhou. Pareto ensemble pruning. In Proceedings of the 29th AAAI Conference on Artificial Intelligence.

Digital Library

[136]

Hong Qian and Yang Yu. 2017. Solving high-dimensional multi-objective optimization problems with low effective dimensions. In Proceedings of the 31st AAAI Conference on Artificial Intelligence. 875–881.

Digital Library

[137]

Shufen Qin, Chaoli Sun, Yaochu Jin, Ying Tan, and Jonathan Fieldsend. 2021. Large-scale evolutionary multi-objective optimization assisted by directed sampling. IEEE Transactions on Evolutionary Computation. Early access, March 3, 2021.

[138]

Sumanta Ray and Ujjwal Maulik. 2017. Identifying differentially coexpressed module during HIV disease progression: A multiobjective approach. Scientific Reports 7 (2017), 86.

[139]

G. Thippa Reddy, M. Praveen Kumar Reddy, Kuruva Lakshmanna, Rajesh Kaluri, Dharmendra Singh Rajput, Gautam Srivastava, and Thar Baker. 2020. Analysis of dimensionality reduction techniques on big data. IEEE Access 8 (2020), 54776–54788.

[140]

Herbert Robbins and Sutton Monro. 1951. A stochastic approximation method. Annals of Mathematical Statistics 22, 3 (1951), 400–407.

[141]

Alejandro Rosales-Pérez, Salvador García, Jesus A. Gonzalez, Carlos A. Coello Coello, and Francisco Herrera. 2017. An evolutionary multiobjective model and instance selection for support vector machines with Pareto-based ensembles. IEEE Transactions on Evolutionary Computation 21, 6 (2017), 863–877.

Digital Library

[142]

Indrajit Saha, Ujjwal Maulik, and Dariusz Plewczynski. 2011. A new multi-objective technique for differential fuzzy clustering. Applied Soft Computing 11, 2 (2011), 2765–2776.

Digital Library

[143]

Karam M. Sallam, Saber M. Elsayed, Ripon K. Chakrabortty, and Michael J. Ryan. 2020. Improved multi-operator differential evolution algorithm for solving unconstrained problems. In Proceedings of the IEEE Congress on Evolutionary Computation.

[144]

Frederick Sander, Heiner Zille, and Sanaz Mostaghim. 2018. Transfer strategies from single- to multi-objective grouping mechanisms. In Proceedings of the 2018 Annual Genetic and Evolutionary Computation Conference. ACM, New York, NY, 729–736.

Digital Library

[145]

Wen Shi, Wei-Neng Chen, Ying Lin, Tianlong Gu, Sam Kwong, and Jun Zhang. 2019. An adaptive estimation of distribution algorithm for multipolicy insurance investment planning. IEEE Transactions on Evolutionary Computation 23, 1 (2019), 1–14.

[146]

J. Shoaf and J. A. Foster. The efficient set GA for stock portfolios. In Proceedings of the 1998 IEEE International Conference on Evolutionary Computation. IEEE, Los Alamitos, CA.

[147]

Sameer Singh, Jeremy Kubica, Scott Larsen, and Daria Sorokina. 2009. Parallel large scale feature selection for logistic regression. In Proceedings of the 2009 SIAM International Conference on Data Mining.

[148]

An Song, Qiang Yang, Wei-Neng Chen, and Jun Zhang. 2016. A random-based dynamic grouping strategy for large scale multi-objective optimization. In Proceedings of the 2016 IEEE Congress on Evolutionary Computation. IEEE, Los Alamitos, CA, 468–475.

Digital Library

[149]

Rainer Storn and Kenneth Price. 1997. Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11, 4 (1997), 341–359.

Digital Library

[150]

Felix Streichert, Holger Ulmer, and Andreas Zell. Comparing discrete and continuous genotypes on the constrained portfolio selection problem. In Proceedings of the 2004 Genetic and Evolutionary Computation Conference.

[151]

Jiawei Su, Danilo Vasconcellos Vargas, and Kouichi Sakurai. 2019. One pixel attack for fooling deep neural networks. IEEE Transactions on Evolutionary Computation 23, 5 (2019), 828–841.

[152]

Marcin Suchorzewski and Jeff Clune. 2011. A novel generative encoding for evolving modular, regular and scalable networks. In Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation. ACM, New York, NY.

Digital Library

[153]

Masanori Suganuma, Mete Ozay, and Takayuki Okatani. 2018. Exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search. Proceedings of Machine Learning Research 80 (2018), 4771–4780.

[154]

Yanan Sun, Bing Xue, Mengjie Zhang, and Gary G. Yen. 2018. A new two-stage evolutionary algorithm for many-objective optimization. IEEE Transactions on Evolutionary Computation 23, 5 (2018), 748–761.

[155]

Yanan Sun, Gary G. Yen, and Zhang Yi. 2019. Evolving unsupervised deep neural networks for learning meaningful representations. IEEE Transactions on Evolutionary Computation 23, 1 (2019), 89–103.

[156]

Takahiro Suzuki, Shingo Takeshita, and Satoshi Ono. Adversarial example generation using evolutionary multi-objective optimization. In Proceedings of the 2019 IEEE Congress on Evolutionary Computation. IEEE, Los Alamitos, CA.

[157]

K. C. Tan, Y. H. Chew, and L. H. Lee. 2006. A hybrid multi-objective evolutionary algorithm for solving truck and trailer vehicle routing problems. European Journal of Operational Research 172, 3 (2006), 855–885.

Digital Library

[158]

Ryoji Tanabe and Alex Fukunaga. 2013. Success-history based parameter adaptation for differential evolution. In Proceedings of the 2013 IEEE Congress on Evolutionary Computation. IEEE, Los Alamitos, CA.

[159]

Ryoji Tanabe and Hisao Ishibuchi. 2020. A review of evolutionary multi-modal multi-objective optimization. IEEE Transactions on Evolutionary Computation 24, 1 (2020), 193–200.

Digital Library

[160]

Linpeng Tang, Lei Zhang, Ping Luo, and Min Wang. Incorporating occupancy into frequent pattern mining for high quality pattern recommendation. In Proceedings of the 21th ACM International Conference on Information and Knowledge Management. ACM, New York, NY.

Digital Library

[161]

Ye Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin. 2017. PlatEMO: A MATLAB platform for evolutionary multi-objective optimization [educational forum]. IEEE Computational Intelligence Magazine 12, 4 (2017), 73–87.

Digital Library

[162]

Ye Tian, Cheng He, Ran Cheng, and Xingyi Zhang. 2019. A multistage evolutionary algorithm for better diversity preservation in multiobjective optimization. IEEE Transactions on Systems, Man, and Cybernetics: Systems (2019).

[163]

Ye Tian, Ruchen Liu, Xingyi Zhang, Haiping Ma, Kay Chen Tan, and Yaochu Jin. 2021. A multi-population evolutionary algorithm for solving large-scale multi-modal multi-objective optimization problems. IEEE Transactions on Evolutionary Computation 25, 3 (2021), 405–418.

[164]

Ye Tian, Chang Lu, Xingyi Zhang, Fan Cheng, and Yaochu Jin. 2020. A pattern mining based evolutionary algorithm for large-scale sparse multi-objective optimization problems. IEEE Transactions on Cybernetics. Early access, December 30, 2020.

[165]

Ye Tian, Chang Lu, Xingyi Zhang, Kay Chen Tan, and Yaochu Jin. 2021. Solving large-scale multiobjective optimization problems with sparse optimal solutions via unsupervised neural networks. IEEE Transactions on Cybernetics 51, 6 (2021), 3115–3128.

[166]

Ye Tian, Xiaochun Su, Yansen Su, and Xingyi Zhang. 2020. EMODMI: A multi-objective optimization based method to identify disease modules. IEEE Transactions on Emerging Topics in Computational Intelligence. Early access, August 21, 2020.

[167]

Ye Tian, Shangshang Yang, Lei Zhang, Fuchen Duan, and Xingyi Zhang. 2019. A surrogate-assisted multiobjective evolutionary algorithm for large-scale task-oriented pattern mining. IEEE Transactions on Emerging Topics in Computational Intelligence 3, 2 (2019), 106–116.

[168]

Ye Tian, Shangshang Yang, and Xingyi Zhang. 2020. An evolutionary multiobjective optimization based fuzzy method for overlapping community detection. IEEE Transactions on Fuzzy Systems 28, 11 (2020), 2841–2855.

[169]

Ye Tian, Shangshang Yang, Xingyi Zhang, and Yaochu Jin. Using PlatEMO to solve multi-objective optimization problems. In Proceedings of the 2019 IEEE Congress on Evolutionary Computation. IEEE, Los Alamitos, CA.

[170]

Ye Tian, Tao Zhang, Jianhua Xiao, Xingyi Zhang, and Yaochu Jin. 2021. A coevolutionary framework for constrained multi-objective optimization problems. IEEE Transactions on Evolutionary Computation 25, 1 (2021), 102–116.

[171]

Ye Tian, Xingyi Zhang, Chao Wang, and Yaochu Jin. 2020. An evolutionary algorithm for large-scale sparse multi-objective optimization problems. IEEE Transactions on Evolutionary Computation 24, 2 (2020), 380–393.

[172]

Ye Tian, Xiutao Zheng, Xingyi Zhang, and Yaochu Jin. 2020. Efficient large-scale multiobjective optimization based on a competitive swarm optimizer. IEEE Transactions on Cybernetics 50, 8 (2020), 3696–3708.

[173]

Mario Ventresca, Kyle Robert Harrison, and Beatrice M. Ombuki-Berman. An experimental evaluation of multi-objective evolutionary algorithms for detecting critical nodes in complex networks. In Proceedings of the 2015 European Conference on the Applications of Evolutionary Computation.

[174]

Nele Verbiest, Joaquín Derrac, Chris Cornelis, Salvador García, and Francisco Herrera. 2016. Evolutionary wrapper approaches for training set selection as preprocessing mechanism for support vector machines: Experimental evaluation and support vector analysis. Applied Soft Computing 38 (2016), 10–22.

Digital Library

[175]

Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th International Conference on Machine Learning. ACM, New York, NY, 1096–1103.

Digital Library

[176]

Bin Wang, Yanan Sun, Bing Xue, and Mengjie Zhang. Evolving deep neural networks by multi-objective particle swarm optimization for image classification. In Proceedings of the 2019 Annual Genetic and Evolutionary Computation Conference. ACM, New York, NY.

Digital Library

[177]

Jiahai Wang, Wenbin Ren, Zizhen Zhang, Han Huang, and Yuren Zhou. 2020. A hybrid multiobjective memetic algorithm for multiobjective periodic vehicle routing problem with time windows. IEEE Transactions on Systems, Man, and Cybernetics: Systems 50, 11 (2020), 4732–4745.

[178]

Jia Wang, Yuchao Su, Qiuzhen Lin, Lijia Ma, Dunwei Gong, Jianqiang Li, and Zhong Ming. 2020. A survey of decomposition approaches in multiobjective evolutionary algorithms. Neurocomputing 408 (2020), 308–330.

[179]

Jiahai Wang, Taiyao Weng, and Qingfu Zhang. 2019. A two-stage multiobjective evolutionary algorithm for multiobjective multidepot vehicle routing problem with time windows. IEEE Transactions on Cybernetics 49, 7 (2019), 2467–2478.

[180]

Jiahai Wang, Ying Zhou, Yong Wang, Jun Zhang, C. L. Philip Chen, and Zibin Zheng. 2016. Multiobjective vehicle routing problems with simultaneous delivery and pickup and time windows: Formulation, instances, and algorithms. IEEE Transactions on Cybernetics 46, 3 (2016), 582–594.

[181]

Shuai Wang, Jing Liu, and Yaochu Jin. 2020. Surrogate-assisted robust optimization of large-scale networks based on graph embedding. IEEE Transactions on Evolutionary Computation 24, 4 (2020), 735–749.

[182]

Yong Wang, Kevin Assogba, Yong Liu, Xiaolei Ma, Maozeng Xu, and Yinhai Wang. 2018. Two-echelon location-routing optimization with time windows based on customer clustering. Expert Systems with Applications 104 (2018), 244–260.

[183]

Lyndon While, Philip Hingston, Luigi Barone, and Simon Huband. 2006. A faster algorithm for calculating hypervolume. IEEE Transactions on Evolutionary Computation 10, 1 (2006), 29–38.

Digital Library

[184]

Siripen Wikaisuksakul. 2014. A multi-objective genetic algorithm with fuzzy c-means for automatic data clustering. Applied Soft Computing 24 (2014), 679–691.

Digital Library

[185]

Yu Wu, Yongshan Zhang, Xiaobo Liu, Zhihua Cai, and Yaoming Cai. 2018. A multiobjective optimization-based sparse extreme learning machine algorithm. Neurocomputing 317 (2018), 88–100.

[186]

Xiaoshu Xiang, Ye Tian, Jianhua Xiao, and Xingyi Zhang. 2020. A clustering-based surrogate-assisted multi-objective evolutionary algorithm for shelter location under uncertainty of road networks. IEEE Transactions on Industrial Informatics 16, 12 (2020), 7544–7555.

[187]

Yi Xiang, Yuren Zhou, Zibin Zheng, and Miqing Li. 2018. Configuring software product lines by combining many-objective optimization and SAT solvers. ACM Transactions on Software Engineering and Methodology 26, 4 (2018), 14.

Digital Library

[188]

Jian Xiong, Chao Zhang, Gang Kou, Rui Wang, Hisao Ishibuchi, and Fawaz E. Alsaadi. 2020. Optimizing long-term bank financial products portfolio problems with a multiobjective evolutionary approach. Complexity 2020 (2020), 3106097.

Digital Library

[189]

Hang Xu, Bin Xue, and Mengjie Zhang. 2021. A duplication analysis based evolutionary algorithm for bi-objective feature selection. IEEE Transactions on Evolutionary Computation 25, 2 (2021), 205–218.

[190]

Bing Xue, Mengjie Zhang, and Will N. Browne. 2013. Particle swarm optimization for feature selection in classification: A multi-objective approach. IEEE Transactions on Cybernetics 43, 6 (2013), 1656–1671.

[191]

Cheng-Hong Yang, Li-Yeh Chuang, and Yu-Da Lin. 2017. Multiobjective differential evolution-based multifactor dimensionality reduction for detecting genegene interactions. Scientific Reports 7 (2017), 12869.

[192]

Peng Yang, Ke Tang, and Xin Yao. 2018. Turning high-dimensional optimization into computationally expensive optimization. IEEE Transactions on Evolutionary Computation 22, 1 (2018), 143–156.

[193]

Shengxiang Yang, Miqing Li, Xiaohui Liu, and Jinhua Zheng. 2013. A grid-based evolutionary algorithm for many-objective optimization. IEEE Transactions on Evolutionary Computation 17, 5 (2013), 721–736.

Digital Library

[194]

Shangshang Yang, Ye Tian, Cheng He, Xingyi Zhang, Kay Chen Tan, and Yaochu Jin. 2021. Gradient guided evolutionary approach to training deep neural networks. IEEE Transactions on Neural Networks and Learning Systems. Early access, March 4, 2021.

[195]

Zhenyu Yang, Ke Tang, and Xin Yao. 2008. Large scale evolutionary optimization using cooperative coevolution. Information Sciences 178, 15 (2008), 2985–2999.

Digital Library

[196]

Zhaohui Yang, Yunhe Wang, Xinghao Chen, Boxin Shi, Chao Xu, Chunjing Xu, Qi Tian, and Chang Xu. CARS: Continuous evolution for efficient neural architecture search. In Proceedings of the 2020 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, Los Alamitos, CA.

[197]

Xin Yao. 1993. A review of evolutionary artificial neural networks. International Journal of Intelligent Systems 8, 4 (1993), 539–567.

[198]

Xin Yao. 1999. Evolving artificial neural networks. Proceedings of the IEEE 87, 9 (1999), 1423–1447.

[199]

Jiao-Hong Yi, Li-Ning Xing, Gai-Ge Wang, Junyu Dong, Athanasios V. Vasilakos, Amir H. Alavi, and Ling Wang. 2020. Behavior of crossover operators in NSGA-III for large-scale optimization problems. Information Sciences 509 (2020), 470–487.

Digital Library

[200]

Caitong Yue, Boyang Qu, and Jing Liang. 2018. A multiobjective particle swarm optimizer using ring topology for solving multimodal multiobjective problems. IEEE Transactions on Evolutionary Computation 22, 5 (2018), 805–817.

[201]

C. T. Yue, J. J. Liang, B. Y. Qu, K. J. Yu, and H. Song. 2019. Multimodal multiobjective optimization in feature selection. In Proceedings of the 2019 IEEE Congress on Evolutionary Computation. IEEE, Los Alamitos, CA.

[202]

Saúl Zapotecas-Martínez, Carlos A. Coello Coello, Hernán E. Aguirre, and Kiyoshi Tanaka. 2019. A review of features and limitations of existing scalable multiobjective test suites. IEEE Transactions on Evolutionary Computation 23, 1 (2019), 130–142.

[203]

Chunkai Zhang, Yepeng Deng, Xin Guo, Xuan Wang, and Chuanyi Liu. An adversarial attack based on multi-objective optimization in the black-box scenario: MOEA-APGA II. In Proceedings of the 2019 International Conference on Information and Communications Security.

[204]

Lei Zhang, Guanglong Fu, Fan Cheng, Jianfeng Qiu, and Yansen Su. 2018. A multi-objective evolutionary approach for mining frequent and high utility itemsets. Applied Soft Computing 62 (2018), 974–986.

[205]

Lei Zhang, Hebin Pan, Yansen Su, and Xingyi Zhang. 2014. A mixed representation based multi-objective evolutionary algorithm for overlapping community detection. IEEE Transactions on Cybernetics 47, 9 (2014), 2703–2716.

[206]

Lei Zhang, Fengjiao Sun, Fan Cheng, Haiping Ma, and Xiaoyan Sun. An overlapping community detection based multi-objective evolutionary algorithm for diversified social influence maximization. In Proceedings of the 2020 IEEE Congress on Evolutionary Computation. IEEE, Los Alamitos, CA.

[207]

Lei Zhang, Xinpeng Wu, Hongke Zhao, Fan Chen, and Qi Liu. 2020. Personalized recommendation in P2P lending based on risk-return management: A multi-objective perspective. IEEE Transactions on Big Data. Early access, May 8, 2020.

[208]

Lei Zhang, Jiajun Xia, Fan Cheng, Jianfeng Qiu, and Xingyi Zhang. 2020. Multi-objective optimization of critical node detection based on cascade model in complex networks. IEEE Transactions on Network Science and Engineering 7, 3 (2020), 2052–2066.

[209]

Lei Zhang, Shangshang Yang, Xinpeng Wu, Fan Cheng, Ying Xie, and Zhiting Lin. 2019. An indexed set representation based multi-objective evolutionary approach for mining diversified top-k high utility patterns. Engineering Applications of Artificial Intelligence 77 (2019), 9–20.

[210]

Qingfu Zhang and Hui Li. 2007. MOEA/D: A multi-objective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation 11, 6 (2007), 712–731.

Digital Library

[211]

Qingfu Zhang, Aimin Zhou, and Yaochu Jin. 2008. RM-MEDA: A regularity model-based multiobjective estimation of distribution algorithm. IEEE Transactions on Evolutionary Computation 12, 1 (2008), 41–63.

Digital Library

[212]

Xingyi Zhang, Fuchen Duan, Lei Zhang, Fan Cheng, Yaochu Jin, and Ke Tang. 2017. Pattern recommendation in task-oriented applications: A multi-objective perspective [application notes]. IEEE Computational Intelligence Magazine 12, 3 (2017), 43–53.

Digital Library

[213]

Xingyi Zhang, Ye Tian, Ran Cheng, and Yaochu Jin. 2018. A decision variable clustering-based evolutionary algorithm for large-scale many-objective optimization. IEEE Transactions on Evolutionary Computation 22, 1 (2018), 97–112.

[214]

Xingyi Zhang, Kefei Zhou, Hebin Pan, Lei Zhang, Xiangxiang Zeng, and Yaochu Jin. 2020. A network reduction-based multiobjective evolutionary algorithm for community detection in large-scale complex networks. IEEE Transactions on Cybernetics 50, 2 (2020), 703–716.

[215]

Yin Zhang, Gai-Ge Wang, Keqin Li, Wei-Chang Yeh, Muwei Jian, and Junyu Dong. 2020. Enhancing MOEA/D with information feedback models for large-scale many-objective optimization. Information Sciences 522 (2020), 1–16.

[216]

Hongke Zhao, Yong Ge, Qi Liu, Guifeng Wang, Enhong Chen, and Hefu Zhang. 2017. P2P lending survey: Platforms, recent advances and prospects. ACM Transactions on Intelligent Systems and Technology 8, 6 (2017), 1–28.

Digital Library

[217]

Hongke Zhao, Qi Liu, Guifeng Wang, Yong Ge, and Enhong Chen. Portfolio selections in P2P lending: A multi-objective perspective. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY.

Digital Library

[218]

Mengjie Zhao, Kai Zhang, Guodong Chen, Xinggang Zhao, Chuanjin Yao, Hai Sun, Zhaoqin Huang, and Jun Yao. 2020. A surrogate-assisted multi-objective evolutionary algorithm with dimension-reduction for production optimization. Journal of Petroleum Science and Engineering 192 (2020), 107192.

[219]

Aimin Zhou, Bo-Yang Qu, Hui Li, Shi-Zheng Zhao, Ponnuthurai Nagaratnam Suganthan, and Qingfu Zhang. 2011. Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and Evolutionary Computation 1 (2011), 32–49.

[220]

Heiner Zille. 2019. Large-Scale Multi-Objective Optimisation: New Approaches and a Classification of the State-of-the-Art. Ph.D. Dissertation. Otto-von-Guericke-Universität Magdeburg, Fakultät für Informatik.

[221]

Heiner Zille, Hisao Ishibuchi, Sanaz Mostaghim, and Yusuke Nojima. 2016. Mutation operators based on variable grouping for multi-objective large-scale optimization. In Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence. IEEE, Los Alamitos, CA.

[222]

Heiner Zille, Hisao Ishibuchi, Sanaz Mostaghim, and Yusuke Nojima. 2016. Weighted optimization framework for large-scale multi-objective optimization. In Proceedings of the 2016 Annual Genetic and Evolutionary Computation Conference Companion. ACM, New York, NY, 83–84.

Digital Library

[223]

Heiner Zille, Hisao Ishibuchi, Sanaz Mostaghim, and Yusuke Nojima. 2018. A framework for large-scale multiobjective optimization based on problem transformation. IEEE Transactions on Evolutionary Computation 22, 2 (2018), 260–275.

[224]

Heiner Zille and Sanaz Mostaghim. Comparison study of large-scale optimisation techniques on the LSMOP benchmark functions. In Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence.

[225]

Heiner Zille and Sanaz Mostaghim. 2019. Linear search mechanism for multi- and many-objective optimisation. In Proceedings of the 2019 International Conference on Evolutionary Multi-Criterion Optimization. 399–410.

[226]

Eckart Zitzler, Kalyanmoy Deb, and Lothar Thiele. 2000. Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation 8, 2 (2000), 173–195.

Digital Library

Cited By

Liu TWu YYe ACao LCao Y(2024)Two-stage sparse multi-objective evolutionary algorithm for channel selection optimization in BCIsFrontiers in Human Neuroscience10.3389/fnhum.2024.140007718Online publication date: 22-May-2024
https://doi.org/10.3389/fnhum.2024.1400077
Garg KShekhar S(2024)Fault sensitivity index-based multi-objective testcase prioritizationJournal of Electrical Engineering10.2478/jee-2024-001875:2(151-160)Online publication date: 4-Apr-2024
https://doi.org/10.2478/jee-2024-0018
Liu XFeng YDing NLi RChen X(2024)Multi-criteria Scheduling in Parallel Environment with Learning EffectFoundations of Computing and Decision Sciences10.2478/fcds-2024-000149:1(3-20)Online publication date: 16-Feb-2024
https://doi.org/10.2478/fcds-2024-0001
Show More Cited By

Index Terms

Evolutionary Large-Scale Multi-Objective Optimization: A Survey

Recommendations

A cooperative co-evolutionary algorithm for large-scale multi-objective optimization problems
GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion

A wide range of real-world problems are multi-objective optimization problems (MOPs). Multi-objective evolutionary algorithms (MOEAs) have been proposed to solve MOPs, but the search process deteriorates with the increase of MOPs' dimension of decision ...
An interactive evolutionary multi-objective optimization and decision making procedure

With the advent of efficient techniques for multi-objective evolutionary optimization (EMO), real-world search and optimization problems are being increasingly solved for multiple conflicting objectives. During the past decade of research and ...
Evolutionary many-objective optimization based on linear assignment problem transformations

The selection mechanisms that are most commonly adopted by multi-objective evolutionary algorithms (MOEAs) are based on Pareto optimality. However, recent studies have provided theoretical and experimental evidence regarding the unsuitability of Pareto-...

Comments

Information & Contributors

Information

Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 54, Issue 8

November 2022

754 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3481697

Editor:
Albert Zomaya
University of Sydney, Australia

Issue’s Table of Contents

Copyright © 2021 Association for Computing Machinery.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 October 2021

Accepted: 01 June 2021

Revised: 01 May 2021

Received: 01 August 2020

Published in CSUR Volume 54, Issue 8

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Survey
Refereed

Funding Sources

National Key R&D Program of China
National Natural Science Foundation of China
Hong Kong Scholars Program
Anhui Provincial Natural Science Foundation
Collaborative Innovation Program of Anhui
Research Grants Council of the Hong Kong Special Administrative Region
Royal Society International Exchanges Program

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

101
Total Citations
View Citations
2,882
Total Downloads

Downloads (Last 12 months)1,108
Downloads (Last 6 weeks)96

Reflects downloads up to 09 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Liu TWu YYe ACao LCao Y(2024)Two-stage sparse multi-objective evolutionary algorithm for channel selection optimization in BCIsFrontiers in Human Neuroscience10.3389/fnhum.2024.140007718Online publication date: 22-May-2024
https://doi.org/10.3389/fnhum.2024.1400077
Garg KShekhar S(2024)Fault sensitivity index-based multi-objective testcase prioritizationJournal of Electrical Engineering10.2478/jee-2024-001875:2(151-160)Online publication date: 4-Apr-2024
https://doi.org/10.2478/jee-2024-0018
Liu XFeng YDing NLi RChen X(2024)Multi-criteria Scheduling in Parallel Environment with Learning EffectFoundations of Computing and Decision Sciences10.2478/fcds-2024-000149:1(3-20)Online publication date: 16-Feb-2024
https://doi.org/10.2478/fcds-2024-0001
Kong JLi JLi P(2024)Optimization of chaotic light output in semiconductor laser systems based on multi-objective optimization algorithmPLOS ONE10.1371/journal.pone.030163019:4(e0301630)Online publication date: 11-Apr-2024
https://doi.org/10.1371/journal.pone.0301630
Dong PChen WWang KZhou KWang W(2024)Research on Combat Mission Configuration of Unmanned Aerial Vehicle Maritime Reconnaissance Based on Particle Swarm Optimization AlgorithmComplexity10.1155/2024/91437742024Online publication date: 31-Mar-2024
https://dl.acm.org/doi/10.1155/2024/9143774
Poulakis YDoulkeridis CKyriazis D(2024)A Survey on AutoML Methods and Systems for ClusteringACM Transactions on Knowledge Discovery from Data10.1145/364356418:5(1-30)Online publication date: 26-Jan-2024
https://dl.acm.org/doi/10.1145/3643564
Liang ZJiang TSun KCheng RLi XHandl J(2024)GPU-accelerated Evolutionary Multiobjective Optimization Using Tensorized RVEAProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654223(566-575)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654223
Gong CNan YPang LIshibuchi HZhang QLi XHandl J(2024)Heuristic Initialization and Knowledge-based Mutation for Large-Scale Multi-Objective 0-1 Knapsack ProblemsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654006(178-186)Online publication date: 14-Jul-2024
https://doi.org/10.1145/3638529.3654006
Wang ZHuang CLi YYao X(2024)Multi-objective Feature Attribution Explanation For Explainable Machine LearningACM Transactions on Evolutionary Learning and Optimization10.1145/36173804:1(1-32)Online publication date: 23-Feb-2024
https://doi.org/10.1145/3617380
Keller PNavet N(2024)Approximation of Worst-Case Traversal Times in Real-Time Ethernet Networks: Exploring the Potential of Many-Objective Optimization for Simulation Aggregation2024 IEEE 20th International Conference on Factory Communication Systems (WFCS)10.1109/WFCS60972.2024.10540870(1-8)Online publication date: 17-Apr-2024
https://doi.org/10.1109/WFCS60972.2024.10540870
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Issue’s Table of Contents