Cited By
View all- Alp SDehkharghani RAkan TBhuiyan M(2024)MOBRO: multi-objective battle royale optimizerThe Journal of Supercomputing10.1007/s11227-023-05676-480:5(5979-6016)Online publication date: 1-Mar-2024
A practical regularity model based evolutionary algorithm for multiobjective optimization
References
[1]
Miettinen Kaisa, Nonlinear Multiobjective Optimization, Vol. 12, Springer Science & Business Media, 2012.
[2]
Deb Kalyanmoy, Multi-Objective Optimization using Evolutionary Algorithms, Vol. 16, John Wiley & Sons, 2001.
[3]
Zhou Aimin, Qu Bo-Yang, Li Hui, Zhao Shi-Zheng, Suganthan Ponnuthurai Nagaratnam, Zhang Qingfu, Multiobjective evolutionary algorithms: A survey of the state of the art, Swarm Evol. Comput. 1 (1) (2011) 32–49.
[4]
Zhang Qingfu, Zhou Aimin, Jin Yaochu, RM-MEDA: A regularity model-based multiobjective estimation of distribution algorithm, IEEE Trans. Evol. Comput. 12 (1) (2008) 41–63.
[5]
Li Hui, Zhang Qingfu, Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II, IEEE Trans. Evol. Comput. 13 (2) (2008) 284–302.
[6]
Sun Jianyong, Zhang Hu, Zhou Aimin, Zhang Qingfu, Zhang Ke, A new learning-based adaptive multi-objective evolutionary algorithm, Swarm Evol. Comput. 44 (2019) 304–319.
[7]
Deb Kalyanmoy, Pratap Amrit, Agarwal Sameer, Meyarivan TAMT, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput. 6 (2) (2002) 182–197.
[8]
Zitzler Eckart, Laumanns Marco, Thiele Lothar, SPEA2: Improving the strength Pareto evolutionary algorithm, TIK-Rep. 103 (2001).
[9]
Zou Juan, Yang Qite, Yang Shengxiang, Zheng Jinhua, Ra-dominance: A new dominance relationship for preference-based evolutionary multiobjective optimization, Appl. Soft Comput. 90 (2020).
[10]
Zitzler Eckart, Künzli Simon, Indicator-based selection in multiobjective search, in: International Conference on Parallel Problem Solving from Nature, Springer, 2004, pp. 832–842.
[11]
Beume Nicola, Naujoks Boris, Emmerich Michael, SMS-EMOA: Multiobjective selection based on dominated hypervolume, European J. Oper. Res. 181 (3) (2007) 1653–1669.
[12]
Pamulapati Trinadh, Mallipeddi Rammohan, Suganthan Ponnuthurai Nagaratnam, IS D E +—An indicator for multi and many-objective optimization, IEEE Trans. Evol. Comput. 23 (2) (2018) 346–352.
[13]
Zhang Qingfu, Li Hui, MOEA/D: A multiobjective evolutionary algorithm based on decomposition, IEEE Trans. Evol. Comput. 11 (6) (2007) 712–731.
[14]
Wu Mengyuan, Li Ke, Kwong Sam, Zhang Qingfu, Zhang Jun, Learning to decompose: A paradigm for decomposition-based multiobjective optimization, IEEE Trans. Evol. Comput. 23 (3) (2018) 376–390.
[15]
Cao Leilei, Xu Lihong, Goodman Erik D., Li Hui, Decomposition-based evolutionary dynamic multiobjective optimization using a difference model, Appl. Soft Comput. 76 (2019) 473–490.
[16]
Storn Rainer, Price Kenneth, Differential evolution–A simple and efficient heuristic for global optimization over continuous spaces, J. Global Optim. 11 (4) (1997) 341–359.
[17]
Das Swagatam, Suganthan Ponnuthurai Nagaratnam, Differential evolution: A survey of the state-of-the-art, IEEE Trans. Evol. Comput. 15 (1) (2010) 4–31.
[18]
Das Swagatam, Mullick Sankha Subhra, Suganthan Ponnuthurai N, Recent advances in differential evolution–an updated survey, Swarm Evol. Comput. 27 (2016) 1–30.
[19]
Wu Jianye, Gong Wenyin, Wang Ling, A clustering-based differential evolution with different crowding factors for nonlinear equations system, Appl. Soft Comput. 98 (2021).
[20]
Kennedy James, Eberhart Russell, Particle swarm optimization, in: Proceedings of ICNN’95-International Conference on Neural Networks, Vol. 4, IEEE, 1995, pp. 1942–1948.
[21]
Liang Jing J, Qin A Kai, Suganthan Ponnuthurai N, Baskar S, Comprehensive learning particle swarm optimizer for global optimization of multimodal functions, IEEE Trans. Evol. Comput. 10 (3) (2006) 281–295.
[22]
Kohler Manoela, Vellasco Marley M.B.R., Tanscheit Ricardo, PSO+: A new particle swarm optimization algorithm for constrained problems, Appl. Soft Comput. 85 (2019).
[23]
Larrañaga Pedro, Lozano Jose A., Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation, Vol. 2, Springer Science & Business Media, 2001.
[24]
Mühlenbein Heinz, Bendisch Jürgen, Voigt H.-M., From recombination of genes to the estimation of distributions II. Continuous parameters, in: International Conference on Parallel Problem Solving from Nature, Springer, 1996, pp. 188–197.
[25]
Wolpert David H., Macready William G., No free lunch theorems for optimization, IEEE Trans. Evol. Comput. 1 (1) (1997) 67–82.
[26]
Zhang Hu, Zhou Aimin, Song Shenmin, Zhang Qingfu, Gao Xiao-Zhi, Zhang Jun, A self-organizing multiobjective evolutionary algorithm, IEEE Trans. Evol. Comput. 20 (5) (2016) 792–806.
[27]
Sun Jianyong, Zhang Hu, Zhou Aimin, Zhang Qingfu, Zhang Ke, Tu Zhenbiao, Ye Kai, Learning from a stream of nonstationary and dependent data in multiobjective evolutionary optimization, IEEE Trans. Evol. Comput. 23 (4) (2018) 541–555.
[28]
Hillermeier Claus, et al., Nonlinear Multiobjective Optimization: A Generalized Homotopy Approach, Vol. 135, Springer Science & Business Media, 2001.
[29]
Kambhatla Nandakishore, Leen Todd K., Dimension reduction by local principal component analysis, Neural Comput. 9 (7) (1997) 1493–1516.
[30]
Wang Yong, Xiang Jian, Cai Zixing, A regularity model-based multiobjective estimation of distribution algorithm with reducing redundant cluster operator, Appl. Soft Comput. 12 (11) (2012) 3526–3538.
[31]
Wang Handing, Zhang Qingfu, Jiao Licheng, Yao Xin, Regularity model for noisy multiobjective optimization, IEEE Trans. Cybern. 46 (9) (2015) 1997–2009.
[32]
Sun Yanan, Yen Gary G., Yi Zhang, Improved regularity model-based EDA for many-objective optimization, IEEE Trans. Evol. Comput. 22 (5) (2018) 662–678.
[33]
Zhou Aimin, Zhang Qingfu, Jin Yaochu, Approximating the set of Pareto-optimal solutions in both the decision and objective spaces by an estimation of distribution algorithm, IEEE Trans. Evol. Comput. 13 (5) (2009) 1167–1189.
[34]
Zhou Aimin, Jin Yaochu, Zhang Qingfu, A population prediction strategy for evolutionary dynamic multiobjective optimization, IEEE Trans. Cybern. 44 (1) (2013) 40–53.
[35]
Li Ke, Kwong Sam, A general framework for evolutionary multiobjective optimization via manifold learning, Neurocomputing 146 (2014) 65–74.
[36]
Yuan Qiong, Dai Guangming, Zhang Yuzhen, A novel multi-objective evolutionary algorithm based on LLE manifold learning, Eng. Comput. 33 (2) (2017) 293–305.
[37]
Dong Bing, Zhou Aimin, Zhang Guixu, Sampling in latent space for a mulitiobjective estimation of distribution algorithm, in: 2016 IEEE Congress on Evolutionary Computation, CEC, IEEE, 2016, pp. 3027–3034.
[38]
Wang Rui, Dong Nan-Jiang, Gong Dun-Wei, Zhou Zhong-Bao, Cheng Shi, Wu Guo-Hua, Wang Ling, PCA-assisted reproduction for continuous multi-objective optimization with complicated Pareto optimal set, Swarm Evol. Comput. 60 (2021).
[39]
Pan Linqiang, Li Lianghao, Cheng Ran, He Cheng, Tan Kay Chen, Manifold learning-inspired mating restriction for evolutionary multiobjective optimization with complicated Pareto sets, IEEE Trans. Cybern. (2019).
[40]
Li Xin, Zhang Hu, Song Shenmin, MOEA/D with the online agglomerative clustering based self-adaptive mating restriction strategy, Neurocomputing 339 (2019) 77–93.
[41]
Zhang Hu, Zhang Xiujie, Gao Xiao-Zhi, Song Shenmin, Self-organizing multiobjective optimization based on decomposition with neighborhood ensemble, Neurocomputing 173 (2016) 1868–1884.
[42]
Fang Hui, Zhou Aimin, Zhang Hu, Information fusion in offspring generation: A case study in DE and EDA, Swarm Evol. Comput. 42 (2018) 99–108.
[43]
Gu Fangqing, Liu Hai-Lin, Tan Kay Chen, A multiobjective evolutionary algorithm using dynamic weight design method, Int. J. Innovative Comput. Inf. Control 8 (5 (B)) (2012) 3677–3688.
[44]
Li Hui, Zhang Qingfu, Deng Jingda, Biased multiobjective optimization and decomposition algorithm, IEEE Trans. Cybern. 47 (1) (2016) 52–66.
[45]
Von Luxburg Ulrike, A tutorial on spectral clustering, Stat. Comput. 17 (4) (2007) 395–416.
[46]
Wang Shuai, Zhang Hu, Zhang Yi, Zhou Aimin, Adaptive population structure learning in evolutionary multi-objective optimization, Soft Comput. (2019) 1–18.
[47]
Jain Anil K., Data clustering: 50 years beyond K-means, Pattern Recognit. Lett. 31 (8) (2010) 651–666.
[48]
Ng Andrew Y., Jordan Michael I., Weiss Yair, On spectral clustering: Analysis and an algorithm, in: Advances in Neural Information Processing Systems, 2002, pp. 849–856.
[49]
Cheng Ran, Jin Yaochu, Narukawa Kaname, Sendhoff Bernhard, A multiobjective evolutionary algorithm using Gaussian process-based inverse modeling, IEEE Trans. Evol. Comput. 19 (6) (2015) 838–856.
[50]
Hansen Nikolaus, Müller Sibylle D., Koumoutsakos Petros, Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES), Evol. Comput. 11 (1) (2003) 1–18.
[51]
Ishibuchi Hisao, Masuda Hiroyuki, Tanigaki Yuki, Nojima Yusuke, Modified distance calculation in generational distance and inverted generational distance, in: International Conference on Evolutionary Multi-Criterion Optimization, Springer, 2015, pp. 110–125.
[52]
Zitzler Eckart, Thiele Lothar, Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach, IEEE Trans. Evol. Comput. 3 (4) (1999) 257–271.
[53]
Tian Ye, Cheng Ran, Zhang Xingyi, Jin Yaochu, PlatEMO: A MATLAB platform for evolutionary multi-objective optimization [educational forum], IEEE Comput. Intell. Mag. 12 (4) (2017) 73–87.
[54]
Huband Simon, Barone Luigi, While Lyndon, Hingston Phil, A scalable multi-objective test problem toolkit, in: International Conference on Evolutionary Multi-Criterion Optimization, Springer, 2005, pp. 280–295.
[55]
Friedman Milton, The use of ranks to avoid the assumption of normality implicit in the analysis of variance, J. Amer. Statist. Assoc. 32 (200) (1937) 675–701.
[56]
Dunn Olive Jean, Multiple comparisons among means, J. Amer. Statist. Assoc. 56 (293) (1961) 52–64.
[57]
Derrac Joaquín, García Salvador, Molina Daniel, Herrera Francisco, A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms, Swarm Evol. Comput. 1 (1) (2011) 3–18.
Index Terms
- A practical regularity model based evolutionary algorithm for multiobjective optimization
Index terms have been assigned to the content through auto-classification.
Recommendations
A preference-based evolutionary algorithm for multiobjective optimization: the weighting achievement scalarizing function genetic algorithm
When solving multiobjective optimization problems, preference-based evolutionary multiobjective optimization (EMO) algorithms introduce preference information into an evolutionary algorithm in order to focus the search for objective vectors towards the ...
Evolutionary multiobjective optimization
GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computationMany optimization problems are multiobjective in nature in the sense that multiple, conflicting criteria need to be optimized simultaneously. Due to the conflict between objectives, usually, no single optimal solution exists. Instead, the optimum ...
GECCO 2016 Tutorial on Evolutionary Multiobjective Optimization
GECCO '16 Companion: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference CompanionMany optimization problems are multiobjective in nature in the sense that multiple, conflicting criteria need to be optimized simultaneously. Due to the conflict between objectives, usually, no single optimal solution exists. Instead, the optimum ...
Comments
Information & Contributors
Information
Published In
Elsevier B.V.
Publisher
Elsevier Science Publishers B. V.
Netherlands
Publication History
Published: 01 November 2022
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 27 Jul 2024
Other Metrics
Citations
Cited By
View all- Alp SDehkharghani RAkan TBhuiyan M(2024)MOBRO: multi-objective battle royale optimizerThe Journal of Supercomputing10.1007/s11227-023-05676-480:5(5979-6016)Online publication date: 1-Mar-2024
View Options
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.
Sign in