Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

Multi-system genetic algorithm for complex system optimization

  • Optimization
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Complex system optimization is an emerging research topic in the field of evolutionary computation, whose goal is to handle complex systems with multiple coupled subsystems, each including multiple objectives and multiple constraints in real-world applications. This paper proposes a multi-system genetic algorithm (MSGA), stemming from implicit parallelism in population-based search algorithms, to solve multiple coupled subsystems simultaneously in a complex system. The proposed MSGA is composed of within-subsystem evolution and cross-subsystem migration operators. The objective of the former is to optimize each subsystem by appropriate search strategies, and the objective of the latter is to exchange information between multiple subsystems by migration, which is based on the similarity probability of objectives and constraints, and the intersection probability of solutions in different subsystems. During migration across subsystems, three statistical approaches of measuring similarity and three metrics of solution intersection in information theory are used to calculate these probabilities. Performance is tested on a set of multi-subsystem benchmark functions, and the simulation results show that cross-subsystem migration plays the key role for the performance of MSGA. Furthermore, the proposed MSGA is compared with other competitive algorithms, and results show that it is a promising multi-system optimization algorithm. In summary, the contribution of this paper is the introduction of multi-system optimization to the EA community.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

source subsystem

Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Abell J, Du D (2010) A framework for multi-objective, biogeography-based optimization of complex system families. In: Proceeding of AIAA/ISSMO multidiscipline analysis optimization conference, Fort Worth, Texas, pp 1–10

  • Agrawal R (2020) Finite-sample concentration of the multinomial in relative entropy. IEEE Trans Inf Theory 66(10):6297–6302

    Article  MathSciNet  Google Scholar 

  • Allison J (2004) Complex system optimization: a review of analytical target cascading, collaborative optimization, and other formulations. M. S. Thesis, University of Michigan, Ann Arbor, MI

  • Antonio LM, Coello CA (2018) Coevolutionary multi-objective evolutionary algorithms: a survey of the state-of-the-art. IEEE Trans Evol Comput 22(6):851–865

    Article  Google Scholar 

  • Bhunre PK, Bhowmick P, Mukherjee J (2019) On efficient computation of inter-simplex Chebyshev distance for voxelization of 2-manifold surface. Inf Sci 499:102–123

    Article  MathSciNet  Google Scholar 

  • Chen Y, Ye J, Li J (2020) Aggregated Wasserstein distance and state registration for hidden Markov models. IEEE Trans Pattern Anal Mach Intell 42(9):2133–2147

    Article  Google Scholar 

  • Cheng S, Ma L, Lu H, Lei X, Shi Y (2021) Evolutionary computation for solving search-based data analytics problems. Artif Intell Rev 54(2):1321–1348

    Article  Google Scholar 

  • Chiu WY, Yen GG, Juan TK (2016) Minimum Manhattan distance approach to multiple criteria decision making in multiobjective optimization problems. IEEE Trans Evol Comput 20(6):972–985

    Article  Google Scholar 

  • Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  • Derrac J, Garcia S, Molina D, Herrera F (2011) 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):3–18

    Article  Google Scholar 

  • Dizangian B, Ghasemi MR (2019) Border-search and jump reduction method for size optimization of spatial truss structures. Front Struct Civ Eng 13(1):123–134

    Article  Google Scholar 

  • Dizangian B, Ghasemi MR (2021) Optimization of structural and mechanical engineering problems using the enriched ViS-BLAST method. Struct Eng Mech 77(5):613–626

    Google Scholar 

  • Du D, Simon D (2013) Complex system optimization using biogeography-based optimization, Complexity, Article ID: 456232

  • Gee S, Tan KC, Abbass H (2017) A benchmark test suite for dynamic evolutionary multiobjective optimiztion. IEEE Trans Cybern 47(2):461–472

    Google Scholar 

  • Gong YJ, Chen WN, Zhan ZH, Zhang J, Li Y, Zhang Q (2015) Distributed evolutionary algorithms and their models: A survey of the state-of-the-art. Appl Soft Comput 34:286–300

    Article  Google Scholar 

  • Gupta A, Ong YS, Feng L, Tan KC (2016) Multiobjective multifactorial optimization in evolutionary multitasking. IEEE Trans Cybern 47(7):1652–1665

    Article  Google Scholar 

  • Hamidzadeh J, Kashefi N, Moradi M (2020) Combined weighted multi-objective optimizer for instance reduction in two-class imbalanced data problem. Eng Appl Artif Intell 90:103500

    Article  Google Scholar 

  • Hathaway R, Bezdek J (2001) Fuzzy c-means clustering of incomplete data. IEEE Trans Syst Man Cybern B Cybern 31(5):735–744

    Article  Google Scholar 

  • Hosseini N, Ghasemi MR, Dizangian B (2022) ANFIS-based optimum design of real power transmission towers with size, shape and panel design variables using BBO algorithm. IEEE Trans Power Delivery 37(1):29–39

    Article  Google Scholar 

  • Ma L, Cheng S, Shi Y (2021) Enhancing learning efficiency of brain storm optimization via orthogonal learning design. IEEE Trans Syst Man Cybern Syst 51(11):6723–6742

    Article  Google Scholar 

  • Ma H, Fei M, Jiang Z, Li L, Zhou H, Crookes D (2020) A multi-population based multi-objective evolutionary algorithm. IEEE Trans Cybern 50(2):689–702

    Article  Google Scholar 

  • Ma H, Shen S, Yu M, Yang Z, Fei M, Zhou H (2019) Multi-population techniques in nature inspired optimization algorithms: a comprehensive survey. Swarm Evol Comput 44:365–387

    Article  Google Scholar 

  • Martins J, Lambe A (2013) Multidisciplinary design optimization: a survey of architectures. AIAA J 51(9):2049–2075

    Article  Google Scholar 

  • Ong YS, Gupta A (2016) Evolutionary multitasking: a computer science view of cognitive multitasking. Cogn Comput 8(2):125–142

    Article  Google Scholar 

  • Pulido B, Zamarreno J, Merino A, Bregon A (2019) State space neural networks and model-decomposition methods for fault diagnosis of complex industrial systems. Eng Appl Artif Intell 79:67–86

    Article  Google Scholar 

  • Roshan SE, Asadi S (2020) Improvement of Bagging performance for classification of imbalanced datasets using evolutionary multi-objective optimization. Eng Appl Artif Intell 87:103319

    Article  Google Scholar 

  • Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713

    Article  Google Scholar 

  • Yang C, Ding J, Jin Y, Chai T (2020) Offline data-driven multiobjective optimization: knowledge transfer between surrogates and generation of final solutions. IEEE Trans Evol Comput 24(3):409–423

    Google Scholar 

  • Yuan Y, Ong YS, Feng L, Qin A K, Gupta A, Da B, Zhang Q, Tan K C, Jin Y, Ishibuchi H (2016) Evolutionary multitasking for multiobjective continuous optimization: benchmark problems, performance metrics and baseline results, Technical Report

  • Zhang X, Delpha C, Diallo D (2020) Incipient fault detection and estimation based on Jensen-Shannon divergence in a data-driven approach. Signal Process 169:107410

    Article  Google Scholar 

  • Zhang Q, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731

    Article  Google Scholar 

  • Zhao Z, Liu B, Zhang C, Liu H (2019) An improved adaptive NSGA-II with multi-population algorithm. Appl Intell 49(2):569–580

    Article  Google Scholar 

  • Zheng X, Lei Y, Qin A K, Zhou D, Shi J, Gong M (2019) Differential evolutionary multi-task optimization. In: Proceeding of 2019 IEEE congress on evolutionary computation, Wellington, New Zealand, pp 1914–1922

  • Zhou A, Qu B, Li H, Zhao S, Suganthan PN, Zhang Q (2011) Multi-objective evolutionary algorithms: a survey of the state of the art. Swarm Evol Comput 1(1):32–49

    Article  Google Scholar 

Download references

Acknowledgements

This research is supported by Zhejiang Provincial Natural Science Foundation of China under Grant No. LY19F030011, the National Natural Science Foundation of China under Grant Nos. 52077213 and 62003332, the National Key Research and Development Project of China under Grant No. 2018YFB1702200.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Shaoxing University, Shaoxing, Zhejiang, China

    Haiping Ma & Yu Shan

  2. Department of Electrical and Electronic Engineering, University of Nottingham Ningbo China, Ningbo, Zhejiang, China

    Jinglin Wang

  3. School of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China

    Xiaolei Chen

  4. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China

    Zhile Yang

  5. Department of Electrical and Computer Engineering, Cleveland State University, Cleveland, OH, USA

    Dan Simon

Authors
  1. Haiping Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu Shan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jinglin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaolei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhile Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Dan Simon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Haiping Ma.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Data availability

Enquires about data availability should be directed to the authors.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ma, H., Shan, Y., Wang, J. et al. Multi-system genetic algorithm for complex system optimization. Soft Comput 26, 10187–10205 (2022). https://doi.org/10.1007/s00500-022-07286-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-022-07286-3

Keywords

Search

Navigation