Research on Reproduction Operator and Multi-objective Optimization Based on Multi-source Mating Selection Strategy

被引：0

作者：

Zhang Y. ^{[1
]}

Lu Y. ^{[1
]}

Wang S. ^{[1
]}

Lu T. ^{[2
]}

机构：

[1] School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou

[2] School of Bussiness, Changzhou University, Changzhou

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2021年 / 49卷 / 09期

关键词：

Clustering learning; Evolutionary algorithm; Mating selection; Multi-objective optimization;

D O I：

10.12263/DZXB.20200397

中图分类号：

学科分类号：

摘要：

This work proposes a multi-source mating selection based multi-objective evolutionary algorithm(MMSEA). In MMSEA, the spectral clustering algorithm is used to exploit the property of the multi-objective optimization problems. Based on the obtained population structure information, a multi-source mating selection strategy is designed to guide the algorithm search. The convergence of the algorithm is accelerated and the diversity of the population is maintained by setting multiple mating selections for each individual and using similar-based reproduction. The experimental results show that the proposed reproduction operator can effectively improve the performance of the algorithm. MMSEA is experimentally compared with variety of mainstream multi-objective evolutionary algorithms, and parameter sensitivity is also performed. In these experiments, MMSEA demonstrates strong competitiveness over the other approaches in solving typical multi-objective optimization problems with complex characteristics. © 2021, Chinese Institute of Electronics. All right reserved.

引用

页码：1754 / 1760

页数：6

共 16 条

[1]

Zhou A M, Qu B Y, Li H, Et al., Multiobjective evolutionary algorithms: A survey of the state of the art, Swarm and Evolutionary Computation, 1, 1, pp. 32-49, (2011)

[2]

Zhang Q F, Zhou A M, Jin Y C., RM-MEDA: A regularity model-based multiobjective estimation of distribution algorithm, IEEE Transactions on Evolutionary Computation, 12, 1, pp. 41-63, (2008)

[3]

Zhang H, Zhou A M, Song S M, Et al., A self-organizing multiobjective evolutionary algorithm, IEEE Transactions on Evolutionary Computation, 20, 5, pp. 792-806, (2016)

[4]

Sun J Y, Zhang H, Zhou A M, Et al., Learning from a stream of nonstationary and dependent data in multiobjective evolutionary optimization, IEEE Transactions on Evolutionary Computation, 23, 4, pp. 541-555, (2019)

[5]

Li X., Study of clustering-based mating restriction strategies for multiobjective evolutionary algorithms, (2019)

[6]

Wang S, Zhang H, Zhang Y, Et al., A spectral clustering-based multi-source mating selection strategy in evolutionary multi-objective optimization, IEEE Access, 7, pp. 131851-131864, (2019)

[7]

Luxburg U., A tutorial on spectral clustering, Statistics and Computing, 17, 4, pp. 395-416, (2007)

[8]

Deb K, Pratap A, Agarwal S, Et al., A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6, 2, pp. 182-197, (2002)

[9]

de Jong K., Evolutionary computation: A unified approach, Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, pp. 185-199, (2016)

[10]

Gu F, Liu H L, Tan K C., A multiobjective evolutionary algorithm using dynamic weight design method, International Journal of Innovative Computing, Information and Control, 8, 5, pp. 3677-3688, (2012)

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