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Chaos Multi-objective Particle Swarm Optimization Based on Efficient Non-dominated Sorting

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Bio-Inspired Computing -- Theories and Applications (BIC-TA 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 562))

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Abstract

In order to reduce computational complexity of multiobjective particle swarm optimization algorithm in non-dominated sort, and make the global convergence of the algorithm more quickly in processing continuous problems, a chaos multi-objective particle swarm optimization using efficient non-dominated sort (CMOPSO-ENS) is proposed. In this algorithm, a solution to be assigned to a front needs to be compared only with those solutions that have already been assigned to a front, thereby avoiding many unnecessary comparisons. What’s more, the chaotic map is used to optimize globe best solution in the algorithm. Based on the ergodicity, stochastic property and regularity of chaos, a new superior individual is reproduced by chaotic maps in the current global best individual, and a stochastic selected individual in the current population is replaced by the new superior individual. The algorithm embedded into chaotic maps quickens the evolution process, and improves the abilities of seeking the global excellent solutions. Several benchmark functions are used to test the search capability of the improved algorithm. The simulation results demonstrate that the convergence speed of the proposed CMOPSO-ENS algorithm is superior to original MOPSO algorithms, especially in solving problems with convex and piecewise Pareto front.

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Acknowledgments

The work for this paper was supported by the National Natural Science Foundation of China (Nos. 6147237161472372), Basic and Frontier Technology Research Program of Henan Province (Grant Nos. 142300413214), Program for Science and Technology Innovation Talents in Universities of Henan Province (No. 15HASTIT019), the Innovation Scientists and Technicians Troop Construction Projects of Henan Province (154200510012), and Young Backbone Teachers Project of Henan province (Grant No. 2013GGJS-106).

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Authors and Affiliations

  1. College of Electrical and Information Engineering, Zhengzhou University of Light Industry, No. 5 Dongfeng Road, Zhengzhou, 450002, China

    Xuncai Zhang, Xiaoxiao Wang, Ying Niu & Guangzhao Cui

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  1. Xuncai Zhang
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  2. Xiaoxiao Wang
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  3. Ying Niu
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  4. Guangzhao Cui
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Corresponding author

Correspondence to Guangzhao Cui .

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Editors and Affiliations

  1. Xidian University, Xi'an, China

    Maoguo Gong

  2. Huazhong Univ. of Science and Technology, Wuhan, China

    Pan Linqiang

  3. University of Petroleum, Qingdao, China

    Song Tao

  4. University of Science and Technology of China, Hefei, China

    Ke Tang

  5. Anhui University, Hefei, China

    Xingyi Zhang

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Zhang, X., Wang, X., Niu, Y., Cui, G. (2015). Chaos Multi-objective Particle Swarm Optimization Based on Efficient Non-dominated Sorting. In: Gong, M., Linqiang, P., Tao, S., Tang, K., Zhang, X. (eds) Bio-Inspired Computing -- Theories and Applications. BIC-TA 2015. Communications in Computer and Information Science, vol 562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49014-3_61

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  • DOI: https://doi.org/10.1007/978-3-662-49014-3_61

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-49013-6

  • Online ISBN: 978-3-662-49014-3

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