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An Approach for Diversity and Convergence Improvement of Multi-Objective Particle Swarm Optimization

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Proceedings of The Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 2013

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 212))

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Abstract

To improve the diversity and convergence of multi-objective optimization, a modified Multi-Objective Particle Swarm Optimization (MOPSO) algorithm using Step-by-step Rejection (SR) strategy is presented in this paper. Instead of using crowding distance based sorting technique, the SR strategy allows only the solution with the least crowding distance to be rejected at one iteration and repeat until the predefined number of solutions selected. With introduction of SR to the selection of particles for next iteration, the modified algorithm MOPSO-SR has shown remarkable performance against a set of well-known benchmark functions (ZDT series). Comparison with the representative multi-objective algorithms, it is indicated that, with SR technique, the proposed algorithm performs well on both convergence and diversity of Pareto solutions.

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Acknowledgments

This work was supported by State Key Laboratory of Power Transmission Equipment & System Security and New Technology (2007DA10512710205) and the National “111” Project (B08036).

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Correspondence to Shan Cheng .

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Cheng, S., Chen, MY., Hu, G. (2013). An Approach for Diversity and Convergence Improvement of Multi-Objective Particle Swarm Optimization. In: Yin, Z., Pan, L., Fang, X. (eds) Proceedings of The Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 2013. Advances in Intelligent Systems and Computing, vol 212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37502-6_59

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  • DOI: https://doi.org/10.1007/978-3-642-37502-6_59

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37501-9

  • Online ISBN: 978-3-642-37502-6

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