Multi-Objective Random Drift Particle Swarm Optimization Algorithm Based on RDPSO and Crowding Distance Sorting
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Multi-Objective Random Drift Particle Swarm Optimization Algorithm Based on RDPSO and Crowding Distance Sorting

Authors: Yiqiong Yuan, Jun Sun, Dongmei Zhou, Jianan Sun

Abstract:

In this paper, we presented a Multi-Objective Random Drift Particle Swarm Optimization algorithm (MORDPSO-CD) based on RDPSO and crowding distance sorting to improve the convergence and distribution with less computation cost. MORDPSO-CD makes the most of RDPSO to approach the true Pareto optimal solutions fast. We adopt the crowding distance sorting technique to update and maintain the archived optimal solutions. Introducing the crowding distance technique into MORDPSO can make the leader particles find the true Pareto solution ultimately. The simulation results reveal that the proposed algorithm has better convergence and distribution.

Keywords: Multi-objective optimization, random drift particle swarm optimization, crowding distance, Pareto optimal solution.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1123861

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References:


[1] A. E. Smith, ”Multi-objective optimization using evolutionary algorithms (Book Review),” Evolutionary Computation, IEEE Transactions on, vol. 6, pp. 526-526, 2002.
[2] C. A. Coello and M. S. Lechuga, ”MOPSO: a proposal for multiple objective particle swarm optimization,” in Evolutionary Computation, 2002. CEC ’02. Proceedings of the 2002 Congress on, 2002, pp. 1051-1056.
[3] F. G. Guimaraes, E. F. Wanner, and R. H. C. Takahashi, ”A quality metric for multi-objective optimization based on Hierarchical Clustering Techniques,” in Evolutionary Computation, 2009. CEC ’09. IEEE Congress on, 2009, pp. 3292-3299.K. Elissa, Title of paper if known, unpublished.
[4] M. Salazar-Lechuga and J. E. Rowe, ”Particle swarm optimization and fitness sharing to solve multi-objective optimization problems,” in Evolutionary Computation, 2005. The 2005 IEEE Congress on, 2005, pp. 1204-1211 Vol. 2.
[5] C. A. C. Coello, G. T. Pulido, and M. S. Lechuga, ”Handling multiple objectives with particle swarm optimization,” Evolutionary Computation, IEEE Transactions on, vol. 8, pp. 256-279, 2004.
[6] Y. J. J, Z. J. Z, R. C. fang, and e. al., ”Multi-objective particle swarm optimization based on adaptive grid algorithm,” J of System Simulation, pp. 5843-5847, 2008.
[7] Sun J, Lai C H, Wu X J. Particle swarm optimisation: classical and quantum perspectives (M). CRC Press, 2011.
[8] Raquel C R, Jr P C N. An effective use of crowdingdistance in multiobjective particle swarm optimization (C). Proc of the 2005 Workshops on Genetic and Evolutionary Computation. Washington: ACM Press, 2005: 257-264.
[9] E. Zitzler, K. Deb, and L. Thiele, ”Comparison of Multiobjective Evolutionary Algorithms: Empirical Results,” Evolutionary Computation, vol. 8, pp. 173-195, 2000.
[10] J. Sun, X. Wu, V. Palade, W. Fang, and Y. Shi, ”Random drift particle swarm optimization,” arXiv preprint arXiv:1306.2863, 2013.
[11] S. Jun, V. Palade, W. Xiao-Jun, F. Wei, and W. Zhenyu, ”Solving the Power Economic Dispatch Problem With Generator Constraints by Random Drift Particle Swarm Optimization,” Industrial Informatics, IEEE Transactions on, vol. 10, pp. 222-232, 2014.
[12] L. Liqin, Z. Xueliang, X. Liming, and D. Juan, ”A novel multi-objective particle swarm optimization based on dynamic crowding distance,” in Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on, 2009, pp. 481-485.