Abstract—An improved algorithm—multi-objective particle swarm optimization with swarm energy conservation (SEC-MOPSO) is proposed, which is aimed to solve the problem of convergence and distribution in multi-objective particle swarm optimization (MOPSO) algorithm. Swarm energy conservation mechanism is used to update the velocity and position of particles. Besides, non-dominated sorting method, adaptive grid mechanism and elitism mechanism are also incorporated into SEC-MOPSO algorithm to improve searching capabilities and avoid falling into the second-best non-dominated front. The simulation results show that SEC-MOPSO has better performance than MOPSO in distribution and convergence.
Index Terms—multi-objective optimization, MOPSO, swarm energy conservation.
Y. Xue is with College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029 China (e-mail: yaoyu225@126.com).
L. Q. Zhao is with College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029 China (e-mail: zhaolq@mail.buct.edu.cn).
J. H. Wu is with College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029 China (e-mail: wujiahuan_buct@126.com).
J. L. Wang is with College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029 China (corresponding author, phone:+86-01064433803 e-mail: wangjl@mail.buct.edu.cn).
Cite: Yaoyu Xue, Liqiang Zhao, Jiahuan Wu, and Jianlin Wang, "An Improved Multi-objective PSO Algorithm with Swarm Energy Conservation," International Journal of Modeling and Optimization vol. 1, no. 3, pp. 226-230, 2011.
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