@inproceedings{6b0fb355558d4d0fbdde6185267075a4,
title = "A new multi-swarm multi-objective particle swarm optimization based on Pareto front set",
abstract = "In this paper, a new multi-swarm method is proposed for multi-objective particle swarm optimization. To enhance the Pareto front searching ability of PSO, the particles are divided into many swarms. Several swarms are dynamically searching the objective space around some points of the Pareto front set. The rest of particles are searching the space keeping away from the Pareto front to improve the global search ability. Simulation results and comparisons with existing Multi-objective Particle Swarm Optimization methods demonstrate that the proposed method effectively enhances the search efficiency and improves the search quality.",
keywords = "Multi-objective Optimization, Multiple swarms, Pareto front, Particle Swarm Optimization",
author = "Yanxia Sun and \{Van Wyk\}, \{Barend Jacobus\} and Zenghui Wang",
year = "2012",
doi = "10.1007/978-3-642-25944-9\_27",
language = "English",
isbn = "9783642259432",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "203--210",
booktitle = "Advanced Intelligent Computing Theories and Applications",
address = "Germany",
note = "7th International Conference on Intelligent Computing, ICIC 2011 ; Conference date: 11-08-2011 Through 14-08-2011",
}