Handling multi-objective optimization problems with a multi-swarm cooperative particle swarm optimizer |
| |
| Authors: | Yong Zhang Dun-wei Gong Zhong-hai Ding |
| |
| Affiliation: | 1. School of Information and Electronic Engineering, China University of Mining and Technology, Xunzhou 221008, China;2. Department of Mathematical Sciences, University of Nevada, Las Vegas, NV 89154-4020, USA;1. Instituto Cajal, Consejo Superior de Investigaciones Científicas, Madrid, Spain;2. Dept. of Computer Systems Architecture and Technology, Universidad Politécnica de Madrid, Spain;1. Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran;2. Department of Electrical, Computer, and Software Engineering, University of Ontario Institute of Technology (UOIT), 2000 Simcoe Street North, Oshawa, ON L1H 7K4, Canada;1. Department of Computer Engineering, Firat University, 23119 Elazig, Turkey;2. Department of Computer Science, University of Calgary, Calgary, AB, Canada;3. Department of Computer Science, Global University, Beirut, Lebanon |
| |
| Abstract: | This paper presents a new multi-objective optimization algorithm in which multi-swarm cooperative strategy is incorporated into particle swarm optimization algorithm, called multi-swarm cooperative multi-objective particle swarm optimizer (MC-MOPSO). This algorithm consists of multiple slave swarms and one master swarm. Each slave swarm is designed to optimize one objective function of the multi-objective problem in order to find out all the non-dominated optima of this objective function. In order to produce a well distributed Pareto front, the master swarm is developed to cover gaps among non-dominated optima by using a local MOPSO algorithm. Moreover, in order to strengthen the capability locating multiple optima of the PSO, several improved techniques such as the Pareto dominance-based species technique and the escape strategy of mature species are introduced. The simulation results indicate that our algorithm is highly competitive to solving the multi-objective optimization problems. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
