Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/9536
Title: Multiple populations for multiple objectives : a coevolutionary technique for solving multiobjective optimization problems
Authors: Zhan, ZH
Li, J
Cao, J 
Zhang, J
Chung, HSH
Shi, YH
Keywords: Coevolutionary algorithms
Multiobjective optimization problems (MOPs)
Multiple populations for multiple objectives (MPMO)
Particle swarm optimization (PSO)
Issue Date: 2013
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on cybernetics, 2013, v. 43, no. 2, p. 445-463 How to cite?
Journal: IEEE transactions on cybernetics 
Abstract: Traditional multiobjective evolutionary algorithms (MOEAs) consider multiple objectives as a whole when solving multiobjective optimization problems (MOPs). However, this consideration may cause difficulty to assign fitness to individuals because different objectives often conflict with each other. In order to avoid this difficulty, this paper proposes a novel coevolutionary technique named multiple populations for multiple objectives (MPMO) when developing MOEAs. The novelty of MPMO is that it provides a simple and straightforward way to solve MOPs by letting each population correspond with only one objective. This way, the fitness assignment problem can be addressed because the individuals' fitness in each population can be assigned by the corresponding objective. MPMO is a general technique that each population can use existing optimization algorithms. In this paper, particle swarm optimization (PSO) is adopted for each population, and coevolutionary multiswarm PSO (CMPSO) is developed based on the MPMO technique. Furthermore, CMPSO is novel and effective by using an external shared archive for different populations to exchange search information and by using two novel designs to enhance the performance. One design is to modify the velocity update equation to use the search information found by different populations to approximate the whole Pareto front (PF) fast. The other design is to use an elitist learning strategy for the archive update to bring in diversity to avoid local PFs. CMPSO is comprehensively tested on different sets of benchmark problems with different characteristics and is compared with some state-of-the-art algorithms. The results show that CMPSO has superior performance in solving these different sets of MOPs.
URI: http://hdl.handle.net/10397/9536
ISSN: 2168-2267
EISSN: 2168-2275
DOI: 10.1109/TSMCB.2012.2209115
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