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Movement Strategies for Multi-Objective Particle Swarm Optimization

Movement Strategies for Multi-Objective Particle Swarm Optimization

S. Nguyen, V. Kachitvichyanukul
Copyright: © 2010 |Volume: 1 |Issue: 3 |Pages: 21
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781609609542|DOI: 10.4018/jamc.2010070105
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MLA

Nguyen, S., and V. Kachitvichyanukul. "Movement Strategies for Multi-Objective Particle Swarm Optimization." IJAMC vol.1, no.3 2010: pp.59-79. http://doi.org/10.4018/jamc.2010070105

APA

Nguyen, S. & Kachitvichyanukul, V. (2010). Movement Strategies for Multi-Objective Particle Swarm Optimization. International Journal of Applied Metaheuristic Computing (IJAMC), 1(3), 59-79. http://doi.org/10.4018/jamc.2010070105

Chicago

Nguyen, S., and V. Kachitvichyanukul. "Movement Strategies for Multi-Objective Particle Swarm Optimization," International Journal of Applied Metaheuristic Computing (IJAMC) 1, no.3: 59-79. http://doi.org/10.4018/jamc.2010070105

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Abstract

Particle Swarm Optimization (PSO) is one of the most effective metaheuristics algorithms, with many successful real-world applications. The reason for the success of PSO is the movement behavior, which allows the swarm to effectively explore the search space. Unfortunately, the original PSO algorithm is only suitable for single objective optimization problems. In this paper, three movement strategies are discussed for multi-objective PSO (MOPSO) and popular test problems are used to confirm their effectiveness. In addition, these algorithms are also applied to solve the engineering design and portfolio optimization problems. Results show that the algorithms are effective with both direct and indirect encoding schemes.

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