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Multi-Objective Particles Swarm Optimization Approaches

Konstantinos E. Parsopoulos, Michael N. Vrahatis

Source Title: Multi-Objective Optimization in Computational Intelligence: Theory and Practice

ISBN13: 9781599044989|ISBN10: 1599044986|EISBN13: 9781599045009

DOI: 10.4018/978-1-59904-498-9.ch002

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MLA

Parsopoulos, Konstantinos E., and Michael N. Vrahatis. "Multi-Objective Particles Swarm Optimization Approaches." Multi-Objective Optimization in Computational Intelligence: Theory and Practice, edited by Lam Thu Bui and Sameer Alam, IGI Global, 2008, pp. 20-42. https://doi.org/10.4018/978-1-59904-498-9.ch002

APA

Parsopoulos, K. E. & Vrahatis, M. N. (2008). Multi-Objective Particles Swarm Optimization Approaches. In L. Thu Bui & S. Alam (Eds.), Multi-Objective Optimization in Computational Intelligence: Theory and Practice (pp. 20-42). IGI Global. https://doi.org/10.4018/978-1-59904-498-9.ch002

Chicago

Parsopoulos, Konstantinos E., and Michael N. Vrahatis. "Multi-Objective Particles Swarm Optimization Approaches." In Multi-Objective Optimization in Computational Intelligence: Theory and Practice, edited by Lam Thu Bui and Sameer Alam, 20-42. Hershey, PA: IGI Global, 2008. https://doi.org/10.4018/978-1-59904-498-9.ch002

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Abstract

The multiple criteria nature of most real world problems has boosted research on multi-objective algorithms that can tackle such problems effectively, with the smallest possible computational burden. Particle Swarm Optimization has attracted the interest of researchers due to its simplicity, effectiveness and efficiency in solving numerous single-objective optimization problems. Up-to-date, there are a significant number of multi-objective Particle Swarm Optimization approaches and applications reported in the literature. This chapter aims at providing a review and discussion of the most established results on this field, as well as exposing the most active research topics that can give initiative for future research.

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Multi-Objective Particles Swarm Optimization Approaches

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