Abstract
Particle swarm optimization (PSO) has been applied to multi-objective problems. However, PSO may easily get trapped in the local optima when solving complex problems. In order to improve convergence and diversity of solutions, a correlative particle swarm optimization (CPSO) with disturbance operation is proposed, named MO-CPSO, for dealing with multi-objective problems. MO-CPSO adopts the correlative processing strategy to maintain population diversity, and introduces a disturbance operation to the non-dominated particles for improving convergence accuracy of solutions. Experiments were conducted on multi-objective benchmark problems. The experimental results showed that MO-CPSO operates better in convergence metric and diversity metric than three other related works.
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© 2011 Springer-Verlag Berlin Heidelberg
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Shen, Y., Wang, G., Liu, Q. (2011). Correlative Particle Swarm Optimization for Multi-objective Problems. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds) Advances in Swarm Intelligence. ICSI 2011. Lecture Notes in Computer Science, vol 6729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21524-7_3
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DOI: https://doi.org/10.1007/978-3-642-21524-7_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21523-0
Online ISBN: 978-3-642-21524-7
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