Adaptive particle swarm optimization algorithms

Ai, The Jin and Kachitvichyanukul, Voratas (2008) Adaptive particle swarm optimization algorithms. In: Proceedings of the International Conference on Intelligent Logistics Systems, 21-230Agustus 2008, Shanghai Maritime University, China.

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Abstract

This paper reviews the literature on the mechanisms foradapting parameters of particle swarm optimization (PSO) algorithm. The discussion focuses on the mechanisms for adaptively setting such parameters as inertia weight, acceleration constants, number of particles and number of iterations. Two mechanisms are proposed and tasted. The velocity index pattern is proposed for adapting the inertia weight while the acceleration constants are adapted via the use of relative gaps between various learning terms and the best objective function values. The mechanismns are demonstrated by modifying GLNPSO for a specific optimization problem, namely, the vehicle routing problem (VRP). The preliminary indicates that the addition of the proposed adaptive mechanisms can privide good algorithm performance in terms of solution quality with a slightly slower computational time.

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