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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/19001
Title: A non-deterministic adaptive inertia weight in PSO
Authors: Deep K.
Madhuri
Bansal J.C.
Published in: Proceedings of Genetic and Evolutionary Computation Conference, GECCO'11
Abstract: Particle Swarm Optimization (PSO) is a relatively recent swarm intelligence algorithm inspired from social learning of animals. Successful implementation of PSO depends on many parameters. Inertia weight is one of them. The selection of an appropriate strategy for varying inertia weight w is one of the most effective ways of improving the performance of PSO. Most of the works done till date for investigating inertia weight have considered small values of w, generally in the range [0,1]. This paper presents some experiments with widely varying values of w which adapts itself according to improvement in fitness at each iteration. The same strategy has been implemented in two different ways giving rise to two inertia weight variants of PSO namely Globally Adaptive Inertia Weight (GAIW) PSO, and Locally Adaptive Inertia Weight (LAIW) PSO. The performance of the proposed variants has been compared with three existing inertia weight variants of PSO employing a test suite of 6 benchmark global optimization problems. The experiments show that the results obtained by the proposed variants are comparable with those obtained by the existing ones but with better convergence speed and less computational effort. Copyright 2011 ACM.
Citation: Proceedings of Genetic and Evolutionary Computation Conference, GECCO'11, (2011), 1155- 1161. Dublin
URI: https://doi.org/10.1145/2001576.2001732
http://repository.iitr.ac.in/handle/123456789/19001
Issue Date: 2011
Keywords: Adaptive inertia weight
Dynamic inertia weight
Non-deterministic inertia weight
Particle Swarm Optimization
Adaptive inertia
Computational effort
Convergence speed
Dynamic inertia weight
Global optimization problems
Inertia weight
Particle swarm
Social learning
Swarm Intelligence
Animals
Artificial intelligence
Cellular automata
Experiments
Global optimization
Particle swarm optimization (PSO)
ISBN: 9780000000000
Author Scopus IDs: 8561208900
49663604500
57189656835
Author Affiliations: Deep, K., Department of Mathematics, Indian Institute of Technology Roorkee, Uttarakhand, India
Madhuri, Department of Mathematics, Indian Institute of Technology Roorkee, Uttarakhand, India
Bansal, J.C., Department of Mathematics, ABV-Indian Institute of Information Technology and Management, Gwalior (M.P.), India
Corresponding Author: Deep, K.; Department of Mathematics, Indian Institute of Technology Roorkee, Uttarakhand, India; email: kusumfma@iitr.ernet.in
Appears in Collections:Conference Publications [MA]

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