Static Learning Particle Swarm Optimization with Enhanced Exploration and Exploitation using Adaptive Swarm Size

Panda, A. and Ghoshal, S. and Konar, A. and Banerjee, B. and Nagar, Atulya K. (2016) Static Learning Particle Swarm Optimization with Enhanced Exploration and Exploitation using Adaptive Swarm Size. In: IEEE Congress on Evolutionary Computation 2016, 24-29 July 2016, Vancouver, Canada.

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Official URL: http://ieeexplore.ieee.org/document/7744016/

Abstract

In this paper, a novel Static Learning (SL) strategy to adaptively vary swarm size has been proposed and integrated with Particle Swarm Optimization algorithm. Besides, the whole population has been divided into two sub swarms, where particles of different sub swarms interact within their neighbourhood and the existence of better particle is determined by evaluating its survival probability. Proper resource based particle replacement scheme and a linear chaotic term has also been included to ensure preservation of diversity of the swarm. In addition, the PSO algorithm is divided into two phases, with relevant algorithmic modification for each phase. The first phase is assigned to focus solely on better exploration of the search space. The second phase focuses on better utilization of the explored information. The proposed Static Learning Particle Swarm Optimization with Enhanced Exploration and Exploitation using Adaptive Swarm Size (SLPSO) algorithm is tested on a set of shifted and rotated benchmark problems and compared with six other recent state-of-the-art PSO algorithms. The proposed (SLPSO) algorithm demonstrates superior performance over other PSO variants.

Item Type: Conference or Workshop Item (Paper)
Additional Information and Comments: (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works."
Keywords: Static learning, exploration, exploitation, particle swarm optimizer.
Faculty / Department: Faculty of Science > Mathematics and Computer Science
Depositing User: Atulya Nagar
Date Deposited: 09 Jun 2016 11:47
Last Modified: 24 Nov 2016 11:46
URI: http://hira.hope.ac.uk/id/eprint/1439

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