Enhancement structures for the bat algorithm

Reddy, MP and Ganguli, R (2019) Enhancement structures for the bat algorithm. In: UNSPECIFIED, 18 November 2018 through 21 November 2018, pp. 601-608.

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Abstract

Bat algorithm is a recently developed swarm optimization technique which has been found to be a powerful method for multimodal optimization problems. Some of the drawbacks in standard BA are reduced convergence speed due to lack of inertia weight factor and premature convergence due to low diversification capability of the algorithm as the problem's dimension increases. This paper presents an enhanced bat algorithm (EBA) for optimization which employs five different enhancement structures to improve the standard bat algorithm for better global exploration. EBA is tested on several standard test functions and correlated with recently published studies in the literature using non-parametric statistical tests. It shown from statistical test results that EBA is often superior to the standard BA and is also computationally efficient compared to other published algorithms such as GA and PSO. © 2018 IEEE.

Item Type:	Conference Poster
Publication:	Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	Copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords:	Artificial intelligence; Natural resources exploration; Optimization; Statistical tests; Stochastic systems, bioinspired; Computationally efficient; diversification; Multimodal optimization problems; Non-parametric statistical tests; Pre-mature convergences; stochastic; swarm, Particle swarm optimization (PSO)
Department/Centre:	Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited:	15 Apr 2019 05:19
Last Modified:	15 Apr 2019 05:19
URI:	http://eprints.iisc.ac.in/id/eprint/62092

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