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Neural Computing and Applications

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01-11-2014 | Original Article

A chaotic-based big bang–big crunch algorithm for solving global optimisation problems

Author: A. Rezaee Jordehi

Published in: Neural Computing and Applications | Issue 6/2014

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Abstract

Big bang–big crunch (BBBC) algorithm is a fairly novel gradient-free optimisation algorithm. It is based on theories of evolution of the universe, namely the big bang and big crunch theory. The big challenge in BBBC is that it is easily trapped in local optima. In this paper, chaotic-based strategies are incorporated into BBBC to tackle this challenge. Five various chaotic-based BBBC strategies with three different chaotic map functions are investigated and the best one is selected as the proposed chaotic strategy for BBBC. The results of applying the proposed chaotic BBBC to different unimodal and multimodal benchmark functions vividly show that chaotic-based BBBC yields quality solutions. It significantly outperforms conventional BBBC, cuckoo search optimisation and gravitational search algorithm.

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Title: A chaotic-based big bang–big crunch algorithm for solving global optimisation problems
Author: A. Rezaee Jordehi
Publication date: 01-11-2014
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 6/2014
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-014-1613-1

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