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

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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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Authors and Affiliations

  1. Department of Electrical Engineering, University Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia

    A. Rezaee Jordehi

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  1. A. Rezaee Jordehi
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Correspondence to A. Rezaee Jordehi.

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Rezaee Jordehi, A. A chaotic-based big bang–big crunch algorithm for solving global optimisation problems. Neural Comput & Applic 25, 1329–1335 (2014). https://doi.org/10.1007/s00521-014-1613-1

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  • DOI: https://doi.org/10.1007/s00521-014-1613-1

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