Chaos in popular metaheuristic optimizers – a bibliographic analysis

Abstract

This paper presents an overview of the history and recent efforts in combining chaos theory and evolutionary computation techniques. Various algorithms from the evolutionary computation domain, also known as metaheuristic algorithms, have been successfully enhanced with chaotic components in the past. Numerous ways to incorporate chaos have been examined, and many impressive results have been reported. Implementations of discrete chaotic maps such as Lozi, Hénon, and logistic map as generators of chaotic pseudo-random sequences for controlling evolution operators in metaheuristics have achieved significant popularity. In this survey, we focus on the research field itself and perform a bibliographical analysis to show how broad and active is nowadays the research field of chaos-enhanced metaheuristics and what are some of the most recent works published.

Keywords:

• Evolutionary computing
• chaos
• metaheuristic
• genetic algorithm
• differential evolution
• particle swarm optimization

Maths::

• 65P20
• 00-02

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 https://www.webofscience.com, Search date: October 14th, 2022.

2 https://github.com/P-N-Suganthan/CEC2016.

3 http://numbbo.github.io/workshops/.

Additional information

Funding

This work was supported by the Internal Grant Agency of Tomas Bata University under the Projects no. IGA/CebiaTech/2023/004, and further by the resources of A.I.Lab at the Faculty of Applied Informatics, Tomas Bata University in Zlin (ailab.fai.utb.cz).