Chaotic grey wolf optimization algorithm for constrained optimization problems

By Mehak Kohli, Sankalap Arora. Journal of Computational Design and Engineering, 2018

414 citations recorded by the source corpus.

Abstract The Grey Wolf Optimizer (GWO) algorithm is a novel meta-heuristic, inspired from the social hunting behavior of grey wolves. This paper introduces the chaos theory into the GWO algorithm with the aim of accelerating its global convergence speed. Firstly, detailed studies are carried out on thirteen standard constrained benchmark problems with ten different chaotic maps to find out the most efficient one. Then, the chaotic GWO is compared with the traditional GWO and some other popular meta-heuristics viz. Firefly Algorithm, Flower Pollination Algorithm and Particle Swarm Optimization algorithm. The performance of the CGWO algorithm is also validated using five constrained engineering design problems. The results showed that with an appropriate chaotic map, CGWO can clearly outperform standard GWO, with very good performance in comparison with other algorithms and in application to constrained optimization problems.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/ac375033e7bcb86e97b6208cf3bb3403c8a11b36

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Citations in the corpus

Boosted binary Harris hawks optimizer and feature selection (2020, background): “Kohli et al. [44] provided a chaotic GWO with accelerating convergence speed according to the introduction of the chaos theory.”
Chaotic grasshopper optimization algorithm for global optimization (2019, methodology): “Therefore, it can perform downright searches at higher speeds compared to the stochastic searches that mainly rely on probabilities [29].”
A survey on new generation metaheuristic algorithms (2019, methodology): “Kohli and Arora (2018) introduce the chaotic GWO algorithm to accelerate its global convergence speed. Experiments are studied to carry out to solve standard constrained benchmark problems. Song et al. (2015) propose a novel GWO for surface wave dispersion curve inversion scheme. The proposed algori”
Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems (2022, background): “, 2019), engineering (Kohli and Arora, 2018; Yıldız and Yıldız, 2018), wireless sensor network (Kaushik et al.”