An investigation of the effects of chaotic maps on the performance of metaheuristics. Issue 8 (9th February 2021) - Gagnon, Iannick; April, Alain; Abran, Alain
Abstract: This article presents an empirical investigation of the effects of chaotic maps on the performance of metaheuristics. Particle Swarm Optimization and Simulated Annealing are modified to use chaotic maps instead of the traditional pseudorandom number generators and then compared on five common benchmark functions using nonparametric null hypothesis statistical testing. Contrary to what has often been assumed, results show that chaotic maps do not generally appear to increase the performance of swarm metaheuristics in a statistically significant way, except possibly for noisy functions. No performance differences were observed with the single‐state Simulated Annealing algorithm. Finally, it is shown that sequence effects may be responsible for the observed performance increase. These findings reveal new research directions in using chaotic maps for metaheuristics research. The MATLAB code used in this article is available in a GitHub repository for suggestions and/or corrections. Abstract : This article proposes a rigorous analysis of the effect of replacing traditional random number generators by chaotic maps in metaheuristics. Particle Swarm Optimization and Simulated Annealing are modified to use chaotic maps and tested on popular benchmark functions.