MCSO: Levy’s Flight Guided Modified Chicken Swarm Optimization

Abstract

This paper proposes a Modified Chicken Swarm Optimization (MCSO) in which the local optima and early convergence problem of Chicken Swarm Optimization (CSO) is addressed and solved. The CSO adopted the Swarm Intelligence (SI) of chickens to solve the optimization problem in which the behaviour of roosters, hens, and chicks for food search is mathematically formulated. Hens follow their group rooster, whereas mother hen is followed by chicks in search of food. The problem occurs whenever the rooster follows the wrong path and is stuck in the local optima so the mother hen and, therefore, the chick. This situation leads to early convergence and may not provide global optimization. Most of the existing research studies focused on solving the local optima problem of hens. Hence, there is a need to address the local optima problem of roosters as well. The paper offers a solution to this problem by using the randomness phenomenon of Levy’s Fight. Levy’s flight is offered to guide the roosters, hens, and chicks, which allows the chickens to choose a random direction in a situation when there is no way to find the optimal solution. The inclusion of Levy’s flight enhances the self-learning capability of the chicken. The MCSO is tested on the benchmark functions, IEEE CEC-2017 functions and an engineering problem. The results are validated by a comparative analysis with well-known SI agorithms. The results indicate that the MCSO provides competitive performance. The results are statistically verified with the win-tie-loss, Bonferroni-Dunn post-hoc, and Wilcoxon tests.