Wenjun Liu, Azlan Mohd Zain, Zuriahati Binti Mohd Yunos, Zuraini Ali Shah, Didik Dwi Prasetya, Shengjun Ma
Chicken Swarm Optimization (CSO) is a new metaheuristic algorithm inspired by biologically inspired metaheuristic algorithms that imitate the behavior of chicken flocks. CSO has the advantages of strong global search ability, high stability, and strong multi-subgroup collaborative search ability, and has important research potential. It is widely used in various optimization problems in real life. They still suffer from limitations such as simplistic update strategies, insufficient exchange of information among individuals, limited spatial perturbation, and lack of parameter adaptability, leading to premature convergence and stagnation in local optima. We propose an improved swarm algorithm, Adaptive Levy Flight Chicken Swarm Optimization (ALCSO), to overcome the low convergence accuracy and imbalance balancing search and refinement in the original CSO algorithm. These improvements enhance population diversity, reinforce interactions among individuals, and enable adaptive parameter tuning throughout the search process. Simulation experiment results indicate that ALCSO performs better in terms of solution accuracy and stability. © 2025 IEEE.
Universiti Teknologi Malaysia, Faculty of Computing, Skudai, Malaysia; State University of Malang, Faculty of Engineering, Malang, Indonesia; School of Technology New Zealand Skills and Education, Auckland, New Zealand
