Liu, WJZain, AMBin Talib, MSMa, SJ2026-02-112026-02-112025International Journal of Advanced Computer Science and Applications, ISSN: 2158-107X (Print); 2156-5570 (Online), The Science and Information Organization, 16(8), 764-783. doi: 10.14569/IJACSA.2025.01608752158-107X2156-5570http://hdl.handle.net/10292/20617This study proposes an improved swarm algorithm, Adaptive Levy Flight Chicken Swarm Optimization with Differential Evolution (ALCSODE), to overcome the low convergence accuracy and imbalance between exploration and exploitation in the original CSO algorithm. The method incorporates adaptive perturbation based on individual differences and a differential evolution mechanism into the rooster update process. An elitism preservation strategy is also applied to enhance population stability and information sharing. The algorithm is evaluated on 24 benchmark functions, including unimodal, high-dimensional multimodal, and CEC2022 functions. Performance metrics such as search trajectories and convergence curves are used to assess its effectiveness. Experimental results show that ALCSODE achieves a better exploration–exploitation trade-off and shows statistically superior performance over seven classical algorithms, confirming its potential as an effective tool for solving complex optimization problems.This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.https://creativecommons.org/licenses/by/4.0/46 Information and Computing Sciences4602 Artificial Intelligence0803 Computer Software1005 Communications Technologies46 Information and computing sciencesChicken swarm optimizationlevy flightdifferential evolution algorithmadaptive adjustment strategyfunction optimizationJournal ArticleOpenAccess10.14569/IJACSA.2025.0160875