A Multi-Strategy Enhanced Chaotic Evolution Algorithm for Global Optimization and Engineering Applications

Chaotic Evolution Optimization (CEO) is a meta-heuristic optimization method inspired by the hyper-chaotic dynamics of two-dimensional discrete amnesia mapping. It enhances global exploration by simulating co-evolution among chaotic individuals and employing chaotic mapping to generate random search directions. Despite its competitiveness, the CEO algorithm has limitations in convergence accuracy, easy to fall into local optima, and premature convergence, particularly in complex problem domains. In this paper, a Multi-strategy Chaotic Evolution Optimization (MCEO) algorithm is proposed. It leverages four improvement strategies to mitigate these issues and synergistically boost the algorithm’s performance. First, a good point set initialization strategy generates a more uniform initial population for faster convergence into promising regions. Second, a novel nonlinear dynamic adjustment factor is designed to balance between global exploration and local exploitation, thereby improving convergence accuracy. Third, a spiral-guided random walk strategy replaces the original position update in the local exploitation phase, preventing search homogenization. Finally, a lens opposition-based learning strategy updates the current best solution after each iteration to reduce the risk of falling into local optima. This experimental work comprised two components. A parameter sensitivity analysis and ablation experiments for the MCEO algorithm were conducted on the IEEE CEC2020 benchmark. Subsequently, the algorithm’s performance in multidimensional optimization was assessed using the CEC2022 and CEC2017 test suites, benchmarking it against nine other peer algorithms. Results show that MCEO algorithm exhibits significantly better convergence accuracy and stability in most scenarios. Additionally, validation on three constrained engineering design problems and one wireless sensor network coverage problem confirms that MCEO algorithm outperforms the basic CEO and eight other algorithms in both optimization performance and practical applicability.