An Improved Chaos Sparrow Search Optimization Algorithm Using Adaptive Weight Modification and Hybrid Strategies

Sparrow Search Algorithm (SSA) is a kind of novel swarm intelligence algorithm, which has been applied in-to various domains because its unique characteristics, such as strong global search capability, few adjustable parameters, and clear structure. However, the SSA still some inherent weaknesses that hinder further development, poor population diversity, weak local searchability, falling into optimal easily. This manuscript proposes an improved chaos sparrow optimization algorithm (ICSSOA) to overcome mentioned shortcomings standard SSA. Firstly, Cubic mapping introduced increase diversity in initialization stage. Then, adaptive weight employed automatically adjust step for balancing performance capability different phases. Finally, hybrid strategy Levy flight reverse learning presented perturb position individuals according random strategy, greedy utilized select with higher fitness values decrease possibility optimum. The experiments are divided two modules. former investigates proposed approach through 20 benchmark functions using ICSSOA, SSA, other four variants. In latter experiment, selected also optimized by ICSSOA classic algorithms, namely ACO, PSO, GWO, WOA. Experimental results corresponding statistical analysis revealed only one function test was slightly lower than CSSOA WOA among 20-function optimization. most cases, both solution accuracy convergence speed algorithms. It indicates outstanding ability jump out