An Improved Lion Swarm Optimization Algorithm With Chaotic Mutation Strategy and Boundary Mutation Strategy for Global Optimization

Lion swarm optimization (LSO) inspired by the natural division of labor among lion king, lionesses and cubs in a group, i.e., king guarding, hunting following, is relatively novel intelligent technique. Due to its remarkable performance, canonical LSO has been extensively researched. However, how balance contradictions between exploration exploitation alleviate premature convergence are two critical concerns that need be dealt with study. To address these drawbacks, enhance broaden application domain, an improved algorithm chaotic mutation strategy boundary (CBLSO) proposed this paper. In algorithm, based on cubic mapping designed ability while concept multilevel parallel adopted manage constraint violations, which beneficial for improving algorithm. The CBLSO evaluated 56 classic test functions 30 CEC2014 benchmark functions, compared quite few state-of-the-art algorithms regarding often-used performance metrics. experimental results demonstrate superior embedded strategies balancing exploitation. Furthermore, applied optimal dispatch problem cascade hydropower stations constraints handling method paper validate good practicability performance. case study China’s Wujiang indicate can produce better more reliable than other comparison competitive speed. Thus, we conclude effective alterative tool solve complex numerical problems real-world complicated constraints.