Improved Competitive Swarm Optimizer with Linear Population Reduction for Large-scale Optimization

Competitive swarm optimizer (CSO) is an efficient and effective swarm intelligence approach, especially for large-scale optimization. This paper presents an enhanced version of CSO termed improved CSO with linear population reduction (L-ICSO). The novel triple-individuals competitive mechanism is introduced to strengthen the optimization performance of L-ICSO, and the linear population reduction mechanism from L-SHADE is integrated into L-ICSO to highlight the explorative search in the initial phase of optimization and emphasize the exploitative behavior in the late phase. We conduct comprehensive numerical experiments in 100-dimensional CEC2017 benchmark functions. Ten state-of-the-art optimizers such as L-SHADE, jSO, L-SHADE-cnEpSin, and the original CSO are employed as competitor algorithms. The Mann–Whitney U and Holm multiple comparison tests are used to measure the statistical significance between L-ICSO and competitor algorithms. The experimental results and statistical analysis confirm the efficiency and effectiveness of our proposed L-ICSO in addressing large-scale optimization problems. The source code of L-ICSO can be found at https://github.com/RuiZhong961230/L-ICSO.