Exploitation Enhanced Sine Cosine Algorithm with Compromised Population Diversity for Optimization

The sine cosine algorithm is a newly proposed optimization algorithm and it has shown remarkable performance in solving some optimization problems. However, its search ability deteriorates when facing complex problems because of premature convergence. To mitigate this drawback, we propose a population diversity based local refinement strategy to help it maintain population diversity in a high level. Twenty-nine test functions in CEC’17 benchmark suit is implemented to evaluate its performance. The experimental results indicate the flexibility of controlling diversity and the proposed strategy is promising to be applied to other algorithms.