Improved sine cosine algorithm for optimization problems based on self-adaptive weight and social strategy

The Sine Cosine Algorithm (SCA) is a well-known optimization technique that utilizes sine and cosine functions to identify optimal solutions. Despite its popularity, the SCA has limitations in terms of low diversity, stagnation local optima, difficulty achieving global optimization, particularly complex large-scale problems. In response, we propose novel approach named Improved Weight Strategy (IWSCA). IWSCA achieves this by introducing self-adaptive weight social strategies enable algorithm efficiently search for solutions performance evaluated with 23 benchmark test IEEE CEC 2019 suite, compare it state-of-the-art heuristic two improved versions SCA. Our experimental results demonstrate outperforms existing methods convergence precision robustness.