An improved sine cosine algorithm with greedy selection for global optimization

Abstract

Abstract The sine cosine algorithm (SCA) is a simple and efficient optimization algorithm that utilizes sine and cosine trigonometric functions to update solutions. The SCA may suffer from premature convergence to local optima due to its insufficient utilization of population information and lack of mechanism to escape from local optima. Therefore, this study proposes an improved version of the SCA called the novel sine cosine algorithm (NSCA). NSCA incorporates a new solution update equation, a greedy selection mechanism, and a disturbance mechanism to improve population diversity and prevent search stagnation. Experimental results on the Congress on Evolutionary Computation (CEC) 2017 benchmark function set and six point cloud registration problems demonstrate the effectiveness and robustness of NSCA compared to other algorithms.