LDW-SCSA: Logistic Dynamic Weight based Sine Cosine Search Algorithm for Numerical Functions Optimization
This work addresses a common issue in numerical optimization for researchers and practitioners, but it is incremental as it modifies an existing algorithm with a known chaotic map.
The paper tackles the problem of optimization algorithms getting trapped in local optima by proposing LDW-SCSA, a novel method that integrates logistic map dynamic weights into the Sine Cosine algorithm, achieving superior performance on numerical benchmark functions compared to other swarm optimization methods.
Particle swarm optimization (PSO) and Sine Cosine algorithm (SCA) have been widely used optimization methods but these methods have some disadvantages such as trapped local optimum point. In order to solve this problem and obtain more successful results than others, a novel logistic dynamic weight based sine cosine search algorithm (LDW-SCSA) is presented in this paper. In the LDW-SCSA method, logistic map is used as dynamic weight generator. Logistic map is one of the famous and widely used chaotic map in the literature. Search process of SCA is modified in the LDW-SCSA. To evaluate performance of the LDW-SCSA, the widely used numerical benchmark functions were utilized as test suite and other swarm optimization methods were used to obtain the comparison results. Superior performances of the LDW-SCSA are proved success of this method.