LDW-SCSA: Logistic Dynamic Weight based Sine Cosine Search Algorithm for Numerical Functions Optimization

TL;DR

This paper introduces LDW-SCSA, a novel optimization algorithm combining logistic dynamic weights with sine cosine search to improve global search capabilities and avoid local optima in numerical function optimization.

Contribution

The paper proposes a new optimization method that integrates logistic map-based dynamic weights into SCA, enhancing its ability to escape local optima and outperform existing algorithms.

Findings

LDW-SCSA outperforms traditional PSO and SCA on benchmark functions.

The logistic dynamic weight improves search diversity and convergence.

Experimental results confirm the effectiveness of the proposed method.

Abstract

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.