  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.
