Implementation of Differential Evolutionary Algorithm for Different Approaches

ABSTRACT An efficient design of optimal circuit is the cornerstone of any design environment. This study introduces a Differential evolutionary based methodology for optimal testing of benchmark functions. The DE algorithm has been used in many practical cases and has revealed good convergence properties. It has only a few control parameters, which are kept fixed throughout the entire evolutionary process. As a relatively new population based optimization technique, differential evolution has been drawing increasing attention for a wide variety of engineering applications. Unlike the conventional evolutionary algorithms which depend on predefined likelihood distribution function for mutation process, differential evolution uses the differences of randomly sampled pairs of objective vectors for its mutation process. Consequently, the object vectors' differences will pass the objective functions natural information toward the optimization process, and therefore provide more effective global optimization competence This paper targets at providing an outline of differential evolution and presenting it as an alternative to evolutionary algorithms with comparison between its different methodologies on the benchmark functions.