OPTIMIZATION PROBLEMS SOLVED BY DIFFERENTIAL EVOLUTION ALGORITHM

Abstract

In recent years differential evolution is most important method for solving computer science and mathematical optimization problem. This type of optimization problems generally solved by metaheuristic procedure. Metaheuristic is a higher level procedure to find, generate or select heuristic that may provide sufficiently good solution to an optimization problem. Metaheuristic procedure is used to
obtain attractive result in solving much higher degree equation and engineering optimization problems. Differential evolution has two major problems. First, its performance is significantly influenced by the control parameters, which are problem dependant and which vary in different regions of space exploration. Secondly, it can easily get stuck in a local optimum or fail to generate better solutions before the population has converged. This research paper aims to develop new differential evolution algorithms to address the two mentioned problems. The methods proposed in this research paper have been tested in different benchmark problems. All the result of this research paper is compare with the state of the art algorithms and other competitive result.