Evolutionary Algorithms (EAs), in their traditional form or in combination as in Memetic Algorithms (MAs), have been quite successful in solving a variety of optimization problems in the past. More recently, several excellent Differential Evolution (DE) based algorithms have been proposed which have had outstanding success in IEEE Congress on Evolutionary Computation (CEC) competition problems. Inspired by previous studies, we propose an algorithm combining the strengths of EA, DE and MA in this paper. The algorithm utilizes a population of random solutions to start with and generates a child population either through EA or DE based evolution with equal probability. Local search is then performed from one of the solutions in the population for further improvement objective value. To avoid stagnation, re-initialization of the population is performed whenever the local search is unable to improve the values consecutively for a prescribed number of generations. The performance of the proposed algorithm is presented in this paper for the newly introduced real world optimization problems for CEC 2011 competition.
['Hemant Kumar Singh', 'Tapabrata Ray']
Performance of a hybrid EA-DE-memetic algorithm on CEC 2011 real world optimization problems