A robust memory based hybrid differential evolution for continuous optimization problem

A number of efficient variants of differential evolution (DE) and its hybrid have been suggested in recent years to deal with continuous optimization problems. However, recent past studies have indicated that the performance of such algorithms is largely affected by the choice of parameters e.g. mutation factor, crossover rate, mutation strategy and the type of crossover. A combination of these parameters may work out to be the best for a problem while resulting in poor performance for others. In general practice, during simulation DE does not employ any strategy of memorizing the so-far-best results obtained in the initial part of the previous generation. In this paper, a hybrid DE based on use of memory concept under the particle swarm optimization (PSO), called memory based DE (MBDE), is presented for the continuous optimization problems. The algorithm employs two newly operators namely swarm mutation and swarm crossover. These operators are properly balance exploration and exploitation and improving the convergence rate of the proposed algorithm. Experiments are conducted on a comprehensive set of benchmark functions and real life problems. The results of proposed MBDE are compared with state-of-the-art algorithms. Numerical, statistical and graphical analysis reveals the competency of the proposed MBDE.