A modified differential evolution algorithm for unconstrained optimization problems

A modified differential evolution algorithm (MDE) is proposed to solve unconstrained optimization problems in this paper. Gauss distribution and uniform distribution have one thing in common, that is randomness or indeterminateness. Due to this characteristic, MDE employs both distributions to adjust scale factor and crossover rate, which is useful to increase the diversity of the entire population. To guarantee the quality of the swarm, MDE uses an external archive, and some solutions of high quality in this external archive can be selected for candidate solutions. MDE adopts two common mutation strategies to produce new solutions, and the information of global best solution is more likely to be utilized for the mutation during late evolution process, which is beneficial to improving the convergence of the proposed algorithm. In addition, a central solution is generated in terms of all the other candidate solutions, and it can provide a potential searing direction. Experimental results show that MDE algorithm can yield better objective function values than the other six DE algorithms for some unconstrained optimization problems, thus it is an efficient alternative on solving unconstrained optimization problems.