Hybrid Differential Evolution Algorithm for Solving Combinatorial Optimization Problems

In order to improve the ability of evolution algorithm to solve the complicated combinatorial optimization problems of massive deceptive problems, this paper proposes an improved algorithm which introduces simulated annealing operator to differential evolution algorithm. It aims to enhance the population multiplicity by using the simulated annealing operators´ mutation search, and to improve the differential evolution algorithm´s optimization ability. In the experiments, various deceptive problems are used to evaluate the performance of algorithm, and the simulation results show that this algorithm has better global convergence ability.