A hybrid differential evolutionary algorithm based on the simulated annealing operation
Yang Yanxi · Caai Transactions on Intelligent Systems · 2014
In order to improve the ability of the evolutionary algorithm for solving such complicated combination and optimization problems as the massive deceptive problems and hierarchical problems,this paper proposes an improved algorithm,which introduces the simulated annealing operation into the differential evolutionary algorithm. Using this method,the simulated annealing operation is carried out for a randomly generated initial individual and the temperature-reducing operation is carried out for a new individual. After several times of iterations,the optimal solution in the population is taken as the solution to the question. By utilizing the mutation search of the simulated annealing operator to improve the diversity of the population,the differential evolutionary algorithm can better utilize colony differences for an overall search. In the experiment,various types of deceptive functions and the hierarchical functions with a tree-shape structure are applied to simulation testing of the algorithm. In the initial stage,the algorithm keeps diversity of the population; in the later stage,a local optimal solution may be generated,the convergence scope nears to the overall optimal solution. The simulation results show that the algorithm has advantage for the aspect of searching the overall optimal solution.