A hybrid differential evolution for multi-objective optimisation problems

Abstract

In order to effectively use differential evolution (DE) to solve multi-objective optimisation problems, it is necessary to consider how to ensure the search ability of DE. However, the search ability of DE is affected by related parameters and mutation mode. Based on decomposition, this paper proposed a hybrid differential evolution (HMODE/D) for solving multi-objective optimisation problems. First, when generation satisfies a certain condition, the local optimum is selected using the information of neighbour individual objective values to produce mutation offspring. Then, the heuristic crossover operator is established by using a uniform design method to produce better crossover individuals. Next, an external archive is set for each individual to store the individuals beneficial to the optimisation objective functions. Then, the individual is selected from the external archive to generate mutation offspring. In addition, considering that the performance of DE is determined by parameters, using the relevant information of the objective space function value, the self-adaptive adjustment strategy is adopted for the relevant parameter. Finally, a series of test functions with 5-, 10-, and 15-objectives are performed in the experiments to evaluate the superiority of HMODE/D. The results show that HMODE/D can solve the multi-objective optimisation problem very well.