Multi-objective Differential Evolution Algorithm based on Adaptive Mutation and Partition Selection

A multi-objective differential evolution algorithm based on adaptive mutation strategies and partition selected search is proposed based on classical differential evolution (DE) to further improve the convergence and diversity of multi-objective optimization problems. This algorithm improves mutation operation in DE, makes search oriented and ensures the convergence of algorithm by adaptively selecting mutation strategies based on the non-inferiority of the individuals of the population in evolution. In addition, a partition-based elitist preserving mechanism is applied to select the best individuals for the next generation, thus improving the selection operation in DE and maintaining the diversity of Pareto optimal set. The experiment on 5 ZDT test functions and 3 DTLZ test functions and comparison with and analysis of other classical algorithms such as NSGA-II and SPEA2 show that this algorithm converges the populations towards non-inferior frontier rapidly on the premise of maintaining the diversity of the populations. From the measure and graphs, it can be seen that this algorithm is feasible and effective in solving the multi-objective optimization problems.