An Improved Differential Evolution for Multi-objective Optimization

Evolutionary algorithms (EAs) are well-known optimization approaches to deal with nonlinear and complex problems. However, these population-based algorithms are computationally expensive due to the slow nature of the evolutionary process. This paper proposes an improved differential evolution algorithm (CDE). On the one hand CDE combines the advantages of DE with the mechanisms of Pareto based ranking and crowding distance sorting which are similar to the NSGA-II, on the other hand different from the previous DE, CDE compares the trial vector to its nearest neighbor to decide whether to preserve it. Experimental results confirm that CDE outperforms the other two classical multi-objective evolutionary algorithms (MOEAs) NSGA-II and SPEA2 in terms of diversity and convergence.