Computer Science and Information Engineering, World Congress on (2009)
Los Angeles, California USA
Mar. 31, 2009 to Apr. 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.181
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
H. Lv, K. Li, C. Zhou and J. Zheng, "An Improved Differential Evolution for Multi-objective Optimization," 2009 WRI World Congress on Computer Science and Information Engineering, CSIE(CSIE), Los Angeles, CA, 2009, pp. 825-830.