Abstract
Differential Evolution (DE) is a relatively new EA and it has become a major branch of EA. Based on the classical Differential Evolution, we proposed a new Multi-Objective Differential Evolutionary Algorithm in this paper. Compared with NSGA-II and ε-MOEA, the experimental results demonstrate that the new algorithm tends to be more effective in obtaining good convergence and can converge to the true Pareto front with comparable efficiency.
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Zheng, J., Wu, J., Lv, H. (2008). A Multi-objective Differential Evolutionary Algorithm Based on Spacial Distance. In: Kang, L., Cai, Z., Yan, X., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2008. Lecture Notes in Computer Science, vol 5370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92137-0_17
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DOI: https://doi.org/10.1007/978-3-540-92137-0_17
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