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In this paper, our recently developed Self-adaptive Differential Evolution algorithm (SaDE) is extended to solve numerical optimization problems with multiple conflicting objectives. The performance of the proposed MOSaDE algorithm is evaluated on a suit of 19 benchmark problems provided for the CEC2007 special session (http://www.ntu.edu.sg/home/epnsugan/) on Performance Assessment of Multi-Objective Optimization Algorithms.
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Huang et al. (2007) studied this question.