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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...
It is of great importance for a solution with high robustness in the real application, not only with good quality. Searching for robust Pareto optimal solutions for multi-objective optimization problems (MOPs) is a challenge, no exception for multi-objective evolutionary algorithms (MOEAs). Recently, as one of the popular approach to search robust Pareto optimal solutions, ldquoeffective objective...
Searching robust solutions in multi-objective evolutionary algorithms (MOEAs) usually optimize effective objective functions instead of original objective functions. It needs sampling in a delta-neighborhood of an individual. Fitness of every sample must be evaluated, so large computation of MOEAs becomes an urgent problem. To solve the flaw of low efficiency resulted from fixed sample size, this...
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