Abstract
Multi-objective evolutionary algorithms (MOEAs) are commonly applied to treat multi-objective optimization problems (MOPs) due to their global nature, robustness, and reliability. However, it is also well known that MOEAs need quite a few resources to compute a good approximation of the Pareto set/front. Even more, MOEAs may exhibit difficulties when dealing with highly constrained search spaces. A possible remedy is the hybridization of MOEAs with specialized local search operators; if the local search is based on mathematical programming techniques, gradient information is needed, resulting in relatively high computational cost in many problem instances.
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