A Fast and Adaptive Local Search Algorithm for Multi-Objective Optimization
نویسنده
چکیده
Although population-based algorithms are robust in solving Multi-objective Optimization Problems (MOP), they often require a large number of function evaluations. In contrast, individual-solution based algorithms are fast but can be stuck in local minima. To solve these problems, we introduce a fast and adaptive local search algorithm for MOP. Our algorithm is an individual-solution algorithm with a flexible mechanism for switching between the exploration and exploitation phase to escape from local minima. The experimental results on the DTLZ benchmark show that our algorithm significantly outperforms the popular evolutionary algorithm NSGAII and three other simulated annealing algorithms for MOP.
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