Abstract
A novel memetic algorithm for multi-objective optimization problems is proposed in this paper. The uniqueness of the method is that it hybridizes scalarizing selection with Pareto selection for exploitation and exploration. For extending the spread of solutions as quickly and fully as possible, the scalarizing functions defined by a wide diversified set of weights are used to go through all regions in objective space in the first phase at each generation. In the second phase, for intensifying search ability and achieving global exploration, a grid-based method is used to discover the gaps on existing tradeoff surface, and a fuzzy local perturbation is employed to reproduce additional ”good” individuals in the missing areas. Both the exploitation and exploration are made dynamic and adaptive to online optimization conditions based on a function of progress ratio, ensuring better stability of the algorithm. Compared with several state-of-the-art approaches using the same set of multi-objective 0/1 knapsack problem instances, experiment results show that the proposed method perform better to some extent in terms of finding a near-Pareto front and well-extended nondominated set.
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Guo, X., Yang, G., Wu, Z. (2005). A Hybrid Self-adjusted Memetic Algorithm for Multi-objective Optimization. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds) MICAI 2005: Advances in Artificial Intelligence. MICAI 2005. Lecture Notes in Computer Science(), vol 3789. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11579427_67
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DOI: https://doi.org/10.1007/11579427_67
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