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A Hybrid Fine-Tuned Multi-Objective Memetic Algorithm Xiuping GUO Genke YANG Zhiming WU Zhonghua HUANG Publication IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences Vol.E89-A No.3 pp.790-797 Publication Date: 2006/03/01 Online ISSN: 1745-1337 DOI: 10.1093/ietfec/e89-a.3.790 Print ISSN: 0916-8508 Type of Manuscript: PAPER Category: Numerical Analysis and Optimization Keyword: hybrid, fine-tuned, memetic algorithm, multi-objective optimization, multi-objective 0/1 knapsack problem, Full Text: PDF(651KB)>>
Summary: In this paper, we propose a hybrid fine-tuned multi-objective memetic algorithm hybridizing different solution fitness evaluation methods for global exploitation and exploration. To search across all regions in objective space, the algorithm uses a widely diversified set of weights at each generation, and employs a simulated annealing to optimize each utility function. For broader exploration, a grid-based technique is adopted to discover the missing nondominated regions on existing tradeoff surface, and a Pareto-based local perturbation is performed to reproduce incrementing solutions trying to fill up the discontinuous areas. Additional advanced feature is that the procedure is made dynamic and adaptive to the online optimization conditions based on a function of improvement ratio to obtain better stability and convergence of the algorithm. Effectiveness of our approach is shown by applying it to multi-objective 0/1 knapsack problem (MOKP). | ||||||||||
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