A hybrid adaptive multi-objective memetic algorithm for 0/1 knapsack problem

Authors:
XiuPing Guo;ZhiMing Wu;GenKe Yang
Affiliations:
Department of Automation, Shanghai Jiaotong University, Shanghai, Shanghai, P.R. China;Department of Automation, Shanghai Jiaotong University, Shanghai, Shanghai, P.R. China;Department of Automation, Shanghai Jiaotong University, Shanghai, Shanghai, P.R. China
Venue:
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Year:
2005

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Abstract

A hybrid adaptive memetic algorithm for a multi-objective combinatorial optimization problem is proposed in this paper. Different solution fitness evaluation methods are hybridized to achieve global exploitation and exploration. At each generation, a wide diversified set of weights are used to search across all regions in objective space, and each weighted linear utility function is optimized with a simulated annealing. For a broader exploration, a grid-based technique is employed to discover the missing nondominated regions on existing tradeoff surface, and a Pareto-based local perturbation is used to reproduce additional good individuals trying to fill up the discontinuous areas. For better stability and convergence of the algorithm, the procedure is made dynamic and adaptive to online optimization conditions based upon a function of improvement ratio. Experiment results show the effectiveness of the proposed method on multi-objective 0/1 knapsack problems.