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MOTGA: A multiobjective Tchebycheff based genetic algorithm for the multidimensional knapsack problem
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DOI：10.1016/j.cor.2006.02.008.png)
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This paper presents a new multiobjective genetic algorithm based on the Tchebycheff scalarizing function, which aims to generate a good approximation of the nondominated solution set of the multiobjective problem. The algorithm performs several stages, each one intended for searching potentially nondominated solutions in a different part of the Pareto front. Pre-defined weight vectors act as pivots to define the weighted-Tchebycheff scalarizing functions used in each stage. Therefore, each stage focuses the search on a specific region, leading to an iterative approximation of the entire nondominated set. This algorithm, called MOTGA (Multiple objective Tchebycheff based Genetic Algorithm) has been designed to the multiobjective multidimensional 0/1 knapsack problem, for which a dedicated routine to repair infeasible solutions was implemented. Computational results are presented and compared with the outcomes of other evolutionary algorithms. (C) 2006 Elsevier Ltd. All rights reserved.
Keyword:
genetic algorithms
multiple objective programming
knapsack problem
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4.3
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