Local search-based heuristics for the multiobjective multidimensional knapsack problem

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Dalessandro Soares Vianna²  Marcilene De Fátima Dianin Vianna¹ 

[1] ,Universidade Federal Fluminense,Brazil

关键词: Multiobjective multidimensional knapsack problem;    Multiobjective combinatorial optimization;    GRASP;    ILS;   

DOI  :  10.1590/S0103-65132012005000081

来源: SciELO

【 摘 要 】

In real optimization problems it is generally desirable to optimize more than one performance criterion (or objective) at the same time. The goal of the multiobjective combinatorial optimization (MOCO) is to optimize simultaneously r > 1 objectives. As in the single-objective case, the use of heuristic/metaheuristic techniques seems to be the most promising approach to MOCO problems because of their efficiency, generality and relative simplicity of implementation. In this work, we develop algorithms based on Greedy Randomized Adaptive Search Procedure (GRASP) and Iterated Local Search (ILS) metaheuristics for the multiobjective knapsack problem. Computational experiments on benchmark instances show that the proposed algorithms are very robust and outperform other heuristics in terms of solution quality and running times.

【 授权许可】

CC BY   
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