Local search-based heuristics for the multiobjective multidimensional knapsack problem

• Autor(a) principal: Vianna,Dalessandro Soares
• Data de Publicação: 2013
• Outros Autores: Vianna,Marcilene de Fátima Dianin
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Production
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-65132013000300003
• topic: Multiobjective multidimensional knapsack problem; Multiobjective combinatorial optimization; GRASP; ILS

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.