Article ID: | iaor20084150 |
Country: | United Kingdom |
Volume: | 34 |
Issue: | 11 |
Start Page Number: | 3458 |
End Page Number: | 3470 |
Publication Date: | Nov 2007 |
Journal: | Computers and Operations Research |
Authors: | Alves Maria Joo, Almeida Marla |
Keywords: | heuristics: genetic algorithms, programming: integer |
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.