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A population-based algorithm for solving linear assignment problems with two objectives
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DOI：10.1016/j.cor.2016.07.006.png)
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Abstract
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The paper presents a population-based algorithm for computing approximations of the efficient solution set for the linear assignment problem with two objectives. This is a multiobjective metaheuristic based on the intensive use of three operators a local search, a crossover and a path-relinking performed on a population composed only of elite solutions. The initial population is a set of feasible solutions, where each solution is one optimal assignment for an appropriate weighted sum of two objectives. Genetic information is derived from the elite solutions, providing a useful genetic heritage to be exploited by crossover operators. An upper bound set, defined in the objective space, provides one acceptable limit for performing a local search. Results reported using referenced data sets have shown that the heuristic is able to quickly find a very good approximation of the efficient frontier, even in situation of heterogeneity of objective functions. In addition, this heuristic has two main advantages. It is based on simple easy-to-implement principles, and it does not need a parameter tuning phase. (C) 2017 Published by Elsevier Ltd.
Keywords:
Multiobjective optimization
Linear assignment problem
Metaheuristic
Heterogeneous functions
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