Paper Info
Title
A population-based algorithm for solving linear assignment problems with two objectives.
Abstract
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.
Year
DOI
Venue
2017
10.1016/j.cor.2016.07.006
Computers & Operations Research
Keywords
Field
DocType
Multiobjective optimization,Linear assignment problem,Metaheuristic,Heterogeneous functions
Weapon target assignment problem,Population,Heuristic,Mathematical optimization,Crossover,Algorithm,Assignment problem,Local search (optimization),Mathematics,Linear bottleneck assignment problem,Metaheuristic
Journal
Volume
ISSN
Citations 
79
0305-0548
0
PageRank 
References 
Authors
0.34
8
3
Name
Order
Citations
PageRank
Xavier Gandibleux143632.53
Hiroyuki Morita213.42
naoki katoh31101187.43