Do multiple-objective metaheuristics deliver on their promises? A computational experiment on the set-covering problem

In this paper, we compare the computational efficiency of three state-of-the-art multiobjective metaheuristics (MOMHs) and their single-objective counterparts on the multiple-objective set-covering problem (MOSCP). We use a methodology that allows consistent evaluation of the quality of approximatel...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 7; no. 2; pp. 133 - 143
Main Author: Jaszkiewicz, A.
Format: Journal Article
Language:English
Published: New York IEEE 01.04.2003
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Computation
Computational efficiency
Computational modeling
Evolutionary algorithms
Evolutionary computation
Experiments
Genetic algorithms
Mathematical analysis
Mathematical models
Pareto optimality
Simulated annealing
Sorting
State of the art
Studies
Vectors (mathematics)
Warranties
ISSN:1089-778X, 1941-0026
Online Access:Get full text
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Summary:In this paper, we compare the computational efficiency of three state-of-the-art multiobjective metaheuristics (MOMHs) and their single-objective counterparts on the multiple-objective set-covering problem (MOSCP). We use a methodology that allows consistent evaluation of the quality of approximately Pareto-optimal solutions generated by of both MOMHs and single-objective metaheuristics (SOMHs). Specifically, we use the average value of the scalarization functions over a representative sample of weight vectors. Then, we compare computational efforts needed to generate solutions of approximately the same quality by the two kinds of methods. In the computational experiment, we use two SOHMs - the evolutionary algorithm (EA) and memetic algorithm (MA), and three MOMH-controlled elitist nondominated sorting genetic algorithm, the strength Pareto EA, and the Pareto MA. The methods are compared on instances of the MOSCP with 2, 3, and 4 objectives, 20, 40, 80 and 200 rows, and 200, 400, 800 and 1000 columns. The results of the experiment indicate good computational efficiency of the multiple-objective metaheuristics in comparison to their single-objective counterparts.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2003.810759