A Comparative Study of Multiple-Objective Metaheuristics on the Bi-Objective Set Covering Problem and the Pareto Memetic Algorithm

The paper describes a comparative study of multiple-objective metaheuristics on the bi-objective set covering problem. Ten representative methods based on genetic algorithms, multiple start local search, hybrid genetic algorithms and simulated annealing are evaluated in the computational experiment....

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Bibliographic Details
Published in:Annals of operations research Vol. 131; no. 1-4; pp. 135 - 158
Main Author: Jaszkiewicz, Andrzej
Format: Journal Article
Language:English
Published: New York Springer Nature B.V 01.10.2004
Subjects:
Algorithms
Experiments
Genetic algorithms
Heuristic
Operations research
Optimization
Pareto optimum
Studies
ISSN:0254-5330, 1572-9338
Online Access:Get full text
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Summary:The paper describes a comparative study of multiple-objective metaheuristics on the bi-objective set covering problem. Ten representative methods based on genetic algorithms, multiple start local search, hybrid genetic algorithms and simulated annealing are evaluated in the computational experiment. Nine of the methods are well known from the literature. The paper introduces also a new hybrid genetic algorithm called Pareto memetic algorithm. The results of the experiment indicate very good performance of hybrid genetic algorithms, however, no algorithm was able to outperform all other methods on all instances. Furthermore, the results indicate that the performance of multiple-objective metaheuristics may differ radically even if the methods are based on the same single objective algorithm and use exactly the same problem-specific operators.[PUBLICATION ABSTRACT]
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ISSN:0254-5330
1572-9338
DOI:10.1023/B:ANOR.0000039516.50069.5b