A genetic algorithm for a bi-objective capacitated arc routing problem
Computers and Operations Research
An effective hybrid DE-based algorithm for multi-objective flow shop scheduling with limited buffers
Computers and Operations Research
A multi-objective scatter search for a dynamic cell formation problem
Computers and Operations Research
Interactive Multiobjective Evolutionary Algorithms
Multiobjective Optimization
A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Identification of Full and Partial Class Relevant Genes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
HCS: a new local search strategy for memetic multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A hybrid evolutionary metaheuristics (HEMH) applied on 0/1 multiobjective knapsack problems
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A hybrid quantum-inspired genetic algorithm for multi-objective scheduling
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
A multi-objective memetic algorithm for intelligent feature extraction
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A hybrid evolutionary approach with search strategy adaptation for mutiobjective optimization
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Comprehensive Survey of the Hybrid Evolutionary Algorithms
International Journal of Applied Evolutionary Computation
Variable and large neighborhood search to solve the multiobjective set covering problem
Journal of Heuristics
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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.