An interactive evolutionary metaheuristic for multiobjective combinatorial optimization

Date

2003-12-01

Author

Phelps, S
Köksalan, Mustafa Murat

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We propose an evolutionary metaheuristic for multiobjective combinatorial optimization problems that interacts with the decision maker (DM) to guide the search effort toward his or her preferred solutions. Solutions are presented to the DM, whose pairwise comparisons are then used to estimate the desirability or fitness of newly generated solutions. The evolutionary algorithm comprising the skeleton of the metaheuristic makes use of selection strategies specifically designed to address the multiobjective nature of the problem. Interactions With the DM are triggered by a probabilistic evaluation of estimated fitnesses, while memory structures with indifference thresholds restrict the presentation of solutions resembling those that have already been rejected. The algorithm has been tested on a number of random instances of the Multiobjective Knapsack Problem (MOKP) and the Multiobjective Spanning Tree Problem (MOST). Simulation results indicate that the algorithm requires only a small number of comparisons to be made for satisfactory solutions to be found.

Subject Keywords

Evolutionary algorithm, Metaheuristic, Multiobjective combinatorial optimization;

URI

https://hdl.handle.net/11511/57159

Journal

MANAGEMENT SCIENCE

DOI

https://doi.org/10.1287/mnsc.49.12.1726.25117

Collections

Department of Industrial Engineering, Article

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Citation Formats

S. Phelps and M. M. Köksalan, “An interactive evolutionary metaheuristic for multiobjective combinatorial optimization,” MANAGEMENT SCIENCE, pp. 1726–1738, 2003, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/57159.