A Favorable Weight-Based Evolutionary Algorithm for Multiple Criteria Problems

Date

2010-04-01

Author

SOYLU, Banu
Köksalan, Mustafa Murat

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In this paper, we present a favorable weight-based evolutionary algorithm for multiple criteria problems. The algorithm tries to both approximate the Pareto frontier and evenly distribute the solutions over the frontier. These two goals are common for many multiobjective evolutionary algorithms. To achieve these goals in our algorithm, each member selects its own weights for a weighted Tchebycheff distance function to define its fitness score. The fitness scores favor solutions that are closer to the Pareto frontier and that are located at underrepresented regions. We compare the performance of the algorithm with two leading evolutionary algorithms on various continuous test problems having different number of criteria.

Subject Keywords

Theoretical Computer Science, Computational Theory and Mathematics, Software

URI

https://hdl.handle.net/11511/57962

Journal

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION

DOI

https://doi.org/10.1109/tevc.2009.2027357

Collections

Department of Industrial Engineering, Article

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Citation Formats

B. SOYLU and M. M. Köksalan, “A Favorable Weight-Based Evolutionary Algorithm for Multiple Criteria Problems,” IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, pp. 191–205, 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/57962.