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DOPGA: a new fitness assignment scheme for multi-objective evolutionary algorithms

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  • Engin Ergul
  • Ilyas Eminoglu

Abstract

In this article, a new fitness assignment scheme to evaluate the Pareto-optimal solutions for multi-objective evolutionary algorithms is proposed. The proposed DOmination Power of an individual Genetic Algorithm (DOPGA) method can order the individuals in a form in which each individual (the so-called solution) could have a unique rank. With this new method, a multi-objective problem can be treated as if it were a single-objective problem without drastically deviating from the Pareto definition. In DOPGA, relative position of a solution is embedded into the fitness assignment procedures. We compare the performance of the algorithm with two benchmark evolutionary algorithms (Strength Pareto Evolutionary Algorithm (SPEA) and Strength Pareto Evolutionary Algorithm 2 (SPEA2)) on 12 unconstrained bi-objective and one tri-objective test problems. DOPGA significantly outperforms SPEA on all test problems. DOPGA performs better than SPEA2 in terms of convergence metric on all test problems. Also, Pareto-optimal solutions found by DOPGA spread better than SPEA2 on eight of 13 test problems.

Suggested Citation

  • Engin Ergul & Ilyas Eminoglu, 2014. "DOPGA: a new fitness assignment scheme for multi-objective evolutionary algorithms," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(3), pages 407-426.
    • Handle: RePEc:taf:tsysxx:v:45:y:2014:i:3:p:407-426
    DOI: 10.1080/00207721.2012.724095
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    Cited by:

    1. Lizhong Zhao & Chen-Fu Chien & Mitsuo Gen, 2018. "A bi-objective genetic algorithm for intelligent rehabilitation scheduling considering therapy precedence constraints," Journal of Intelligent Manufacturing, Springer, vol. 29(5), pages 973-988, June.
    2. Fei He & Kang Shen & Li Guan & Mingming Jiang, 2017. "Research on Energy-Saving Scheduling of a Forging Stock Charging Furnace Based on an Improved SPEA2 Algorithm," Sustainability, MDPI, vol. 9(11), pages 1-21, November.

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