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MGDE: a many-objective guided differential evolution with strengthened dominance relation and bi-goal evolution

Author

Listed:
  • Djaafar Zouache

    (University of Mohamed El Bachir El Ibrahimi
    University of Science and Technology Houari Boumediene)

  • Fouad Ben Abdelaziz

    (NEOMA Business School)

Abstract

When solving many-objective optimization problems, evolutionary algorithms do not differentiate between solutions due to the large number of objectives involved, and therefore they do not generate an accurate potential efficient set. The main problem such algorithms encounter is that of the balance between convergence and diversity. To overcome this difficulty, we propose a Guided Differential Evolution method (MGDE) that uses strengthened dominance relation and bi-goal evolution to solve many-objective optimization problems (MaOPs). We also use adapted differential evolutionary operators for the crossover and mutation to guide the search. This guided exploration strategy makes it possible to converge toward the Pareto front with good solution diversity. We use the CEC 2017 Competition problems to test our algorithm and show that it performs better than most recent algorithms.

Suggested Citation

  • Djaafar Zouache & Fouad Ben Abdelaziz, 2025. "MGDE: a many-objective guided differential evolution with strengthened dominance relation and bi-goal evolution," Annals of Operations Research, Springer, vol. 346(2), pages 1929-1966, March.
    • Handle: RePEc:spr:annopr:v:346:y:2025:i:2:d:10.1007_s10479-022-04641-3
    DOI: 10.1007/s10479-022-04641-3
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