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Improved Dual-Population Genetic Algorithm: A Straightforward Optimizer Applied to Engineering Optimization

Author

Listed:
  • Zhihua Chen

    (State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300072, China
    Department of Civil Engineering, Tianjin University, Tianjin 300072, China)

  • Xuchen Xu

    (Department of Civil Engineering, Tianjin University, Tianjin 300072, China)

  • Hongbo Liu

    (State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300072, China
    Department of Civil Engineering, Hebei University of Engineering, Handan 056038, China)

Abstract

Aiming at the current limitations of the dual-population genetic algorithm, an improved dual-population genetic algorithm (IDPGA) for solving multi-constrained optimization problems is proposed by introducing a series of strategies, such as remaining elite individuals, a dynamic immigration operator, separating the objective and constraints, normalized constraints, etc. We selected 14 standard mathematical benchmarks to check the performance of IDPGA, and the results were compared with the theoretical value of CEC 2006. The results show that IDPGA with the current parameters obtains good solutions for most problems. Then 6 well-known engineering optimization problems were solved and compared with other algorithms. The results show that all of the solutions are feasible, the solution precision of IDPGA is better than other algorithms, and IDPGA performs with good efficiency and robustness. Meanwhile, no parameters need to be ignored when IDPGA is applied to solving engineering problems, which is enough to prove that IDPGA is suitable for solving engineering optimization. A Friedman test showed no significant difference between IDPGA and six algorithms, but significant differences between IDPGA and seven other algorithms; thus, a larger number of evaluators will be needed in the future. In addition, further research is still needed about the performance of IDPGA for solving practical large-scale engineering problems.

Suggested Citation

  • Zhihua Chen & Xuchen Xu & Hongbo Liu, 2023. "Improved Dual-Population Genetic Algorithm: A Straightforward Optimizer Applied to Engineering Optimization," Sustainability, MDPI, vol. 15(20), pages 1-32, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:20:p:14821-:d:1258698
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    References listed on IDEAS

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    1. Atidel Ben Hadj-Alouane & James C. Bean, 1997. "A Genetic Algorithm for the Multiple-Choice Integer Program," Operations Research, INFORMS, vol. 45(1), pages 92-101, February.
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