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Improved Dual-Population Genetic Algorithm: A Straightforward Optimizer Applied to Engineering Optimization
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.
Improved Dual-Population Genetic Algorithm: A Straightforward Optimizer Applied to Engineering Optimization
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.
Improved Dual-Population Genetic Algorithm: A Straightforward Optimizer Applied to Engineering Optimization
Zhihua Chen (author) / Xuchen Xu (author) / Hongbo Liu (author)
2023
Article (Journal)
Electronic Resource
Unknown
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