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<p>In this study, a population quality improvement (PQI) framework to enhance the performance of the recently emerged nature-inspired metaheuristic algorithm, named golden jackal optimization (GJO) algorithm, for solving real-world engineering optimization problems is proposed. Firstly, the maximin LHS strategy is employed to generate the initial population, which can efficiently enhance the search space in the early stages. Then a non-linear population reduction mechanism is applied to screen out the individuals with unsatisfying solutions, and it can also reduce the computational complexity. Meanwhile, a novel circular mutation strategy is introduced, which can dynamically adjust the algorithm to explore in a wilder space or to exploit deeper, this method can not only accelerate the convergence of the optimization, but also reduce the risk of trapping in the local optima. Moreover, the proposed framework shows tremendous potential in application to other population-based searching algorithms, due to that the algorithms are improved, while remaining the inherent mathematical structure and searching strategy. The experimental results demonstrate that the proposed framework can efficiently improve the performance of GJO on solving real-world engineering optimization problems, and the proposed PQI–GJO algorithm can save 196.7001 $/h, 0.3595 MW and 0.0552 p.u. in comparison with the state-of-art algorithm JAYA in solving OPF problems for IEEE-57 bus system.</p>

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Golden jackal optimization algorithm with a population quality improvement framework for real-world engineering optimization problems

  • 期刊论文

  • 发表日期：

    2025年4月6日

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  • Rui Xue,
  • Kefeng Deng

摘要

In this study, a population quality improvement (PQI) framework to enhance the performance of the recently emerged nature-inspired metaheuristic algorithm, named golden jackal optimization (GJO) algorithm, for solving real-world engineering optimization problems is proposed. Firstly, the maximin LHS strategy is employed to generate the initial population, which can efficiently enhance the search space in the early stages. Then a non-linear population reduction mechanism is applied to screen out the individuals with unsatisfying solutions, and it can also reduce the computational complexity. Meanwhile, a novel circular mutation strategy is introduced, which can dynamically adjust the algorithm to explore in a wilder space or to exploit deeper, this method can not only accelerate the convergence of the optimization, but also reduce the risk of trapping in the local optima. Moreover, the proposed framework shows tremendous potential in application to other population-based searching algorithms, due to that the algorithms are improved, while remaining the inherent mathematical structure and searching strategy. The experimental results demonstrate that the proposed framework can efficiently improve the performance of GJO on solving real-world engineering optimization problems, and the proposed PQI–GJO algorithm can save 196.7001 $/h, 0.3595 MW and 0.0552 p.u. in comparison with the state-of-art algorithm JAYA in solving OPF problems for IEEE-57 bus system.