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<p>Inspired by the unique survival strategies of lungfish, this paper proposes a novel meta-heuristic algorithm named the Lungfish Optimization Algorithm (LFOA). LFOA emulates the distinct three-phase behaviors exhibited by lungfish: active foraging during the rainy season, group collaboration in the breeding season, and dormancy throughout the drought period. Correspondingly, LFOA incorporates a dynamic search strategy comprising Global Exploration to simulate long-distance foraging behavior for extensive search, Information Sharing to update the optimal solution region through collaborative group dynamics, and Local Exploitation to simulate dormancy by introducing an adaptive step-size contraction mechanism and enabling focused local search. This biologically grounded and structurally balanced design enables LFOA to achieve outstanding performance. In comprehensive evaluations across the CEC2017, CEC2020, and CEC2022 benchmark suites, LFOA consistently attained the best average Friedman rank among ten state-of-the-art metaheuristics in each suite, demonstrating superior overall performance. Specifically, it achieved average ranks of 2.52, 2.38, and 2.62, respectively. This leading performance is attributed to its remarkable stability, evidenced by securing top-three rankings in over 94.12% of all test functions. Furthermore, empirical results on three real-world engineering design problems further confirm that LFOA is significantly superior to the leading competing algorithms in solving complex high-dimensional problems.</p>

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Lungfish optimizer: a novel metaheuristic algorithm for global optimization and engineering design problems

  • 期刊论文

  • 发表日期：

    2026年2月17日

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  • Meifeng Shi,
  • Hongqiu Liu,
  • Hongwei Zhang

摘要

Inspired by the unique survival strategies of lungfish, this paper proposes a novel meta-heuristic algorithm named the Lungfish Optimization Algorithm (LFOA). LFOA emulates the distinct three-phase behaviors exhibited by lungfish: active foraging during the rainy season, group collaboration in the breeding season, and dormancy throughout the drought period. Correspondingly, LFOA incorporates a dynamic search strategy comprising Global Exploration to simulate long-distance foraging behavior for extensive search, Information Sharing to update the optimal solution region through collaborative group dynamics, and Local Exploitation to simulate dormancy by introducing an adaptive step-size contraction mechanism and enabling focused local search. This biologically grounded and structurally balanced design enables LFOA to achieve outstanding performance. In comprehensive evaluations across the CEC2017, CEC2020, and CEC2022 benchmark suites, LFOA consistently attained the best average Friedman rank among ten state-of-the-art metaheuristics in each suite, demonstrating superior overall performance. Specifically, it achieved average ranks of 2.52, 2.38, and 2.62, respectively. This leading performance is attributed to its remarkable stability, evidenced by securing top-three rankings in over 94.12% of all test functions. Furthermore, empirical results on three real-world engineering design problems further confirm that LFOA is significantly superior to the leading competing algorithms in solving complex high-dimensional problems.