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<p>The Snake Optimizer (SO), a meta-heuristic optimization algorithm proposed in recent years, is known for its simplicity and effectiveness. However, SO can underperform in certain cases, exhibiting slow convergence and a tendency to get trapped in local optima. To address these shortcomings, this paper proposes a multi-strategy improved snake optimizer (ISO). During the exploration phase, an adaptive Lévy flight strategy is introduced to enhance the global search ability while minimizing the negative impact on convergence speed. In the exploitation phase, a new food position update mechanism is applied to accelerate the algorithm’s convergence. Additionally, ISO incorporates a dual-lens fusion enhanced opposition-based learning and a population-based survival-of-the-fittest strategy to improve the algorithm’s robustness. Extensive experimental evaluations are conducted to validate the effectiveness of the proposed ISO algorithm. In addition to performing multidimensional experiments on the CEC2017, CEC2020, and CEC2022 benchmark test suites, the ISO is also tested on five classic constrained engineering problems and UAV path planning problem. The experimental results show that the ISO outperforms both the original SO and other state-of-the-art algorithms.</p>

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Multi-strategy improved snake optimizer based on adaptive lévy flight and dual-lens fusion

  • 期刊论文

  • 发表日期：

    2025年2月25日

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  • Guangming Gong,
  • Shengwei Fu,
  • Haisong Huang,
  • Hefan Huang,
  • Xin Luo

摘要

The Snake Optimizer (SO), a meta-heuristic optimization algorithm proposed in recent years, is known for its simplicity and effectiveness. However, SO can underperform in certain cases, exhibiting slow convergence and a tendency to get trapped in local optima. To address these shortcomings, this paper proposes a multi-strategy improved snake optimizer (ISO). During the exploration phase, an adaptive Lévy flight strategy is introduced to enhance the global search ability while minimizing the negative impact on convergence speed. In the exploitation phase, a new food position update mechanism is applied to accelerate the algorithm’s convergence. Additionally, ISO incorporates a dual-lens fusion enhanced opposition-based learning and a population-based survival-of-the-fittest strategy to improve the algorithm’s robustness. Extensive experimental evaluations are conducted to validate the effectiveness of the proposed ISO algorithm. In addition to performing multidimensional experiments on the CEC2017, CEC2020, and CEC2022 benchmark test suites, the ISO is also tested on five classic constrained engineering problems and UAV path planning problem. The experimental results show that the ISO outperforms both the original SO and other state-of-the-art algorithms.