模仿蛇行进方式，设计新优化算法，性能优于多数现有方法。
Snake locomotion learning search
- 借鉴蛇的扭动和履带式运动，设计两种搜索机制。
- 在60个标准测试题和7个工程问题中表现优异。
- 适合解决复杂优化问题，尤其擅长平衡探索与利用。
本研究提出一种名为蛇形运动学习搜索算法（SLLS）的新启发式算法，用于求解优化问题。该算法灵感来源于蛇的运动模式，特别是蜿蜒型和履带型运动。基于这两种运动方式，SLLS设计了两种不同的搜索机制。为模拟蛇对环境的自然适应能力，引入由Sigmoid函数生成的学习效率组件，以在计算过程中动态平衡探索与利用。通过在60个标准基准优化问题和7个经典工程优化问题上的应用，验证了该算法的有效性与高效性。性能分析表明，在多数情况下SLLS优于其他算法，即使在少数情形下也表现出稳健性能。这符合“无免费午餐定理”，证实SLLS是一种具有重要潜力的启发式算法，适用于特定优化挑战。
原文摘要 · Abstract (English)
This research introduces a novel heuristic algorithm known as the Snake Locomotion Learning Search algorithm (SLLS) designed to address optimization problems. The SLLS draws inspiration from the locomotion patterns observed in snakes, particularly serpentine and caterpillar locomotion. We leverage these two modes of snake locomotion to devise two distinct search mechanisms within the SLLS. In our quest to mimic a snake's natural adaptation to its surroundings, we incorporate a learning efficiency component generated from the Sigmoid function. This helps strike a balance between exploration and exploitation capabilities throughout the SLLS computation process. The efficacy and effectiveness of this innovative algorithm are demonstrated through its application to 60 standard benchmark optimization problems and seven well-known engineering optimization problems. The performance analysis reveals that in most cases, the SLLS outperforms other algorithms, and even in the remaining scenarios, it exhibits robust performance. This conforms to the No Free Lunch Theorem, affirming that the SLLS stands as a valuable heuristic algorithm with significant potential for effectively addressing specific optimization challenges.
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