Zhao Youdao, Li Xiangdong
Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650500, China.
Biomimetics (Basel). 2025 Aug 12;10(8):529. doi: 10.3390/biomimetics10080529.
Global optimization problems, ubiquitous scientific research, and engineering applications necessitate sophisticated algorithms adept at navigating intricate, high-dimensional search landscapes. The Escape (ESC) algorithm, inspired by the complex dynamics of crowd evacuation behavior-where individuals exhibit calm, herding, or panic responses-offers a compelling nature-inspired paradigm for addressing these challenges. While ESC demonstrates a strong intrinsic balance between exploration and exploitation, opportunities exist to enhance its inter-agent communication and search trajectory diversification. This paper introduces an advanced bio-inspired algorithm, termed Crisscross Escape Algorithm (CCESC), which strategically incorporates a Crisscross (CC) information exchange mechanism. This CC strategy, by promoting multi-directional interaction and information sharing among individuals irrespective of their behavioral group (calm, herding, panic), fosters a richer exploration of the solution space, helps to circumvent local optima, and accelerates convergence towards superior solutions. The CCESC's performance is extensively validated on the demanding CEC2017 benchmark suites, alongside several standard engineering design problems, and compared against a comprehensive set of prominent metaheuristic algorithms. Experimental results consistently reveal CCESC's superior or highly competitive performance across a wide array of benchmark functions. Furthermore, CCESC is effectively applied to a complex reservoir production optimization problem, demonstrating its capacity to achieve significantly improved Net Present Value (NPV) over other established methods. This successful application underscores CCESC's robustness and efficacy as a powerful optimization tool for tackling multifaceted real-world problems, particularly in reservoir production optimization within complex sedimentary environments.
全局优化问题在科学研究和工程应用中普遍存在，这就需要复杂的算法来应对复杂的高维搜索空间。逃逸（ESC）算法受人群疏散行为的复杂动态启发，其中个体表现出冷静、聚集或恐慌反应，为应对这些挑战提供了一种引人注目的受自然启发的范式。虽然ESC在探索和利用之间表现出很强的内在平衡，但仍有机会加强其智能体间的通信和搜索轨迹的多样化。本文介绍了一种先进的受生物启发的算法，称为交叉逃逸算法（CCESC），该算法策略性地引入了交叉（CC）信息交换机制。这种CC策略通过促进个体之间的多向交互和信息共享，而不考虑其行为群体（冷静、聚集、恐慌），促进了对解空间更丰富的探索，有助于规避局部最优，并加速向最优解的收敛。CCESC的性能在具有挑战性的CEC2017基准测试套件以及几个标准工程设计问题上得到了广泛验证，并与一系列著名的元启发式算法进行了比较。实验结果一致显示，CCESC在广泛的基准函数上具有卓越或极具竞争力的性能。此外，CCESC有效地应用于一个复杂的油藏生产优化问题，表明它能够比其他既定方法显著提高净现值（NPV）。这一成功应用强调了CCESC作为一种强大的优化工具来解决多方面现实世界问题的稳健性和有效性，特别是在复杂沉积环境中的油藏生产优化方面。
