arXiv 2409.092102024-09cs.NE

ORS：一种受橄榄鳞龟生存启发的新型元启发式优化算法

ORS: A novel Olive Ridley Survival inspired Meta-heuristic Optimization Algorithm

Niranjan Panigrahi, Sourav Kumar Bhoi, Debasis Mohapatra, Rashmi Ranjan Sahoo, Kshira Sagar Sahoo, Anil Mohapatra

AI总结：

本研究受橄榄鳞龟幼龟生存规律启发，提出包含环境生存、轨迹影响两阶段的新型元启发式优化算法ORS，经多组基准函数与工程问题验证，多数场景性能优于部分前沿算法。

摘要：

元启发式算法的开发自诞生以来一直是一个热门研究领域。本文提出了一种新型元启发式优化算法——橄榄鳞龟生存算法（Olive Ridley Survival, ORS），其灵感来源于橄榄鳞龟幼龟所面临的生存挑战。关于橄榄鳞龟生存的一个重要事实显示，从巢穴中孵化出的一千只橄榄鳞龟幼龟中，由于各种环境及其他因素，仅有一只能在海中存活，这一事实构成了所提算法的核心基础。该算法包含两个主要阶段：环境因素影响下的幼龟生存阶段，以及运动轨迹对其生存的影响阶段。研究人员对这两个阶段进行了数学建模，并结合合适的输入表示与适应度函数完成了算法实现，同时对算法开展了理论分析。为验证该算法性能，研究人员对来自标准CEC测试套件的14个数学基准函数进行了评估与统计检验；此外，为研究ORS在近年复杂基准函数上的有效性，还对CEC-06-2019的10个基准函数进行了评估。进一步地，研究人员用ORS求解了3个经典工程问题，并与其他前沿元启发式算法进行了对比。仿真结果表明，在多数场景下，所提出的ORS算法性能优于部分前沿元启发式优化算法，同时也观察到ORS在部分近年基准函数上存在次优表现。

英文摘要：

Meta-heuristic algorithmic development has been a thrust area of research since its inception. In this paper, a novel meta-heuristic optimization algorithm, Olive Ridley Survival (ORS), is proposed which is inspired from survival challenges faced by hatchlings of Olive Ridley sea turtle. A major fact about survival of Olive Ridley reveals that out of one thousand Olive Ridley hatchlings which emerge from nest, only one survive at sea due to various environmental and other factors. This fact acts as the backbone for developing the proposed algorithm. The algorithm has two major phases: hatchlings survival through environmental factors and impact of movement trajectory on its survival. The phases are mathematically modelled and implemented along with suitable input representation and fitness function. The algorithm is analysed theoretically. To validate the algorithm, fourteen mathematical benchmark functions from standard CEC test suites are evaluated and statistically tested. Also, to study the efficacy of ORS on recent complex benchmark functions, ten benchmark functions of CEC-06-2019 are evaluated. Further, three well-known engineering problems are solved by ORS and compared with other state-of-the-art meta-heuristics. Simulation results show that in many cases, the proposed ORS algorithm outperforms some state-of-the-art meta-heuristic optimization algorithms. The sub-optimal behavior of ORS in some recent benchmark functions is also observed.

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