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.

翻译：元启发式算法自诞生以来一直是研究的热点领域。本文提出了一种新颖的元启发式优化算法——榄蠵龟生存算法，其灵感来源于榄蠵龟幼体面临的生存挑战。关于榄蠵龟生存的一个重要事实是：从巢穴中孵化出的一千只榄蠵龟幼体中，由于各种环境及其他因素，最终仅有一只能在海洋中存活。这一事实构成了所提算法开发的核心基础。该算法包含两个主要阶段：幼体在环境因素下的生存过程，以及其运动轨迹对生存的影响。这两个阶段均通过数学模型进行建模与实现，并配备了合适的输入表示和适应度函数。本文对算法进行了理论分析。为验证算法性能，对来自标准CEC测试集的十四个数学基准函数进行了评估与统计检验。同时，为研究ORS在近期复杂基准函数上的效能，对CEC-06-2019的十个基准函数进行了评估。此外，ORS被应用于求解三个经典工程问题，并与其它先进元启发式算法进行了比较。仿真结果表明，在许多案例中，所提出的ORS算法性能优于部分先进的元启发式优化算法。同时，也观察到ORS在部分近期基准函数中存在的次优特性。