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

• URL: http://arxiv.org/abs/2409.09210v2
• Date: Sat, 2 Nov 2024 08:10:43 GMT
• Title: ORS: A novel Olive Ridley Survival inspired Meta-heuristic Optimization Algorithm
• Authors: Niranjan Panigrahi, Sourav Kumar Bhoi, Debasis Mohapatra, Rashmi Ranjan Sahoo, Kshira Sagar Sahoo, Anil Mohapatra,
• Abstract summary: Olive Ridley Survival (ORS) is proposed which is inspired from survival challenges faced by hatchlings of Olive Ridley sea turtle. ORS has two major phases: hatchlings survival through environmental factors and impact of movement trajectory on its survival. To validate the algorithm, fourteen mathematical benchmark functions from standard CEC test suites are evaluated and statistically tested.
• Score: 2.4343652794054487
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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