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

TL;DR

The paper introduces ORS, a new meta-heuristic inspired by Olive Ridley sea turtle hatchling survival challenges, demonstrating its effectiveness through benchmark tests and engineering problem solutions.

Contribution

A novel meta-heuristic algorithm inspired by Olive Ridley hatchling survival, with theoretical analysis and validation on standard and complex benchmark functions.

Findings

Outperforms some state-of-the-art meta-heuristics on benchmark functions.

Effectively solves engineering optimization problems.

Shows sub-optimal behavior on certain recent benchmarks.

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.