Osprey-PSO: A Novel Hybrid Meta-heuristic Algorithm for Unconstrained Optimization Problems

The present work proposes a novel hybrid meta-heuristic algorithm called Osprey-PSO, which combines Osprey Optimization Algorithm (OOA) with Particle Swarm Optimization (PSO) algorithm. The OOA introduces an innovative meta-heuristic algorithm inspired by nature, specifically drawing from the hunting behavior of the osprey, a bird of prey specializing in catching fish for its sustenance. The PSO algorithm is derived from nature, specifically inspired by the collective movement observed in bird flocks. Although the OOA method can rapidly locate reasonable solutions, it occasionally gets trapped in local minima. Hence, the integration of PSO with OOA aims to navigate local minima and address the limitations of OOA. The proposed technique is assessed across 23 benchmark functions, alongside real-world scenarios, yielding satisfactory results. Comparing the suggested method’s findings reveals that, in terms of quality and convergence rate, the results are both efficient and effective.