Osprey optimization algorithm: A new bio-inspired metaheuristic algorithm for solving engineering optimization problems

Analysis

Why This Paper Matters

This paper introduces the Osprey Optimization Algorithm (OOA), a fresh addition to the growing family of nature-inspired metaheuristics. As optimization problems in engineering and AI become increasingly complex, the demand for robust, easy-to-implement algorithms that balance exploration and exploitation remains high. OOA draws inspiration from the osprey's distinctive hunting strategy—detecting fish, diving, and then carrying the prey to a safe feeding spot. This two-phase behavior naturally maps to the exploration-exploitation trade-off central to metaheuristic search. The paper's significance lies in its systematic evaluation: testing on 29 CEC 2017 benchmark functions and 22 real-world constrained engineering problems from CEC 2011, with comparisons against 12 established algorithms. Such thorough benchmarking provides practitioners with credible evidence of OOA's competitiveness.

Technical Contributions

• Novel bio-inspired model: OOA mathematically formalizes osprey hunting into two phases: exploration (global search via prey detection and movement) and exploitation (local search via carrying prey to a safe zone).
• Phase transition mechanism: The algorithm naturally shifts from exploration to exploitation as the osprey moves from hunting to feeding, without requiring additional control parameters.
• Comprehensive evaluation: Tested on 29 unimodal, multimodal, and composite functions from CEC 2017, plus 22 constrained real-world problems from CEC 2011.
• Competitive comparison: Compared against 12 well-known metaheuristics including GA, PSO, DE, GWO, and others, with OOA showing superior or comparable performance.

Results

OOA achieved superior performance on the CEC 2017 benchmark suite, particularly on multimodal and composite functions where maintaining exploration-exploitation balance is critical. On the CEC 2011 real-world problems, OOA demonstrated high capability in finding feasible and optimal solutions for constrained engineering design problems (e.g., pressure vessel design, tension/compression spring design). The paper reports that OOA outperformed all 12 competitor algorithms in terms of final solution quality and convergence speed, though specific numerical metrics (e.g., mean best fitness, standard deviations) are not detailed in the abstract.

Significance

OOA offers AI practitioners and engineers a new, intuitive optimizer that requires minimal parameter tuning. Its strong performance on constrained real-world problems suggests immediate applicability in areas like structural design, robotics, and resource allocation. The algorithm's simplicity and clear biological analogy make it easy to implement and extend. For the broader AI field, OOA contributes to the ongoing search for more efficient metaheuristics that can handle the increasing complexity of optimization in deep learning hyperparameter tuning, neural architecture search, and reinforcement learning policy optimization. Future work could explore hybridization with local search methods or adaptation for multi-objective and dynamic optimization.