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Abstract Optimization is a critical challenge across several engineering and scientific disciplines. This paper introduces a unique Birds of Prey-Based Optimization (BPBO) algorithm, which was influenced by the astute hunting techniques of predatory birds. The approach utilizes both individual and group hunting techniques while selectively targeting weaker birds to effectively balance exploration and exploitation strategies. By incorporating a dynamic relocation strategy to access more suitable prey, this method enhances population diversity and mitigates the risk of premature convergence. The integration of these strategies, in light of the ongoing dynamic shifts, enables the BPBO algorithm to effectively address a wide range of problems. This includes successful application to both small-scale problems with specific constraints and large-scale problems with dimensions extending up to 1000. To thoroughly assess BPBO’s effectiveness, we conduct an in-depth analysis using 23 optimization benchmarks, of which 13 are designated as scalable. The CEC-2014 benchmark functions are considered a more complex test set. Furthermore, BPBO algorithm is utilized in practical engineering design challenges, such as optimizing welded beams, three-bar trusses, gear trains, cantilever beams, and speed reducers. Comparative analyses were conducted utilizing nine and eight algorithms across two sets of standard testing functions. The comparison of the results across all cases utilizing robust algorithms substantiates the proposed algorithm’s effective performance in exploring the feasible space of the problem. Thorough analyses have been conducted to enhance the understanding of the behavior exhibited by the algorithm parameters. The source codes of the BPBO algorithm are publicly available at https://nimakhodadadi.com/algorithms-%2B-codes .
Ghasemi et al. (Tue,) studied this question.