Computational benchmarking demonstrates competitive performance of a bio-inspired metaheuristic across standard test suites and engineering tasks, indicating practical optimization utility.
This study proposes the Piranha Predation Algorithm (PPA), a population‐based metaheuristic inspired by the sensory and group‐response behaviors of piranhas. The algorithm combines four candidate‐generation mechanisms associated with visual, auditory, olfactory, and lateral‐line perception with population interaction and best‐guided local refinement. These operations provide local sampling, directed search, stochastic perturbation, and diversity maintenance to balance exploration and exploitation. Evaluations on the CEC‐2020 and CEC‐2022 benchmark suites show that PPA is competitive with 14 comparison algorithms and performs favorably on many tested functions, although it is not uniformly the best method across all functions and dimensions. Tests on five constrained engineering design problems and four three‐dimensional UAV path‐planning scenarios further indicate its practical potential under the studied settings.
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Peng et al. (2026) studied this question.
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