A Novel Meta-heuristic Algorithm for Numerical and Engineering Optimization Problems:Piranha Foraging Optimization Algorithm (PFOA)

This paper provides a novel meta-heuristic optimization algorithm for solving continuous problems efficiently in the field of numerical and engineering optimization: Piranha Foraging Optimization Algorithm (PFOA). The is inspired by flexible mobile foraging behaviour piranha swarm divides their behavior into three patterns: localized group attack, bloodthirsty cluster attack scavenging foraging, simulates above behaviors to construct two dynamic search processes exploration exploitation. PFOA uses strategies non-linear parameter control, population survival reverse evasion enable populations have better diversity at different stages help find solutions. To gain insight performance PFOA, visualization methods were used assess efficiency analyse impact characteristics modes, sensitivity parameters size on algorithm. was further tested with 27 CEC benchmark functions four real design problems, results compared 13 well-known meta-heuristics. Test based statistical such as box-line plots, Wilcoxon rank sum test Friedman multiple dimensions (30, 50, 100 fixed dimensions) show significant differences other algorithms that stable improvement. unique advantages terms equilibrium convergence speed can avoid getting trapped local optimum regions effectively solve complex spaces.