The Piranha Foraging Optimization Algorithm Oriented to Numerical Optimization Problems

Abstract

This paper provides an efficient meta-heuristic optimization algorithm for solving continuous optimization problems in the field of numerical and engineering optimization: Piranha Foraging Optimization Algorithm (PFOA). The algorithm is inspired by the flexible and mobile foraging behavior of piranhas, which are classified into three modes of localized group attack, bloodthirsty swarming attack, and scavenging foraging, and two dynamic search behaviors of exploration and exploitation are constructed by simulating the above behaviors. PFOA allows the population to be diverse at different times of the search by means of a non-linear parameter adjustment strategy, a piranha population survival strategy and a reverse escape search strategy. Using visual means to evaluate the optimization efficiency of PFOA, this paper developed experiments on 23 standard benchmark functions and compared the results with 15 well-known meta-heuristics. Experimental results based on statistical methods such as Wilcoxon rank sum test and Friedman test in multiple dimensions (30, 50, 100 and fixed dimensions) show significant differentiation, stable and significant improvement in algorithm performance compared to other compared algorithms. The unique advantages of PFOA in terms of convergence speed and exploration utilization balance can avoid getting trapped in local optimum regions and effectively solve optimization problems with complex search spaces of multiple dimensions.