Escape after love: Philoponella prominens optimizer and its application to 3D path planning

Yuansheng Gao, Jinpeng Wang, Changlin Li

Abstract: According to the No Free Lunch theorem, we need to propose new metaheuristic algorithms with different optimization behaviors to better solve global optimization problems. Therefore, this paper presents a novel metaheuristic called Philoponella prominens Optimizer (PPO). It is inspired by the special mating behavior of the P. prominens, and simulates escape, sexual cannibalism and predation behaviors of males after mating. The superiority of the PPO is verified by comparing with seven recently proposed metaheuristics on CEC2017 benchmark functions. Furthermore, the Wilcoxon signed-rank sum test confirms significance in 93.60%, the Friedman ranking test places the PPO first, and the Kruskal–Wallis test verifies that the PPO’s values is smallest on about 2/3 functions. Further tests on CEC2022 benchmark functions demonstrates PPO's competitiveness against six state-of-the-art metaheuristics. Additionally, the PPO is applied to four engineering problems and 3D UAV path planning problem, where it consistently outperforms competitors. Source codes of the PPO are publicly available at https://ww2.mathworks.cn/matlabcentral/fileexchange/171624-philoponella-prominens-optimizer.