Yuansheng Gao

In this paper, a metaheuristic algorithm called PID-based search algorithm (PSA) is proposed for global optimization. The algorithm is based on an incremental PID algorithm that converges the entire population to an optimal state by continuously adjusting the system deviations. PSA is mathematically modeled and implemented to achieve optimization in a wide range of search spaces. PSA is used to solve CEC2017 benchmark test functions and six constrained problems. The optimization performance of PSA is verified by comparing it with seven metaheuristics proposed in recent years. The Kruskal-Wallis, Holm and Friedman tests verified the superiority of PSA in terms of statistical significance. The results show that PSA can be better balanced exploration and exploitation with strong optimization capability.

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.