Adaptive Pyrosome Optimization Algorithm (APOA): a novel algorithm for solving optimization and engineering problems

Inspired by the spores controlling during the life cycle of pyrosomes, this paper proposes a novel swarm intelligence-based meta-heuristic adaptive pyrosome optimization algorithm named APOA, which consists of four stages: Initialization, Information Interaction, Adaptive Decision-making, and Reaction. During the interaction, spores broadcast their information to each other. The decision-making stage employs a mechanism using random and adaptive strategies to enhance global exploration and local exploitation, respectively. Comprehensive tests on 23 test functions demonstrate the superior convergence and exceptional local optima avoidance of APOA. Extensive experimental results across CEC2014, CEC2017, CEC2020, and CEC2022 standard test suites reveal that APOA statistically outperforms 14 state-of-the-art metaheuristics in convergence speed, solution quality, and algorithmic stability. Furthermore, Empirical results on four types of engineering design problems further confirm that APOA is significantly superior to the leading competing algorithms in solving complex high-dimensional optimization challenges.