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Adaptive pyrosome optimization algorithm (APOA): a novel metaheuristic for benchmark and engineering optimization problems
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DOI：10.1007/s10586-026-06397-y.png)
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Abstract
En 中文
Inspired by the distributed information exchange and adaptive responsiveness of pyrosomes, this study proposes an Adaptive Pyrosome Optimization Algorithm (APOA). In APOA, each candidate solution is modeled as a pyrosome composed of interacting spores, which serve as decision-making components that update information through distributed communication and adaptive strategies. The algorithm consists of four stages: initialization (population generation), information exchange (distributed communication), adaptive decision-making (balancing exploration and exploitation via adaptive assumptions and greedy strategies to enhance convergence and solution quality), and reaction (solution update). Benchmark evaluations on 82 functions from the CEC 2014, CEC 2017, CEC 2020, and CEC 2022 test suites demonstrate that APOA achieves superior convergence speed, solution quality, and stability on 23 functions compared with 14 state-of-the-art metaheuristic algorithms. Furthermore, tests on four engineering design problems highlight its robustness and effectiveness in solving complex, high-dimensional optimization tasks.
Keywords:
Optimization
Metaheuristic
Adaptive pyrosome optimization algorithm
Adaptive decision-making mechanism
Journal
C
IF:
4.1
Papers:
5.0K
Citations:
7.5K