Arctic puffin optimization: A bio-inspired metaheuristic algorithm for solving engineering design optimization

Wenchuan Wang

doi:10.1016/j.advengsoft.2024.103694

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Arctic puffin optimization: A bio-inspired metaheuristic algorithm for solving engineering design optimization

2024-09-01

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王

王文川 (Wenchuan Wang) *

Wei-can Tian

Hong-fei Zang

DOI：10.1016/j.advengsoft.2024.103694

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Abstract

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In this paper, we innovatively propose the Arctic Puffin Optimization (APO), a metaheuristic optimization algorithm inspired by the survival and predation behaviors of the Arctic puffin. The APO consists of an aerial flight (exploration) and an underwater foraging (exploitation) phase. In the exploration phase, the Levy flight and velocity factor mechanisms are introduced to enhance the algorithm's ability to jump out of local optima and improve the convergence speed. In the exploitation phase, strategies such as the synergy and adaptive change factors are used to ensure that the algorithm can effectively utilize the current best solution and guide the search direction. In addition, the dynamic transition between the exploration and development phases is realized through the behavioral conversion factor, which effectively balances global search and local development. In order to verify the advancement and applicability of the APO algorithm, it is compared with nine advanced optimization algorithms. In the three test sets of CEC2017, CEC2019, and CEC2022, the APO algorithm outperforms the other compared algorithms in 72%, 70%, and 75% of the cases, respectively. Meanwhile, the Wilcoxon signed-rank test results and Friedman rank-mean statistically prove the superiority of the APO algorithm. Furthermore, on thirteen real-world engineering problems, APO outperforms the other compared algorithms in 85% of the test cases, demonstrating its potential in solving complex real-world optimization problems. In summary, APO proves its practical value and advantages in solving various complex optimization problems by its excellent performance.

Keywords:

Arctic puffin optimization

Metaheuristic optimization algorithm

Engineering design

Constrained optimization

Swarm intelligence