Yuan Tianzuo, Zhang Huanzun, Jin Jie, Chen Zhebo, Cai Shanshan
Faculty of Health Sciences, University of Macau, Macau 999078, China.
Stony Brook Institute, Anhui University, Hefei 230039, China.
Biomimetics (Basel). 2025 Jul 27;10(8):496. doi: 10.3390/biomimetics10080496.
Superb Fairy-wren Optimization Algorithm (SFOA) is an animal-based meta-heuristic algorithm derived from Fairy-wren's behavior of growing, feeding, and avoiding natural enemies. The SFOA has some shortcomings when facing complex environments. Its switching mechanism is not enough to adapt to complex optimization problems, and it faces a weakening of population diversity in the late stage of optimization, leading to a higher possibility of falling into local optima. In addition, its global search ability needs to be improved. To address the above deficiencies, this paper proposes an Adaptive Superb Fairy-wren Optimization Algorithm (ASFOA). To assess the ability of the proposed ASFOA, three groups of experiments are conducted in this paper. Firstly, the effectiveness of the proposed improved strategies is checked on the CEC2018 test set. Second, the ASFOA is compared with eight classical/highly cited/newly proposed metaheuristics on the CEC2018 test set, in which the ASFOA performed the best overall, with average rankings of 1.621, 1.138, 1.483, and 1.966 in the four-dimensional cases, respectively. Then the convergence and robustness of ASFOA is verified on the CEC2022 test set. The experimental results indicate that the proposed ASFOA is a competitive metaheuristic algorithm variant with excellent performance in terms of convergence and distribution of solutions. In addition, we further validate the ability of ASFOA to solve real optimization problems. The average ranking of the proposed ASFOA on 10 engineering constrained optimization problems is 1.500. In summary, ASFOA is a promising variant of metaheuristic algorithms.
超级鹪鹩优化算法（SFOA）是一种基于动物的元启发式算法，它源于鹪鹩的生长、觅食和躲避天敌行为。当面对复杂环境时，SFOA存在一些缺点。其切换机制不足以适应复杂的优化问题，并且在优化后期面临种群多样性减弱的问题，导致陷入局部最优的可能性更高。此外，其全局搜索能力有待提高。为了解决上述不足，本文提出了一种自适应超级鹪鹩优化算法（ASFOA）。为了评估所提出的ASFOA的能力，本文进行了三组实验。首先，在CEC2018测试集上检查所提出的改进策略的有效性。其次，在CEC2018测试集上将ASFOA与八种经典/高引用/新提出的元启发式算法进行比较，其中ASFOA总体表现最佳，在四维情况下的平均排名分别为1.621、1.138、1.483和1.966。然后在CEC2022测试集上验证了ASFOA的收敛性和鲁棒性。实验结果表明，所提出的ASFOA是一种具有竞争力的元启发式算法变体，在解的收敛性和分布方面具有优异的性能。此外，我们进一步验证了ASFOA解决实际优化问题的能力。所提出的ASFOA在10个工程约束优化问题上的平均排名为1.500。综上所述，ASFOA是一种有前途的元启发式算法变体。
