Hou Jun, Cui Yuemei, Rong Ming, Jin Bo
Faculty of Sports Science, Ningbo University, Ningbo 315211, China.
Research Academy of Grand Health, Ningbo University, Ningbo 315211, China.
Biomimetics (Basel). 2024 Jul 8;9(7):419. doi: 10.3390/biomimetics9070419.
The football team training algorithm (FTTA) is a new metaheuristic algorithm that was proposed in 2024. The FTTA has better performance but faces challenges such as poor convergence accuracy and ease of falling into local optimality due to limitations such as referring too much to the optimal individual for updating and insufficient perturbation of the optimal agent. To address these concerns, this paper presents an improved football team training algorithm called IFTTA. To enhance the exploration ability in the collective training phase, this paper proposes the fitness distance-balanced collective training strategy. This enables the players to train more rationally in the collective training phase and balances the exploration and exploitation capabilities of the algorithm. To further perturb the optimal agent in FTTA, a non-monopoly extra training strategy is designed to enhance the ability to get rid of the local optimum. In addition, a population restart strategy is then designed to boost the convergence accuracy and population diversity of the algorithm. In this paper, we validate the performance of IFTTA and FTTA as well as six comparison algorithms in CEC2017 test suites. The experimental results show that IFTTA has strong optimization performance. Moreover, several engineering-constrained optimization problems confirm the potential of IFTTA to solve real-world optimization problems.
足球队训练算法（FTTA）是2024年提出的一种新的元启发式算法。FTTA具有较好的性能，但由于存在过多参考最优个体进行更新以及最优智能体扰动不足等局限性，面临收敛精度差和易陷入局部最优等挑战。为解决这些问题，本文提出了一种改进的足球队训练算法，即IFTTA。为增强集体训练阶段的探索能力，本文提出了适应度距离平衡集体训练策略。这使得球员在集体训练阶段能够更合理地训练，并平衡算法的探索和利用能力。为进一步扰动FTTA中的最优智能体，设计了非垄断额外训练策略以增强摆脱局部最优的能力。此外，还设计了种群重启策略以提高算法的收敛精度和种群多样性。在本文中，我们在CEC2017测试套件中验证了IFTTA和FTTA以及六种比较算法的性能。实验结果表明IFTTA具有强大的优化性能。此外，几个工程约束优化问题证实了IFTTA解决实际优化问题的潜力。
