Zhang Kaifan, Yuan Fujiang, Jiang Yang, Mao Zebing, Zuo Zihao, Peng Yanhong
School of Computer Science, Hubei University of Technology, Wuhan 430068, China.
School of Computer Science and Technology, Taiyuan Normal University, Taiyuan 030619, China.
Biomimetics (Basel). 2025 May 21;10(5):342. doi: 10.3390/biomimetics10050342.
In recent years, metaheuristic algorithms have garnered significant attention for their efficiency in solving complex optimization problems. However, their performance critically depends on maintaining a balance between global exploration and local exploitation; a deficiency in either can result in premature convergence to local optima or low convergence efficiency. To address this challenge, this paper proposes an enhanced ivy algorithm guided by a particle swarm optimization (PSO) mechanism, referred to as IVYPSO. This hybrid approach integrates PSO's velocity update strategy for global searches with the ivy algorithm's growth strategy for local exploitation and introduces an ivy-inspired variable to intensify random perturbations. These enhancements collectively improve the algorithm's ability to escape local optima and enhance the search stability. Furthermore, IVYPSO adaptively selects between local growth and global diffusion strategies based on the fitness difference between the current solution and the global best, thereby improving the solution diversity and convergence accuracy. To assess the effectiveness of IVYPSO, comprehensive experiments were conducted on 26 standard benchmark functions and three real-world engineering optimization problems, with the performance compared against 11 state-of-the-art intelligent optimization algorithms. The results demonstrate that IVYPSO outperformed most competing algorithms on the majority of benchmark functions, exhibiting superior search capability and robustness. In the stability analysis, IVYPSO consistently achieved the global optimum across multiple runs on the three engineering cases with reduced computational time, attaining a 100% success rate (SR), which highlights its strong global optimization ability and excellent repeatability.
近年来，元启发式算法因其在解决复杂优化问题方面的效率而备受关注。然而，它们的性能关键取决于在全局探索和局部开发之间保持平衡；两者中的任何一个不足都可能导致过早收敛到局部最优或收敛效率低下。为应对这一挑战，本文提出了一种由粒子群优化（PSO）机制引导的增强型常春藤算法，称为IVYPSO。这种混合方法将PSO用于全局搜索的速度更新策略与常春藤算法用于局部开发的生长策略相结合，并引入了一个受常春藤启发的变量来强化随机扰动。这些改进共同提高了算法逃离局部最优的能力，并增强了搜索稳定性。此外，IVYPSO根据当前解与全局最优解之间的适应度差异，在局部生长和全局扩散策略之间进行自适应选择，从而提高了解的多样性和收敛精度。为评估IVYPSO的有效性，对26个标准基准函数和三个实际工程优化问题进行了全面实验，并将性能与11种先进的智能优化算法进行了比较。结果表明，IVYPSO在大多数基准函数上优于大多数竞争算法，表现出卓越的搜索能力和鲁棒性。在稳定性分析中，IVYPSO在三个工程案例的多次运行中始终实现全局最优，且计算时间减少，成功率（SR）达到100%，这突出了其强大的全局优化能力和出色的可重复性。
