Guo Jia, Zhou Guoyuan, Yan Ke, Sato Yuji, Di Yi
School of Information Engineering, Hubei University of Economics, Wuhan, 430205, China.
Hubei Internet Finance Information Engineering Technology Research Center, Wuhan, 430205, China.
Sci Rep. 2023 Oct 25;13(1):18314. doi: 10.1038/s41598-023-43748-w.
High-dimensional optimization presents a novel challenge within the realm of intelligent computing, necessitating innovative approaches. When tackling high-dimensional spaces, traditional evolutionary tools often encounter pitfalls, including dimensional catastrophes and a propensity to become trapped in local optima, ultimately compromising result accuracy. To address this issue, we introduce the Pair Barracuda Swarm Optimization (PBSO) algorithm in this paper. PBSO employs a unique strategy for constructing barracuda pairs, effectively mitigating the challenges posed by high dimensionality. Furthermore, we enhance global search capabilities by incorporating a support barracuda alongside the leading barracuda pair. To assess the algorithm's performance, we conduct experiments utilizing the CEC2017 standard function and compare PBSO against five state-of-the-art natural-inspired optimizers in the control group. Across 29 test functions, PBSO consistently secures top rankings with 9 first-place, 13 second-place, 5 third-place, 1 fourth-place, and 1 fifth-place finishes, yielding an average rank of 2.0345. These empirical findings affirm that PBSO stands as the superior choice among all test algorithms, offering a dependable solution for high-dimensional optimization challenges.
高维优化在智能计算领域提出了一个新的挑战，需要创新的方法。在处理高维空间时，传统的进化工具常常遇到陷阱，包括维度灾难和容易陷入局部最优，最终影响结果的准确性。为了解决这个问题，我们在本文中引入了配对梭子鱼群优化（PBSO）算法。PBSO采用了一种独特的策略来构建梭子鱼对，有效地缓解了高维度带来的挑战。此外，我们通过在领先的梭子鱼对旁边加入一条支持梭子鱼来增强全局搜索能力。为了评估该算法的性能，我们使用CEC2017标准函数进行实验，并将PBSO与对照组中的五种最先进的自然启发式优化器进行比较。在29个测试函数中，PBSO始终名列前茅，获得9个第一名、13个第二名、5个第三名、1个第四名和1个第五名，平均排名为2.0345。这些实证结果证实，PBSO是所有测试算法中的最佳选择，为高维优化挑战提供了可靠的解决方案。
