Li Qian, Zhou Yiwei
Guangdong Provincial Key Laboratory of Ornamental Plant Germplasm Innovation and Utilization, Environmental Horticulture Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China.
Department of Computer Engineering, College of Engineering, Dongshin University, Naju 58245, Republic of Korea.
Biomimetics (Basel). 2025 Sep 22;10(9):638. doi: 10.3390/biomimetics10090638.
The basic Salp Swarm Algorithm (SSA) offers advantages such as a simple structure and few parameters. However, it is prone to falling into local optima and remains inadequate for seed classification tasks that involve hyperparameter optimization of machine learning classifiers such as Support Vector Machines (SVMs). To overcome these limitations, an Enhanced Knowledge-based Salp Swarm Algorithm (EKSSA) is proposed. The EKSSA incorporates three key strategies: Adaptive adjustment mechanisms for parameters c1 and α to better balance exploration and exploitation within the salp population; a Gaussian walk-based position update strategy after the initial update phase, enhancing the global search ability of individuals; and a dynamic mirror learning strategy that expands the search domain through solution mirroring, thereby strengthening local search capability. The proposed algorithm was evaluated on thirty-two CEC benchmark functions, where it demonstrated superior performance compared to eight state-of-the-art algorithms, including Randomized Particle Swarm Optimizer (RPSO), Grey Wolf Optimizer (GWO), Archimedes Optimization Algorithm (AOA), Hybrid Particle Swarm Butterfly Algorithm (HPSBA), Aquila Optimizer (AO), Honey Badger Algorithm (HBA), Salp Swarm Algorithm (SSA), and Sine-Cosine Quantum Salp Swarm Algorithm (SCQSSA). Furthermore, an EKSSA-SVM hybrid classifier was developed for seed classification, achieving higher classification accuracy.
基本的樽海鞘群算法（SSA）具有结构简单、参数少等优点。然而，它容易陷入局部最优，对于涉及支持向量机（SVM）等机器学习分类器超参数优化的种子分类任务来说仍显不足。为了克服这些局限性，提出了一种基于知识增强的樽海鞘群算法（EKSSA）。EKSSA包含三个关键策略：对参数c1和α的自适应调整机制，以更好地平衡樽海鞘群体内的探索和利用；在初始更新阶段之后基于高斯游走的位置更新策略，增强个体的全局搜索能力；以及一种动态镜像学习策略，通过解的镜像扩展搜索域，从而增强局部搜索能力。所提出的算法在32个CEC基准函数上进行了评估，与包括随机粒子群优化器（RPSO）、灰狼优化器（GWO）、阿基米德优化算法（AOA）、混合粒子群蝴蝶算法（HPSBA）、天鹰座优化器（AO）、蜜獾算法（HBA）、樽海鞘群算法（SSA）和正弦余弦量子樽海鞘群算法（SCQSSA）在内的8种先进算法相比，表现出了卓越的性能。此外，还开发了一种EKSSA-SVM混合分类器用于种子分类，实现了更高的分类准确率。
