An Enhanced Knowledge Salp Swarm Algorithm for Solving the Numerical Optimization and Seed Classification Tasks.

IF 3.9 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY
Qian Li, Yiwei Zhou
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引用次数: 0

Abstract

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.

一种求解数值优化和种子分类任务的增强知识Salp群算法。
Salp群算法具有结构简单、参数少等优点。然而,它很容易陷入局部最优,对于涉及超参数优化的机器学习分类器(如支持向量机(svm))的种子分类任务仍然不足。为了克服这些局限性,提出了一种基于知识的增强型Salp群算法(EKSSA)。EKSSA包含三个关键策略:参数c1和α的自适应调节机制,以更好地平衡salp种群内的勘探和开发;初始更新阶段后采用基于高斯行走的位置更新策略,增强个体的全局搜索能力;动态镜像学习策略,通过解镜像扩展搜索域,增强局部搜索能力。该算法在32个CEC基准函数上进行了评估,与随机粒子群优化器(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)、Aquila优化器(AO)、蜜獾算法(HBA)、Salp Swarm算法(SSA)和正弦余弦量子Salp Swarm算法(SCQSSA)等8种最先进的算法相比,该算法表现出了优越的性能。在此基础上,建立了EKSSA-SVM混合分类器进行种子分类,提高了分类精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biomimetics
Biomimetics Biochemistry, Genetics and Molecular Biology-Biotechnology
CiteScore
3.50
自引率
11.10%
发文量
189
审稿时长
11 weeks
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