二进制重组粒子群优化及其应用
Jian Zhu1,2, Jianhua Liu1,2, Yuxiang Chen1,2
1School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China.
Biomimetics (Basel, Switzerland)
|June 27, 2023
概括
新的二进制重组粒子群优化 (BRPSO) 算法有效地解决了没有转移函数的离散优化问题. 在特征选择任务中,BRPSO表现出具有竞争力的表现,在较少的特征选择中实现了高分类准确性.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 机器学习 机器学习
背景情况:
- 粒子优化 (PSO) 是一种元启发式算法,主要用于连续优化.
- 现有的二进制PSO变体通常依赖于对离散问题的转移函数.
- 需要高效的二进制优化算法,具有强大的探索能力.
研究的目的:
- 为了适应重组粒子优化 (RPSO) 算法用于离散优化问题.
- 为了引入一种新的二进制变体,二进制重组粒子集群优化 (BRPSO) 算法.
- 评估BRPSO在特征选择任务中的表现.
主要方法:
- 开发了二进制重组粒子集群优化 (BRPSO) 算法,这是RPSO的新版本.
- BRPSO使用基于位置公式比较和随机数的独特粒子更新机制,避免转移函数.
- 在位置更新公式中加入了一个新的扰动术语,以加强探索.
主要成果:
- 在特征选择实验中,BRPSO展示了与四个同行算法的竞争性性能.
- 该算法实现了高分类准确度.
- BRPSO有效地减少了所选特征的数量,表明了效率.
结论:
- BRPSO是一种可行的和有效的算法,用于离散优化问题,特别是特征选择.
- 算法的参数效率和强大的早期探索能力是值得注意的优势.
- BRPSO为现有的二进制元启发算法提供了一个有竞争力的替代方案.
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