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为结合不同演化算法的优势,提出一个混合鲸鱼算法(hybrid whale optimization algorithm, HWOA)。在HWOA算法中鲸鱼优化算法(whale optimization algorithm, WOA)的收缩环绕机制被正余弦算法(sine cosine algorithm, SCA)取代,以实现迭代初期探索和开发之间更好的平衡。此外,在灰狼优化算法(grey wolf optimization, GWO)中引入粒子群算法的个人最佳位置的概念,并引入决策权重参数以更好地反映狼群的等级制度。为提高算法的多样性,在搜索过程中,改进后的灰狼算法和鲸鱼算法的螺旋更新机制随机地被选择。为有效避免算法陷入局部最优,使用非线性的参数调整策略和混沌映射来更新HWOA中的重要参数。实验结果表明,新算法可以有效提高分类的准确率,并选择最合适的特征子集。
Abstract：A hybrid whale optimization algorithm(HWOA) was proposed. In HWOA, the shrinking encircling mechanism of the whale optimization algorithm(WOA) was replaced by sine cosine algorithm(SCA) to achieve a better balance between exploration and exploitation. The concept of personal best position of particle swarm optimization algorithm(PSO) was used in grey wolf optimization algorithm(GWO). Besides, a set of weight parameters was introduced to better reflect the hierarchy of wolves. For increasing the diversity of the search process, the improved grey wolf algorithm was added into the exploitation stage. The spiral updating mechanism of WOA and the improved grey wolf optimizer was randomly selected during the searching process. In order to avoid the algorithm falling into local optimality, nonlinear parameter adjustment strategies and chaotic mapping to update important parameters were applied to HWOA. Experimental results showed that the newly proposed method could effectively improve the accuracy of classification and choose the most suitable feature subset.
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基本信息:
DOI：10.13705/j.issn.1671-6841.2020226
中图分类号:TP18
引用信息:
[1]李天翼,陈红梅.一种用于解决特征选择问题的新型混合演化算法[J],2021,53(02):41-49.DOI:10.13705/j.issn.1671-6841.2020226.
基金信息:
国家自然科学基金项目(61572406,61976182,62076171);; 四川省国际科技创新合作重点项目(2019YFH0097);; 四川省科技厅应用基础研究计划项目(2019YJ0084)