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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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Basic Information:
DOI：10.13705/j.issn.1671-6841.2020226
China Classification Code:TP18
Citation Information:
[1]LI Tianyi,CHEN Hongmei.A New Hybrid Evolutionary Algorithm for Solving Feature Selection Problem[J].Journal of Zhengzhou University(Natural Science Edition),2021,53(02):41-49.DOI:10.13705/j.issn.1671-6841.2020226.
Fund Information:
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)
2020-07-15
2020
2021-01-21
2021-05-28
2021
1
2021-03-25
2021-03-25
2021-03-25