Utilizing bee foraging behavior in mutational salp swarm for feature selection: a study on return-intentions of overseas Chinese after COVID-19

Abstract

Abstract We present a bee foraging behavior-driven mutational salp swarm algorithm (BMSSA) based on an improved bee foraging strategy and an unscented mutation strategy. The improved bee foraging strategy is leveraged in the follower location update phase to break the fixed range search of salp swarm algorithm, while the unscented mutation strategy on the optimal solution is employed to enhance the quality of the optimal solution. Extensive experimental results on public CEC 2014 benchmark functions validate that the proposed BMSSA performs better than nine well-known metaheuristic methods and seven state-of-the-art algorithms. The binary BMSSA (bBMSSA) algorithm is further proposed for feature selection by using BMSSA as the selection strategy and support vector machine as the classifier. Experimental comparisons on 12 UCI datasets demonstrate the superiority of bBMSSA. Finally, we collected a dataset on the return-intentions of overseas Chinese after coronavirus disease (COVID-19) through an anonymous online questionnaire and performed a case study by setting up a bBMSSA-based feature selection optimization model. The outcomes manifest that the bBMSSA-based feature selection model exhibits a conspicuous prowess, attaining an accuracy exceeding 93%. The case study shows that the development prospects, the family and job in the place of residence, seeking opportunities in China, and the possible time to return to China are the critical factors influencing the willingness to return to China after COVID-19.