Jie Xing, Qinqin Zhao, Huiling Cheny, Yili Zhang, Feng Zhou, Hanli Zhao
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Utilizing Bee Foraging Behavior in Mutational Salp Swarm for Feature Selection: A Study on Return Intentions of Overseas Chinese after COVID-19
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 SSA, 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 algorithm is further proposed for feature selection by using BMSSA as the selection strategy and support vector machine as the classifier. Experimental comparisons on twelve UCI datasets demonstrate the superiority of binary BMSSA. Finally, we collected a dataset on the return-intentions of overseas Chinese after COVID-19 through an anonymous online questionnaire and performed a case study by setting up a binary BMSSA-based feature selection optimization model. . 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 critical factors influencing the willingness to return to China after COVID-19.
期刊介绍:
Journal of Computational Design and Engineering is an international journal that aims to provide academia and industry with a venue for rapid publication of research papers reporting innovative computational methods and applications to achieve a major breakthrough, practical improvements, and bold new research directions within a wide range of design and engineering:
• Theory and its progress in computational advancement for design and engineering
• Development of computational framework to support large scale design and engineering
• Interaction issues among human, designed artifacts, and systems
• Knowledge-intensive technologies for intelligent and sustainable systems
• Emerging technology and convergence of technology fields presented with convincing design examples
• Educational issues for academia, practitioners, and future generation
• Proposal on new research directions as well as survey and retrospectives on mature field.