JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOM ›› 2018, Vol. 25 ›› Issue (6): 44-57.doi: 10.19682/j.cnki.1005-8885.2018.1026

• Artificial intelligence • Previous Articles     Next Articles

Novel wolf pack optimization algorithm for intelligent medical treatment personalized recommendation

Wang Dongxing, Qian Xu, Liu Kang, Guan Xinyu   

  1. 1. School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing 100083, China
    2. Department of Mechanical Engineering, Tsinghua University, Beijing 100083, China
  • Received:2018-08-07 Revised:2019-01-02 Online:2018-12-30 Published:2019-02-26
  • Contact: Qian Xu, E-mail: xuqiancumtb@163.com E-mail:xuqiancumtb@163.com
  • About author:Qian Xu, E-mail: xuqiancumtb@163.com
  • Supported by:
    This work was supported by the National Key Research and Development Program of China ( 2016YFB0700502, 2016YFB1001404) and the National Natural Science Foundation of China (51761135121). Authors are indebted to appreciate their full supports of this pioneering work. The authors would
    also like to thank the reviewers for their valuable suggestions and comments.

Abstract: To help the people choose a proper medical treatment organizer, this paper proposes an opposition raiding wolf pack optimization algorithm using random search strategy ( ORRSS-WPOA) for an adaptive shrinking region. Firstly, via the oppositional raiding method (ORM), each wolf has bigger probability of approaching the leader wolf, which makes the exploration of the wolf pack enhanced as a whole. In another word, the wolf pack is not easy to fall into local optimum. Moreover, random searching strategy (RSS) for an adaptive shrinking region is adopted to strengthen exploitation, which enables any wolf to be more likely to find the optimum in some a given region, so macroscopically the wolf pack is easier to find the global optimal in the given range. Finally, a fitness function was designed to judge the appropriateness between a certain patient and a hospital. The performance of the ORRSS-WPOA was comprehensively evaluated by comparing it with several other competitive algorithms on ten classical benchmark functions and the simulated fitness function aimed to solve the problem mentioned above. Under the same condition, our experimental results indicated the excellent performance of ORRSS-WPOA in terms of solution quality and computational efficiency.

Key words: wolf pack optimization, opposition raiding, random search, adaptive, shrinking

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