Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (7): 1829-1938.doi: 10.23940/ijpe.19.07.p9.18291838

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Gaussian Perturbation Whale Optimization Algorithm based on Nonlinear Strategy

Yu Lia,b, Xiaoting Lib,*, Jingsen Liuc, and Xuechen Tub   

  1. a Institute of Management Science and Engineering, Henan University, Kaifeng, 475004, China
    b Business School, Henan University, Kaifeng, 475004, China
    c Institute of Intelligent Network System, Henan University, Kaifeng, 475004, China
  • Submitted on ;
  • Contact: * E-mail address: 104753170952@vip.henu.edu.cn
  • About author:Yu Li received her Ph.D. from University of Shanghai for Science and Technology. She is a professor in the Institute of Management Science and Engineering and Business School at Henan University. Her areas of research include intelligence algorithms, e-commerce, and logistics management.Xiaoting Li received her bachelor's degree in management. Currently, she is a postgraduate student in the Business School at Henan University. Her current research interest is intelligence algorithms.Jingsen Liu received his Ph.D. from Northwestern Polytechnical University. He is a professor in the Institute of Intelligent Network Systems and College of Software at Henan University. His research interests include intelligence algorithms, network information security, and data optimization.Xuechen Tu received her bachelor's degree in management. Currently, she is a master's candidate in the Business School at Henan University. Her research interest is intelligence algorithms.
  • Supported by:
    This study is supported by the National Natural Science Foundation of China (No. 71601071), the Science & Technology Program of Henan Province, China (No. 182102310886 and 162102110109), and the MOE Youth Foundation Project of Humanities and Social Sciences (No. 15YJC630079). We are particularly grateful for the suggestions of the editor and the anonymous reviewers, which greatly improved the quality of the paper.

Abstract: Whale Optimization Algorithm (WOA) is a recently developed swarm intelligence optimization algorithm that has strong global search capability. In this work, considering the deficiency of WOA in a local search mechanism and convergence speed, a Gaussian Perturbation Whale Optimization Algorithm based on Nonlinear Strategy (GWOAN) is introduced. By implementing a nonlinear change strategy on the parameters, the swarm is able to enter the local search process faster and thus improve the local exploitation ability of the algorithm. In a later stage, Gaussian perturbation is performed on the current optimal individuals to enrich the population diversity, avoid premature convergence of the algorithm, and improve the global development capability of the algorithm. The results of the comparison experiment between the GWOAN, WOA, and PSO algorithms show that the accuracy of GWOAN in the selected ten function optimization solutions is significantly higher than that of the comparison algorithms, and its optimization efficiency is also better. Among the ten benchmark functions, four can converge to the theoretical optimal value.

Key words: whale optimization algorithm, swarm intelligence algorithm, nonlinear strategy, Gaussian perturbation, function optimization