Cooperation search algorithm: A novel metaheuristic evolutionary intelligence algorithm for numerical optimization and engineering optimization problems

• Published in: Applied soft computing Vol. 98; p. 106734
• Main Authors: Feng, Zhong-kai, Niu, Wen-jing, Liu, Shuai
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2021
• Subjects: Cooperation search algorithm; Engineering optimization; Numerical optimization; Population-based metaheuristic method; Cooperation search algorithm; Numerical optimization; Engineering optimization; Population-based metaheuristic method
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2020.106734

This paper develops a novel population-based evolutionary method called cooperation search algorithm (CSA) to address the complex global optimization problem. Inspired by the team cooperation behaviors in modern enterprise, the CSA method randomly generates a set of candidate solutions in the problem space, and then three operators are repeatedly executed until the stopping criterion is met: the team communication operator is used to improve the global exploration and determine the promising search area; the reflective learning operator is used to achieve a comprise between exploration and exploitation; the internal competition operator is used to choose solutions with better performances for the next cycle. Firstly, three kinds of mathematical optimization problems (including 24 famous test functions, 25 CEC2005 test problems and 30 CEC2014 test problems) are used to test the convergence speed and search accuracy of the CSA method. Then, several famous engineering optimization problems (like Gear train design, Welded beam design and Speed reducer design) are chosen to testify the engineering practicality of the CSA method. The results in different scenarios demonstrate that as compared with several existing evolutionary algorithms, the CSA method can effectively explore the decision space and produce competitive results in terms of various performance evaluation indicators. Thus, an effective tool is provided for solving the complex global optimization problems. •Cooperation search algorithm is proposed for global optimization problems.•The team communication operator is designed for global exploration.•The reflective learning operator is designed for local exploitation.•The dualistic competition operator is designed for survival of the fittest.•CSA yields satisfying results for 79 test functions and 8 engineering problems.