A Novel Metaheuristic Algorithm: The Team Competition and Cooperation Optimization Algorithm

作     者：Tao Wu Xinyu Wu Jingjue Chen Xi Chen Amir Homayoon Ashrafzadeh 

作者机构：School of Computer ScienceChengdu University of Information TechnologyChengdu610225China School of Computer Science and EngineeringSouthwest Minzu UniversityChengdu610041China CSIT DepartmentSchool of ScienceRMIT UniversityMelbourne3058Australia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第73卷第11期

页      面：2879-2896页

核心收录：

学科分类：0711[理学-系统科学] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research was partially supported by the National Key Research and Development Program of China(2018YFC1507005) Sichuan Science and Technology Program(2020YFG0189,22ZDYF3494) China Postdoctoral Science Foundation(2018M643448) Fundamental Research Funds for the Central Universities,Southwest Minzu University(2022101). 

主　　题：Optimization metaheuristic algorithm 

摘      要：Metaheuristic algorithm is a generalization of heuristic algorithm that can be applied to almost all optimization problems.For optimization problems,metaheuristic algorithm is one of the methods to find its optimal solution or approximate solution under limited conditions.Most of the existing metaheuristic algorithms are designed for serial systems.Meanwhile,existing algorithms still have a lot of room for improvement in convergence speed,robustness,and performance.To address these issues,this paper proposes an easily parallelizable metaheuristic optimization algorithm called team competition and cooperation optimization(TCCO)inspired by the process of human team cooperation and competition.The proposed algorithm attempts to mathematically model human team cooperation and competition to promote the optimization process and find an approximate solution as close as possible to the optimal solution under limited conditions.In order to evaluate the performance of the proposed algorithm,this paper compares the solution accuracy and convergence speed of the TCCO algorithm with the Grasshopper Optimization Algorithm(GOA),Seagull Optimization Algorithm(SOA),Whale Optimization Algorithm(WOA)and Sparrow Search Algorithm(SSA).Experiment results of 30 test functions commonly used in the optimization field indicate that,compared with these current advanced metaheuristic algorithms,TCCO has strong competitiveness in both solution accuracy and convergence speed.