Open Access

ARTICLE

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

by Tao Wu¹, Xinyu Wu¹, Jingjue Chen¹, Xi Chen^2,*, Amir Homayoon Ashrafzadeh³

1 School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
2 School of Computer Science and Engineering, Southwest Minzu University, Chengdu, 610041, China
3 CSIT Department, School of Science, RMIT University, Melbourne, 3058, Australia

* Corresponding Author: Xi Chen. Email:

Computers, Materials & Continua 2022, 73(2), 2879-2896. https://doi.org/10.32604/cmc.2022.028942

Received 21 February 2022; Accepted 07 April 2022; Issue published 16 June 2022

Abstract

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.

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Keywords

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