Competitive Learning: A New Meta-Heuristic Optimization Algorithm

作     者：Afroughinia, Afshin Moghaddam, Reihaneh Kardehi 

作者机构：Islamic Azad Univ Dept Elect Engn Mashhad Branch Mashhad Iran 

出 版 物：《INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS》 (国际人工智能工具杂志)

年 卷 期：2018年第27卷第8期

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Optimization meta-heuristic algorithm competitive learning algorithm 

摘      要：This work proposes a new powerful meta-heuristic optimization algorithm in education process called Competitive Learning (CLA). The algorithm is benchmarked on 8 well-known test functions, and the results are verified by a comparative study with some meta-heuristic optimization methods including: Imperialist Competitive Algorithm (ICA), Teaching-Learning-Based Optimization (TLBO), Grey Wolf Algorithm (GWO), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Analyzing the findings, it is shown that the CLA algorithm is able to provide more accurate results than other well-known meta-heuristic ones. Also, those results applied to famous unimodal and multimodal benchmarks show CLA is efficient in improving accuracy as well as computational speed.