Group teaching optimization algorithm: A novel metaheuristic method for solving global optimization problems

• Published in: Expert systems with applications Vol. 148; p. 113246
• Main Authors: Zhang, Yiying, Jin, Zhigang
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15.06.2020; Elsevier BV
• Subjects: Algorithms; Computer simulation; Criteria; Design engineering; Design optimization; Engineering design; Global optimization; Group teaching; Heuristic methods; Optimization algorithms; Optimization techniques; Parameters; Swarm intelligence; Teachers; Teaching; Engineering design; Group teaching; Global optimization; Swarm intelligence
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2020.113246

•A new classification method for metaheuristic algorithms is presented.•Group teaching optimization algorithm is proposed for global optimization.•Group teaching optimization algorithm is inspired by group teaching.•Experiments show that the proposed method outperforms state-of-the-art algorithms. In last 30 years, many metaheuristic algorithms have been developed to solve optimization problems. However, most existing metaheuristic algorithms have extra control parameters except the essential population size and stopping criterion. Considering different characteristics of different optimization problems, how to adjust these extra control parameters is a great challenge for these algorithms in solving different optimization problems. In order to address this challenge, a new metaheuristic algorithm called group teaching optimization algorithm (GTOA) is presented in this paper. The proposed GTOA is inspired by group teaching mechanism. To adapt group teaching to be suitable for using as an optimization technique, without loss of generality, four simple rules are first defined. Then a group teaching model is built under the guide of the four rules, which consists of teacher allocation phase, ability grouping phase, teacher phase and student phase. Note that GTOA needs only the essential population size and stopping criterion without extra control parameters, which has great potential to be used widely. GTOA is first examined over 28 well-known unconstrained benchmark problems and the optimization results are compared with nine state-of-the-art algorithms. Experimental results show the superior performance of the proposed GTOA for these problems in terms of solution quality, convergence speed and stability. Furthermore, GTOA is used to solve four constrained engineering design optimization problems in the real world. Simulation results demonstrate the proposed GTOA can find better solutions with faster speed compared with the reported optimizers.