A Novel Multi-Objective Group Teaching Optimization Algorithm And Its Application To Engineering Design

This paper proposes a novel multi-objective algorithm by extending the recent group teaching optimization algorithm (GTOA) for the solution of multi-objective optimization problems. The proposed algorithm (designated as MOGTOA) is based on the Pareto dominance theory, and uses an external archive to guide the search direction of population. In order to strike a balance between exploration and exploitation of MOGTOA, a variety of improvements were made, including an improved teacher selection strategy, a modified student phase, and a hybrid mechanism of evolutionary search on the external archive population. The performance of the proposed algorithm was verified on 21 benchmark functions in comparison with six well established algorithms. The numerical results show that MOGTOA is comparable or even superior to other algorithms. In addition, the application of MOGTOA to two classic engineering design problems demonstrated its promising potential to solve practical problems.