Under addressing global competition, manufacturing companies strive to produce better and cheaper products more quickly. For a complex production system, the design problem is intrinsically a daunting optimization task often involving multiple disciplines, nonlinear mathematical model, and computation-intensive processes during manufacturing process. Here is a reason to develop a high performance algorithm for finding an optimal solution to the engineering design and/or optimization problems. In this paper, a hybrid metaheuristic approach is proposed for solving engineering optimization problems. A genetic algorithm (GA), particle swarm optimization (PSO), and teaching and learning-based optimization (TLBO), called the GA-PSO-TLBO approach, is used and demonstrated for the proposed hybrid metaheuristic approach. Since each approach has its strengths and weaknesses, the GA-PSO-TLBO approach provides an optimal strategy that maintains the strengths as well as mitigates the weaknesses, as needed. The performance of the GA-PSO-TLBO approach is compared with those of conventional approaches such as single metaheuristic approaches (GA, PSO and TLBO) and hybrid metaheuristic approaches (GA-PSO and GA-TLBO) using various types of engineering optimization problems. An additional analysis for reinforcing the performance of the GA-PSO-TLBO approach was also carried out. Experimental results proved that the GA-PSO-TLBO approach outperforms conventional competing approaches and demonstrates high flexibility and efficiency.
Keywords: engineering optimization problem; genetic algorithm; hybrid metaheuristic approach; particle swarm optimization; teaching and learning-based optimization.