Developing a varietal GA with ESMA strategy for solving the pick and place problem in printed circuit board assembly line

The main issue for enhancing the productivity in Printed Circuit Board (PCB) is to reduce the cycle time for pick and place (PAP) operations; i.e., to minimize the time for the PAP operations. According to the characteristics of the PAP problems, the sequence for the placement of components can be mostly treated as the Travelling Salesman Problem (TSP). In this paper, a Genetic Algorithm (GA) with External Self-evolving Multiple Archives (ESMA) is developed for minimizing the PAP operations in PCB assembly line. ESMA focuses on the issue of improving the premature convergence time in GA by adopting efficient measures for population diversity, effective diversity control and mutation strategies to enhance the global searching ability. Three mechanisms for varietal GA such as Clustering Strategy, Switchable Mutation and Elitist Propagation have been designed based on the concept of increasing the dynamic diversity of the population. The experimental results in PCB and TSP instances show that the proposed approach is very promising and it contains the ability of local and global searching. The experimental results show ESMA can further improve the performance of GA by searching the solution space with more promising results.