Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Component allocation and partitioning for a dual delivery placement machine
Operations Research
Sequencing of insertions in printed circuit board assembly
Operations Research
Proceedings of the third international conference on Genetic algorithms
Genetic Algorithms and Manufacturing Systems Design
Genetic Algorithms and Manufacturing Systems Design
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators
Artificial Intelligence Review
A New Genetic Algorithm Using Large Mutation Rates and Population-Elitist Selection (GALME)
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
A fuzzy case-based reasoning model for sales forecasting in print circuit board industries
Expert Systems with Applications: An International Journal
ICICIC '08 Proceedings of the 2008 3rd International Conference on Innovative Computing Information and Control
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Editorial: The traveling salesman problem
Discrete Optimization
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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.