Journal
Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.promfg.2018.10.112
Keywords
Genetic algorithm; Particle swarm; Ant colony; Shuffled frog leaping; Optimization
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This paper applies four population-based classical meta-heuristic algorithms to solve a pick-and-place optimization problem for a surface mounter in a PCB assembly environment. A mathematical model of this optimization problem is formulated as an integrated problem of the capacitated vehicle routing problem and the quadratic assignment problem, which are well-known NP-hard problems. A brief description of each method is presented and special operators for the integer encoded solutions are developed. Ten real-world PCB samples are tested and optimized using all the four algorithms. The experiment results show that the genetic algorithm has better performance than the others in terms of solution quality, especially the deviation of results from multiple trials, and computation time. (C) 2018 The Authors. Published by Elsevier B.V.
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