Advances in Natural Science
Improved Adaptive Genetic Algorithm in Optimal Layout of Leather Rectangular Parts
Abstract
In the mass customization of Leather products (such as sofa), the intelligent layout is the key technology to improve material utilization. The paper faces artificial leather fabric cutting problem, most can be converted into a rectangle packing layout problem. This paper proposes a new improved adaptive genetic algorithm. Crossover and mutation probability of genetic algorithm adaptively adjust on the basis of logistic curve equation and the shortcomings of traditional adaptive genetic algorithm solved well. The remaining rectangle algorithm as the decoding algorithm and adopting New cross-ways, the niche technology controlled whether the child individual replacement the parent individual or not accelerating convergence rate. Examples show that the algorithm of leather fabrics nesting is effective and a substantial increase in the utilization of leather fabric.
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Cao, J., & Zhou, J. (1994). Rectangular nesting of backpack algorithm. Chinese Mechanical Engineering, 5(2), 11-12.
Chen, C. (2011). Improved adaptive genetic algorithm and its application. Guangzhou, South China University of Technology.
Dong, D. W., & Yan, Y. H. (2013). Adaptive genetic simulated annealing algorithm in optimal layout of rectangular parts. China Mechanical Engineering, 18(24), 2499-2504.
Huang, L., Qi, J., & Tan, Y. (2012). A genetic-discrete paticle swarm optimization algorithm for rectangular packing. Chinese Journal of Electronics, 6(40), 1103-1107.
Jaya Thomas·Narendra, S. C. (2014). A new metaheuristic genetic-based placement algorithm for 2D strip packing. Springerlink, 47(9), 1-16
Jia, Z. X., Yin, G. F., & Luo, Y. (2002). Two-dimensional irregular parts packing with genetic algorithm. Journal of Computer-Aided Design & Computer Graphics, 5(14), 467-470.
Jiang, X. B., & Lu, X. Q. (2008). Optional packing of rectangles with an adaptive simulated annealing genetic algorithm. Journal of Computer-Aided Design & Computer Graphics, 11(20), 1426-1431.
Lee, C. P., Lin, W. S., & Chen, Y. M., et al. (2011). Gene selection and sample classification on microarray data based on adaptive genetic algorithm/k-nearest neighbor method. Expert Systems With Applications, 38(5), 4661-4667.
Patnailklm, S. M. (1994). Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Transaction on System, Man and Cybernetics, 24(4), 656-667.
Reda, M. S., & Abd, E. A. (1994). An interactive technique for cutting stock problem with multiple objects. European Journal of Operational Research, 78(3), 304-317.
Ren, Z. W., & San, Y. (2006). Improved adaptive genetic algorithm and its application research in parameter identification. Journal of System Simulation, 1(18), 41-44.
Tang, K. W., & Tang, W. K. S. (2005). Metal cutting with hybrid genetic algorithmic (pp. 735-739). Proceedings of the 3rd IEEE International Conference Industrial Informatics, Perth.
Thiruvady, D. R., Meyer, B., & Ernst, A. T. (2008). Strip packing with hybrid ACO: Placement order is learnable. IEEE Congress on Evolutionary Computation, 1207-1213.
DOI: http://dx.doi.org/10.3968/%25x
DOI (PDF): http://dx.doi.org/10.3968/%25x
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