Abstract
That combining membrane computing with optimization technology offers a new information interaction model for the research of problems in optimization filed. Based on this, a membrane algorithm owned six basic membranes is proposed to solve the defects of the slow convergence and the small diversity in solving vehicle routing problem with time window. In order to further improve the efficiency and the precision, some new rules are designed: for the former problem, a node classifier is introduced to improve the efficiency by filtering directly a plenty of in-feasible solutions; two methods for the latter problems: an uncertain segment crossover is designed in the corresponding membrane in order to explore directly two feasible segments and segment-node insertion operation is introduced in order to make two individuals inserted synchronously another path. In order to verify the effectiveness of the algorithm, a series of experiments are designed. Known through the results of experiments that these two properties of membranes make the search ability of the algorithm improving quickly for local and global exploration and node classifier improves effectively the running efficiency of this algorithm, which proves that membrane algorithm can accelerate the convergence speed and increase diversity of population.
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Acknowledgments
This project was supported by National Natural Science Foundation of China (Grant No. 61179032), the Special Scientific Research Fund of Food Public Welfare Profession of China (Grant No. 201513004-3) and the Research and Practice Project of Graduate Education Teaching Reform of Wuhan Polytechnic University (YZ2015002).
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Duan, Y., Zhou, K., Qi, H., Zhang, Z. (2016). Membrane Algorithm with Genetic Operation and VRPTW-Based Public Optimization System. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 681. Springer, Singapore. https://doi.org/10.1007/978-981-10-3611-8_13
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DOI: https://doi.org/10.1007/978-981-10-3611-8_13
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