Abstract
This paper presents a multi-objective algorithm based on tissue P system(MO TPS for short) for solving the tri-objective grain dispatching and transportation. This problem can be abstracted to solve the tri-objective VRPTW. In the algorithm, the cells of the tissue P system are divided into two groups. The first group, consisting of only one cell, aims at approaching to the Pareto front by the intelligent algorithm with non-domination rule while second group, consisting of six cells, focuses on searching boundaries by the artificial bee colony algorithm with different prioritization rules. The main idea of the MO TPS is about three aspects: search boundaries, approach to the Pareto front and approach to the Pareto front on the premise of preserving the elite boundary. 56 Solomon benchmarks are utilized to test algorithm performance. Experimental results show that on the premise of ensuring accuracy, the proposed approach outperforms compared algorithms in terms of three metrics.
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Acknowledgement
This work is partially supported by subproject of the National Key Research and Development Program of China (Grant No. 2017YFD0401102-02), Key Project of Philosophy and Social Science Research Project of Hubei Provincial Department of Education in 2019(19D59) and Science and Technology Research Project of Hubei Provincial Department of Education (D20191604).
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He, Z., Zhou, K., Shu, H., Zhou, J., Lyu, X., Li, G. (2021). Multi-Objective Algorithm Based on Tissue P System for Solving Tri-objective Grain Dispatching and Transportation. In: Pan, L., Pang, S., Song, T., Gong, F. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2020. Communications in Computer and Information Science, vol 1363. Springer, Singapore. https://doi.org/10.1007/978-981-16-1354-8_38
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