Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (5): 212-220.doi: 10.11896/jsjkx.210300019

• Artificial Intelligence • Previous Articles     Next Articles

Multimodal Multi-objective Optimization Based on Parallel Zoning Search and Its Application

LI Hao-dong1, HU Jie1, FAN Qin-qin1,2   

  1. 1 Logistics Research Center,Shanghai Maritime University,Shanghai 201306,China
    2 School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China
  • Received:2021-03-02 Revised:2021-08-08 Online:2022-05-15 Published:2022-05-06
  • About author:LI Hao-dong,born in 1997,postgra-duate.His main research interests include multimodal multi-objective optimization and evolutionary computation.
    FAN Qin-qin,born in 1986,associate professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include multi-objective optimization,machine learning and evolutionary computation.
  • Supported by:
    National Natural Science Foundation of China-Shandong Joint Fund(U2006228), National Social Science Foundation of China(18BGL103),National Natural Science Foundation of China(61603244,71904116) and Shanghai Science and Technology Commission(19DZ1209600,18DZ1201500).

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Abstract: Multimodal multi-objective optimization based on zoning search belongs to a decision space decomposition strategy,thus it has natural parallelism.To improve the solution efficiency,a parallel zoning search (PZS) using the parallel computing technique is proposed in this paper .In the PZS,the entire search space of multimodal multi-objective optimization problem is firs-tly divided into many subspaces,and then a selected multimodal multi-objective evolutionary algorithm is used to independently search each subregion via the parallel computing method.Finally,equivalent solutions are selected from solutions of all subspaces.To verify the effectiveness of the proposed method,two experiments are executed in the current study.The first experiment is that all compared algorithms use the same run time,the other is that all compared algorithms use the same number of function evaluation. The results show that the proposed method can effectively assist the selected multimodal multi-objective evolutionary algorithm in improving the quality of solutions in the decision space under the same calculation time,and can save the computational time under the same number of function evaluations.The multimodal multi-objective evolutionary algorithm combined with PZS is also used to solve the multimodal multi-objective problem of energy consumption of sea-rail intermodal transportation,in which carbon emission is considered.The obtained results can provide decision support for the environmental protection and transportation time issues in the sea-rail intermodal transportation.

Key words: Green shipping, High-performance computing, Multimodal multi-objective optimization, Sea-Rail intermodal transportation, Zoning search

CLC Number: 

  • TP311
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