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Vehicle power train optimization using multi-objective bird swarm algorithm

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Abstract

In this paper, a multi-objective bird swarm algorithm (MOBSA) is proposed to cope with multi-objective optimization problems. The algorithm is explored based on BSA which is an evolutionary algorithm suitable for single objective optimization. In this paper, non-dominated sorting approach is used to distinguish optimal solutions and parallel coordinates is applied to evaluate the distribution density of non-dominated solution and further update the external archive when it is full to overflowing, which ensure faster convergence and more widespread of Pareto front. Then, the MOBSA is adopted to optimize benchmark problems. The results demonstrate that MOBSA gets better performance compared with NSGA-II and MOPSO. Since a vehicle power train problem could be treated as a typical multi-objective optimization problem with constraints, with integration of constrained non-dominated solution, MOBSA is adopted to acquire optimal gear ratios and optimize vehicle power train. The results compared with other popular algorithm prove the proposed algorithm is more suitable for constrained multi-objective optimization problem in engineering field.

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Acknowledgements

The authors acknowledge support from National Nature Science Foundation of China (No. 61571236), Natural Science Foundation of Jiangsu Province (BK20130873, BK20160910). the Macau Science and Technology Fund (FDCT 093/2014/A2, 041/2017/A1), the Research Committee of University of Macau (MYRG2015-00011-FST, MYRG2015-00012-FST), Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX17_0795). Scientific Research Funds of Nanjing University of Posts and Telecommunications (NY214072 and NY215151).

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Authors and Affiliations

  1. Nanjing University of Posts and Telecommunications, Nanjing, China

    Dongmei Wu, Bin Xu, Hao Gao & Zhenghua Wu

  2. Department of Computer and Information Science, University of Macau, Macau, SAR, China

    Chi-Man Pun & Hao Gao

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  1. Dongmei Wu
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  2. Chi-Man Pun
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  3. Bin Xu
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  5. Zhenghua Wu
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Correspondence to Dongmei Wu.

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Wu, D., Pun, CM., Xu, B. et al. Vehicle power train optimization using multi-objective bird swarm algorithm. Multimed Tools Appl 79, 14319–14339 (2020). https://doi.org/10.1007/s11042-018-6522-3

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