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An Adaptive Brain Storm Optimization Algorithm Based on Heuristic Operators for TSP

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Bio-inspired Computing: Theories and Applications (BIC-TA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1159))

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Abstract

Traveling Salesman Problem (TSP) is a classical combinatorial optimization problem. This paper proposed an MDBSO (Modified Discrete Brain Storm Optimization) algorithm to solve TSP. The convex hull or greedy algorithm was introduced to initialize the population, which can improve the initial population quality. The adaptive inertial selection strategy was proposed to strengthen the global search in the early stage while enhancing the local search in the later stage. The heuristic crossover operator, which is based on the local superior genetic information of the parent to generate new individuals, was designed to improve the search efficiency of the algorithm. The experimental results of different benchmarks show that the MDBSO algorithm proposed in this paper significantly improves the convergence performance in solving TSP.

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Acknowledgment

This paper was supported by National Natural Science Foundation of China under Grant Number 2018YFB1703004.

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Authors and Affiliations

  1. School of Automation and Information Engineering, Xi’an University of Technology, Xi’an, Shaanxi, China

    Yali Wu, Xiaopeng Wang, Jinjin Qi & Liting Huang

Authors
  1. Yali Wu
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  2. Xiaopeng Wang
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  3. Jinjin Qi
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  4. Liting Huang
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Corresponding author

Correspondence to Yali Wu .

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Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  2. Zhengzhou University, Zhengzhou, China

    Jing Liang

  3. Zhongyuan University of Technology, Zhengzhou, China

    Boyang Qu

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Wu, Y., Wang, X., Qi, J., Huang, L. (2020). An Adaptive Brain Storm Optimization Algorithm Based on Heuristic Operators for TSP. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1159. Springer, Singapore. https://doi.org/10.1007/978-981-15-3425-6_52

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  • DOI: https://doi.org/10.1007/978-981-15-3425-6_52

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3424-9

  • Online ISBN: 978-981-15-3425-6

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