Cluster Computing

, Volume 22, Supplement 2, pp 3271–3284 | Cite as

An optimized IS-APCPSO algorithm for large scale complex traffic network

  • Ke Huang
  • Hao Lan ZhangEmail author
  • Gelan Yang


Chaotic particle swarm optimization algorithm is improved by incorporating antibody concentration, adaptive propagation, optimization mechanism of the multi-population evolution strategy, elite particles chaotic traversal mechanism and constraint processing mechanism. In this paper, an improved adaptive propagation chaotic particle swarm optimization algorithm based on immune selection (IS-APCPSO algorithm for short) is proposed. The performance of several algorithms has been compared by multimodal function, functions with high dimensional and complex constraints, bi-level programming function and a classic example of traffic network optimization. The experimental results prove that the proposed algorithm in accelerating convergence rate, increasing the diversity of particles, and preventing premature phenomenon is effective. The novel algorithm is expected to be used in the model solution of large-scale complex traffic network optimization problem.


Optimization IS-APCPSO algorithm Traffic network Immune selection 



This research was supported by Zhejiang Provincial Natural Science Foundation of China under Grant No. LQ17G030007. This research was supported by Ningbo Soft Science Project under Grant No. 2017A10070, Ningbo Innovation Team (Grant No. 2016C11024), Zhejiang Provincial Natural Science Foundation of China under Grant No. LY14G010004, and National Natural Science Foundation of China under Grant No. 61272480.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.SCDM CenterNingbo Institute of Technology Zhejiang UniversityNingboChina
  2. 2.School of Information Science and EngineeringHunan City UniversityHeshanChina

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