AN IMPROVED ADAPTIVE PROPAGATION CHAOTIC PARTICLE SWARM OPTIMIZATION ALGORITHM BASED ON IMMUNE SELECTION

被引：0
|
作者：
Huang, Ke [1 ]
Zhang, Haolan [1 ]
Wang, Yiwen [1 ]
Yu, Changbin [1 ]
机构：
[1] Zhejiang Univ, Ningbo Inst Technol, Ningbo 315100, Zhejiang, Peoples R China
来源：
PROCEEDINGS OF 2017 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOL 1 | 2017年
关键词：
Particle swarm optimization algorithm; Immune selection; Adaptive propagation; Multi-population evolution; Traffic network optimization; MODEL;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The particle swarm optimization algorithm is improved by introducing the immune selection, adaptive propagation, multi-population evolution. An improved adaptive propagation chaotic particle swarm optimization algorithm based on immune selection (IS-APCPSO algorithm for short) is proposed in this paper. The performance of several algorithms has been compared by a classic example of traffic network optimization. It is proved that the improved 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.
引用
收藏
页码：105 / 110
页数：6
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