.png){kind=icon}
返回
A hybrid co-evolutionary cultural algorithm based on particle swarm optimization for solving global optimization problems
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2011.08.043.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Intelligent evolutionary algorithms have been widely used to solve large-scale, complex global optimization problems. Co-evolutionary algorithm (CEA), cultural algorithm (CA), and particle swarm optimization (PSO) are all promising methods in the field of intelligent computation. In this paper, a hybrid co-evolutionary cultural algorithm based on particle swarm optimization (CECBPSO) is proposed. In CECBPSO, a novel space called shared global belief space (SGBS) is introduced into the co-evolutionary mechanism, and a new co-evolutionary cultural framework is built. Through the synergistic mechanism, the algorithm has higher probability of avoiding local optima and the whole swarm can find global optima more quickly. Factorial Design (FD) approach is used in this paper in order to get a guideline on how to tune the designed parameters in CECBPSO. Extensive computational studies are also carried out to evaluate the performance of CECBPSO on thirteen benchmark functions and three real-life optimization problems. The results show that the proposed algorithm has superior performance to other compared algorithms in terms of accuracy and convergence speed, especially on high-dimensional problems. (c) 2012 Elsevier B.V. All rights reserved.
Keyword:
Co-evolutionary algorithm
Cultural algorithm
Particle swarm optimization
Global optimization
Factorial design
Orthogonal test
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
暂无机构信息
引用论文
分享收藏分享收藏Revision ofPycnandrasubgenusSebertia(Sapotaceae) and a generic key to the family in New Caledonia.png)
Adansonia
OAAI
AdansoniaIF0
OAAI分享收藏Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization求解全局数值优化问题的策略自适应差分进化算法
OAAI
OAAI分享收藏Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
PREAI
分享收藏
查看更多内容