.png){kind=icon}
返回
Adaptive Distributed Differential Evolution
.png){kind=icon}
OA
AI
DOI：10.1109/TCYB.2019.2944873.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Due to the increasing complexity of optimization problems, distributed differential evolution (DDE) has become a promising approach for global optimization. However, similar to the centralized algorithms, DDE also faces the difficulty of strategies' selection and parameters' setting. To deal with such problems effectively, this article proposes an adaptive DDE (ADDE) to relieve the sensitivity of strategies and parameters. In ADDE, three populations called exploration population, exploitation population, and balance population are co-evolved concurrently by using the master-slave multipopulation distributed framework. Different populations will adaptively choose their suitable mutation strategies based on the evolutionary state estimation to make full use of the feedback information from both individuals and the whole corresponding population. Besides, the historical successful experience and best solution improvement are collected and used to adaptively update the individual parameters (amplification factor F and crossover rate CR) and population parameter (population size N), respectively. The performance of ADDE is evaluated on all 30 widely used benchmark functions from the CEC 2014 test suite and all 22 widely used real-world application problems from the CEC 2011 test suite. The experimental results show that ADDE has great superiority compared with the other state-of-the-art DDE and adaptive differential evolution variants.
Keyword:
Sociology
Statistics
Optimization
Topology
Indexes
Next generation networking
Cybernetics
Adaptive distributed differential evolution (ADDE)
differential evolution (DE)
evolutionary state estimation (ESE)
historical successful experience (HSE)
master– slave multipopulation distributed
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
机构
引用论文
Distributed minimum spanning tree differential evolution for multimodal optimization problems多模态优化问题的分布式最小生成树差分进化算法.png)
SOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
分享收藏分享收藏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Local Binary Pattern-Based Adaptive Differential Evolution for Multimodal Optimization Problems基于局部二进制模式的自适应差分进化算法求解多峰优化问题OAAI
OAAI分享收藏Distributed evolutionary algorithms and their models: A survey of the state-of-the-art分布式进化算法及其模型: 最新技术综述OAAI
OAAI分享收藏A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms关于使用非参数统计检验作为比较进化和群体智能算法的方法的实用教程
PREAI
分享收藏
查看更多内容