.png){kind=icon}
返回
A Random Opposition-Based Learning Grey Wolf Optimizer
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2019.2934994.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Grey wolf optimizer (GWO) algorithm is a swarm intelligence optimization technique that is recently developed to mimic the hunting behavior and leadership hierarchy of grey wolves in nature. It has been successfully applied to many real world applications. In the GWO algorithm, C'' is an important parameter which favoring exploration. At present, the researchers are few study the parameter C'' in GWO algorithm. In addition, during the evolution process, the other individuals in the population move towards to the alpha, beta, and delta wolves which are to accelerate convergence. However, GWO is easy to trap in the local optima. This paper presents a modified parameter C'' strategy to balance between exploration and exploitation of GWO. Simultaneously, a new random opposition-based learning strategy is proposed to help the population jump out of the local optima. The experiments on 23 widely used benchmark test functions with various features, 30 benchmark problems from IEEE CEC 2014 Special Session, and three engineering design optimization problems. The results reveal that the proposed algorithm shows better or at least competitive performance against other compared algorithms on not only global optimization but also engineering design optimization problems.
Keyword:
Grey wolf optimizer
random opposition learning
global optimization
engineering design optimization
exploration
exploitation
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:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm蛾焰优化算法: 一种新的自然启发启发式范式
.png){kind=icon}
PREAI
分享收藏A New MPPT Design Using Grey Wolf Optimization Technique for Photovoltaic System Under Partial Shading Conditions基于灰狼优化技术的局部阴影条件下光伏系统MPPT设计
PREAI
分享收藏分享收藏
查看更多内容