.png){kind=icon}
返回
Large-scale bound constrained optimization based on hybrid teaching learning optimization algorithm
.png){kind=icon}
OA
AI
DOI：10.1016/j.aej.2021.04.002.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Evolutionary computing is an exciting sub-field of soft computing. Many evolutionary algorithm based on the Darwinian principles of natural selection are developed under the umbrella of EC in the last two decades. EAs provide a set of optimal solutions in single simulation unlike traditional optimization techniques for dealing with large-scale global optimization and search problems. Teaching Learning based Optimization (TLBO) is one of the most recently developed EA. TLBO employs a group of learners or a class of learners to perform global optimization search process. The framework of the TLBO consists of two phases, including the Teacher Phase and Learner Phase. The Teacher Phase' means learning from the teachers and the Learner Phase means learning through interaction among learners. In this paper, we have developed a hybrid TLBO (HTLBO) with aim at to further improve the exploration and exploitation abilities of the baseline TLBO algorithm. The performance of the proposed HTLBO algorithm examined upon using recently designed benchmark functions for the special session of the CEC2017 problems. The experimental results of the proposed algorithm are better than some well-known evolutionary algorithms in terms of proximity and diversity. (C) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University.
Keyword:
Global optimization
Soft computing
Evolutionary computing
Evolutionary algorithms
Hybrid evolutionary algorithms
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:
6.8
论文数:
6.3K
被引数:
2.6W
机构
引用论文
.png){kind=icon}
Teaching-learning based optimization algorithm based fuzzy-PID controller for automatic generation control of multi-area power system基于教学优化算法的模糊PID控制器，用于多区域电力系统的自动发电控制
PREAI
分享收藏分享收藏
查看更多内容