.png){kind=icon}
返回
Evolving ant colony optimization based unit commitment
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2010.11.019.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Ant colony optimization (ACO) was inspired by the observation of natural behavior of real ants' pheromone trail formation and foraging. Ant colony optimization is more suitable for combinatorial optimization problems. ACO is successfully applied to the traveling salesman problem. Multistage decision making of ACO gives an edge over other conventional methods. This paper proposes evolving ant colony optimization (EACO) method for solving unit commitment (UC) problem. The EACO employs genetic algorithm (GA) for finding optimal set of ACO parameters, while ACO solves the UC problem. Problem formulation takes into consideration the minimum up and down time constraints, startup cost, spinning reserve, and generation limit constraints. The feasibility of the proposed approach is demonstrated on two different systems. The test results are encouraging and compared with those obtained by other methods. (C) 2010 Elsevier B.V. All rights reserved.
Keyword:
Evolving ant colony optimization
Unit commitment problem
Pheromone matrix
Genetic algorithm
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:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
.png){kind=icon}
Preoperative open field behavior predicts levels of neuropathic pain-related behavior in mice术前旷场行为预测小鼠神经病理性疼痛相关行为水平
PREAI
分享收藏
查看更多内容