返回
Wild Goats Algorithm: An Evolutionary Algorithm to Solve the Real-World Optimization Problems
DOI:10.1109/TII.2017.2779239.png)
摘要
En 中文
Solution of optimization problems is inseparable part of science and engineering. The close dependence of industry applications on science and engineering clarifies need to optimization algorithms for modern industries. In this paper, the proposition of an evolutionary optimization algorithm is presented. The proposed algorithm is inspired from wild goats' climbing. The living in the groups and cooperation between members of groups are main ideas which have been inspired. Along the procedure of the algorithm, leaders of groups attract group's other members and eventually the leader of the biggest group reaches the highest point of mountain. Besides examining with a number of benchmark functions, the performance of the algorithm is gone through by one of the energy systems' important problems, which is known as combined heat and power economic dispatch (CHPED) problem. The aim of the CHPED problem is supplying power and heat demand in an economical manner by conventional thermal units, CHP units, and heat-only units. The effect of valve-point and transmission losses is taken into account in order to consider practical CHPED model. The algorithm is tested on three test systems and the results show the ability of the algorithm to converge the optimum values.
Keyword:
Combined heat and power (CHP)
economic dispatch
evolutionary algorithm
optimization
wild goats
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:
9.9
论文数:
8.3K
被引数:
6.0W
机构
引用论文
Enhancement of combined heat and power economic dispatch using self adaptive real-coded genetic algorithm
APPLIED ENERGY
IF11
An adaptive particle swarm optimization method based on clustering一种基于聚类的自适应粒子群优化方法
SOFT COMPUTING
IF2.5
A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm一种求解约束工程优化问题的元启发式方法: 乌鸦搜索算法

