.png){kind=icon}
返回
A new hybrid method for solving global optimization problem
.png){kind=icon}
PRE
AI
DOI：10.1016/j.amc.2011.08.066.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper we present a new hybrid method, called the SASP method. The purpose of this method is the hybridization of the simulated annealing (SA) with the descent method, where we estimate the gradient using simultaneous perturbation. Firstly, the new hybrid method finds a local minimum using the descent method, then SA is executed in order to escape from the currently discovered local minimum to a better one, from which the descent method restarts a new local search, and so on until convergence. The new hybrid method can be widely applied to a class of global optimization problems for continuous functions with constraints. Experiments on 30 benchmark functions, including high dimensional functions, show that the new method is able to find near optimal solutions efficiently. In addition, its performance as a viable optimization method is demonstrated by comparing it with other existing algorithms. Numerical results improve the robustness and efficiency of the method presented. (C) 2011 Elsevier Inc. All rights reserved.
Keyword:
Global optimization
Hybrid method
Simultaneous perturbation
Simulated annealing
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:
3.4
论文数:
2.3W
被引数:
3.3W
机构
引用论文
.png){kind=icon}
A global optimization using linear relaxation for generalized geometric programming使用线性松弛进行广义几何规划的全局优化
PREAI
分享收藏Particle swarm optimization to solving the economic dispatch considering the generator constraints
OAAI
OAAI分享收藏A hybrid genetic algorithm and particle swarm optimization for multimodal functions一种求解多峰函数的混合遗传粒子群算法
PREAI
分享收藏
查看更多内容