Tabu Search directed by direct search methods for nonlinear global optimization
Tabu Search directed by direct search methods for nonlinear global optimization
复制标题
DOI：
10.1016/j.ejor.2004.05.033
复制
发表时间：
2006-04-16
影响因子：
6.4
通讯作者：
Fukushima, M
中科院分区：
文献类型：
--
作者：
Hedar, AR;Fukushima, M
近年来，在优化社区中对元分析有很大的兴趣。禁忌搜索（Tabu Search，TS）是一种流行的元搜索算法。然而，与遗传算法、模拟退火算法等元算法相比，TS在处理连续问题方面的贡献还很有限。在本文中，我们介绍了一个连续TS称为定向禁忌搜索（TBT）方法。在禁忌搜索方法中，使用基于直接搜索的策略来指导禁忌搜索。这些策略是基于著名的Nelder-Mead方法和一个新的模式搜索过程称为自适应模式搜索。此外，我们引入了一个新的禁忌表的概念与反循环规则称为禁忌区域和半禁忌区域。此外，多样化和集约化搜索计划。数值结果表明，该方法是有前途的，并产生高质量的解决方案。(c)2004 Elsevier B. V.保留所有权利。
In recent years, there has been a great deal of interest in metaheuristics in the optimization community. Tabu Search (TS) represents a popular class of metaheuristics. However, compared with other metaheuristics like genetic algorithm and simulated annealing, contributions of TS that deals with continuous problems are still very limited. In this paper, we introduce a continuous TS called Directed Tabu Search (DTS) method. In the DTS method, direct-search-based strategies are used to direct a tabu search. These strategies are based on the well-known Nelder-Mead method and a new pattern search procedure called adaptive pattern search. Moreover, we introduce a new tabu list conception with anti-cycling rules called Tabu Regions and Semi-Tabu Regions. In addition, Diversification and Intensification Search schemes are employed. Numerical results show that the proposed method is promising and produces high quality solutions. (c) 2004 Elsevier B.V. All rights reserved.