电路布置问题的交换算法

L. C. Cote, Arvind M. Patel
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引用次数: 6

摘要

本文讨论了交换程序在解决电路布置问题中的应用。提出了一种测量解质量的理论分析方法。两个交换算法(Algo I和Algo II)编程和测试中等规模的安置问题。Algo II是在有限计算结果的基础上对Algo i的改进。Algo II似乎在不增加计算成本的情况下提供了显著的改进。交换过程被广泛用于求解旅行商、设施位置、模块放置等组合问题的近似解。交换程序有许多变体。然而,这些变化通常限于为可能的交换选择元件的程序。不同的组合(解决方案)是通过一次只交换几个元素而系统地产生的。不能提高某些规范价值的组合被拒绝。一种能提高这一规范价值的组合被接受,人们试图找到另一种组合来进一步改进。这种情况一直持续下去,直到人们无法提高规范的价值。搜索在有限的步骤中终止。计算方面的考虑限制了测试只能成对交换元素。因此,只生成所有可能组合的一小部分,这通常会导致次优或局部解决方案。即使是非常大的问题(超过100个节点)的次优解决方案在计算上也是非常昂贵的。交换过程(如第4节所示)在计算时间和解的质量方面与其他启发式技术相当竞争。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Interchange Algorithms for Circuit Placement Problems
This paper discusses the applications of interchange procedures to solve circuit placement problems. A theoretical analysis to guage the quality of solutions is presented. Two interchange algorithms (Algo I and Algo II) are programmed and tested for moderate size placement problems. Algo II is an improved version of Algo I. On the basis of the limited computational results. Algo II seems to provide significant improvements without increased computation cost. The interchange procedure is widely utilized to find approximate solutions for combinatorial problems such as traveling salesman, facility locations, module placements, etc. There are many variations of the interchange procedure. However, the variations are generally limited to the procedure of selecting elements for possible exchanges. The different combinations (solutions) are systematically generated by interchanging only a few elements at a time. A combination which does not improve the value of some norm is rejected. A combination which improves the value of this norm is accepted and one tries to find another combination for further improvement. This is continued until one cannot improve the value of the norm. The search is terminated in a finite number of steps. Computational considerations limit the testing to only pairwise exchanges of elements. Thus, only a small subset of all the possible combinations are generated, and this generally results in suboptimum or local solutions. Even suboptimum solutions of very large problems (more than 100 nodes) can be computationally very expensive. Interchange procedures (as shown in Section 4) compete quite well with other heuristic techniques in regard to both computational time and quality of solutions.
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