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首页> 外文期刊>International Journal of Innovative Computing Information and Control >A VARIANT OF DIFFERENTIAL EVOLUTION FOR DISCRETE OPTIMIZATION PROBLEMS REQUIRING MUTUALLY DISTINCT VARIABLES
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A VARIANT OF DIFFERENTIAL EVOLUTION FOR DISCRETE OPTIMIZATION PROBLEMS REQUIRING MUTUALLY DISTINCT VARIABLES

机译：需要互不相同的变量的离散优化问题的微分演化

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摘要

A large number of real world problems are formulated in terms of a set of discrete or integer variables for which an optimal set is obtained through appropriate optimization of a function. These problems are best represented using a set of discrete numbers over bounded or unbounded discrete spaces, in order to limit the search domain of the algorithm. In this work, Differential Evolution (DE) is used for the discrete problem, where the search space is augmented to improve the performance of the technique. Although in principal DE is used to find the optimal solution, the manner in which the space is stated and then searched is altered to improve the overall performance. Both unique and non-unique discrete sets of variables are investigated as control variables of the functions, and the algorithm for each is outlined accordingly. A number of established test functions are used to state the performance of the proposed DE discrete variable optimization technique, when compared to real space DE optimization.
机译：大量的现实世界问题是根据一组离散或整数变量来表述的，通过适当地优化函数可以获得一组最优值。这些问题最好用有界或无界离散空间上的一组离散数表示，以限制算法的搜索范围。在这项工作中，差分演化（DE）用于离散问题，其中扩大了搜索空间以提高该技术的性能。尽管原则上使用DE来找到最佳解决方案，但是更改了陈述空间然后进行搜索的方式，以提高整体性能。研究了变量的唯一和非唯一离散集作为函数的控制变量，并相应地概述了每个算法。与真实空间DE优化相比，许多已建立的测试函数用于陈述所提出的DE离散变量优化技术的性能。

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