A New Solution Approach for Grouping Problems Based on Evolution Strategies
A New Solution Approach for Grouping Problems Based on Evolution Strategies
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一种基于进化策略的分组问题求解新方法
DOI：
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发表时间：
2009
期刊：
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通讯作者：
Mina Husseinzadeh Kashan
中科院分区：
文献类型：
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作者：
A. H. Kashan;M. Jenabi;Mina Husseinzadeh Kashan
自1994年成立以来，分组遗传算法（GGA）是唯一的进化算法进行了大量修改，以适应分组问题的结构。在本文中，我们设计的进化策略（ES）的分组版本。众所周知，ES保持了高斯变异，重组和选择算子，用于优化非线性连续函数。因此，发展的分组进化策略（GES）解决分组问题的本质上是离散的，要求开发的运营商具有原始的主要特征，并响应分组问题的结构。我们提出了一个变异算子类似于原来的工作组，而不是标量，并使用它在一个两阶段的过程中产生新的解决方案。我们实现（1+ λ）-GES和评估其性能与GGA上的一些硬基准的装箱问题的实例。计算结果证明，我们的方法是有效的，可以被视为一个有前途的解决方案，为广泛的类分组问题。
Since its foundation in 1994, the grouping genetic algorithm (GGA) is the only evolutionary algorithm heavily modified to suit the structure of grouping problems. In this paper we design the grouping version of evolution strategies (ES). It is well-known that ES maintains a Gaussian mutation, recombination and a selection operator for optimizing non-linear continuous functions. Therefore, the development of grouping evolution strategies (GES) for solving grouping problems that are discrete in nature, calls for developing operators having the major characteristics of the original ones and being respondent to the structure of grouping problems. We propose a mutation operator analogous to the original one that works with groups instead of scalars and use it in a two phase procedure to generate the new solution. We implement (1+Lambda)-GES and evaluate its performance versus GGA on some of hard benchmarked instances of the bin packing problem. Computational results testify that our approach is efficient and can be regarded as a promising solver for the wide class of grouping problems.