Local search is a basic building block in memetic algorithms. Guided local search (GLS) can improve the efficiency of local search. By changing the guide function, GLS guides a local search to escape from locally optimal solutions and find better solutions. The key component of GLS is its penalizing mechanism which determines which feature is selected to penalize when the search is trapped in a locally optimal solution. The original GLS penalizing mechanism only makes use of the cost and the current penalty value of each feature. It is well known that many combinatorial optimization problems have a big valley structure, i.e., the better a solution is, the more the chance it is closer to a globally optimal solution. This paper proposes to use big valley structure assumption to improve the GLS penalizing mechanism. An improved GLS algorithm called elite biased GLS (EB-GLS) is proposed. EB-GLS records and maintains an elite solution as an estimate of the globally optimal solutions, and reduces the chance of penalizing the features in this solution. We have systematically tested the proposed algorithm on the symmetric traveling salesman problem. Experimental results show that EB-GLS is significantly better than GLS.

中文翻译：

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EB-GLS：基于大山谷结构的改进的引导式本地搜索

本地搜索是模因算法的基本构建块。引导式本地搜索（GLS）可以提高本地搜索的效率。通过更改向导功能，GLS可以指导本地搜索以摆脱本地最优解并找到更好的解决方案。GLS的关键组件是其惩罚机制，当搜索陷入局部最优解决方案中时，该机制将决定选择要惩罚的特征。原始的GLS惩罚机制仅利用成本和每个功能的当前惩罚值。众所周知，许多组合优化问题都具有较大的谷底结构，即解决方案越好，它越接近全局最优解。本文提出使用大谷结构假设来改善GLS的惩罚机制。提出了一种改进的GLS算法，称为精英偏置GLS（EB-GLS）。EB-GLS记录并维护一个精英解决方案，作为对全球最佳解决方案的估计，并减少了惩罚该解决方案中功能的机会。我们已经对对称旅行商问题进行了系统的测试。实验结果表明，EB-GLS明显优于GLS。