EB-GLS：一种改进的基于大谷结构的引导局部搜索算法
EB-GLS: An Improved Guided Local Search Based on the Big Valley Structure
摘要：
局部搜索是模因算法中的基本构建块。引导局部搜索（Guided Local Search，GLS）可以提高局部搜索的效率。通过改变引导函数，广义最小二乘引导局部搜索跳出局部最优解，寻找更优解。GLS的关键组成部分是它的惩罚机制，它决定了当搜索陷入局部最优解时选择哪个特征进行惩罚。原始的GLS惩罚机制仅利用每个特征的代价和当前惩罚值。众所周知，许多组合优化问题具有大的谷结构，即，解越好，它越有可能接近全局最优解。本文提出利用大谷结构假设来改进GLS惩罚机制。提出了一种改进的GLS算法-精英偏置GLS（EB-GLS）。EB-GLS记录并维护精英解作为全局最优解的估计，并减少了惩罚该解中的特征的机会。我们已经系统地测试了对称旅行商问题的算法。实验结果表明，EB-GLS明显优于GLS。
英文摘要：
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.
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