Purpose

Metaheuristic algorithms are classified into two categories namely: single-solution and population-based algorithms. Single-solution algorithms perform local search process by employing a single candidate solution trying to improve this solution in its neighborhood. In contrast, population-based algorithms guide the search process by maintaining multiple solutions located in different points of search space. However, the main drawback of single-solution algorithms is that the global optimum may not reach and it may get stuck in local optimum. On the other hand, population-based algorithms with several starting points that maintain the diversity of the solutions globally in the search space and results are of better exploration during the search process. In this paper more chance of finding global optimum is provided for single-solution-based algorithms by searching different regions of the search space.

Design/methodology/approach

In this method, different starting points in initial step, searching locally in neighborhood of each solution, construct a global search in search space for the single-solution algorithm.

Findings

The proposed method was tested based on three single-solution algorithms involving hill-climbing (HC), simulated annealing (SA) and tabu search (TS) algorithms when they were applied on 25 benchmark test functions. The results of the basic version of these algorithms were then compared with the same algorithms integrated with the global search proposed in this paper. The statistical analysis of the results proves outperforming of the proposed method. Finally, 18 benchmark feature selection problems were used to test the algorithms and were compared with recent methods proposed in the literature.

Originality/value

In this paper more chance of finding global optimum is provided for single-solution-based algorithms by searching different regions of the search space.

中文翻译：

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基于单解决方案的元启发式算法的全局搜索

目的

元启发式算法分为两类：单解算法和基于总体的算法。单解决方案算法通过采用单个候选解决方案来尝试在其附近改进该解决方案，从而执行本地搜索过程。相反，基于种群的算法通过维护位于搜索空间不同点的多个解决方案来指导搜索过程。但是，单解算法的主要缺点是可能无法达到全局最优，并且可能陷入局部最优。另一方面，基于种群的算法具有多个起点，可以在搜索空间中全局维护解决方案的多样性，并且可以在搜索过程中更好地探索结果。

设计/方法/方法

在这种方法中，初始步骤的不同起点是在每个解决方案的邻域中进行本地搜索，然后在搜索空间中构造用于单解决方案算法的全局搜索。

发现

该方法是基于三种单解算法进行测试的，其中包括爬坡（HC），模拟退火（SA）和禁忌搜索（TS）算法在25种基准测试功能上的应用。然后将这些算法的基本版本的结果与本文提出的与全局搜索集成的相同算法进行比较。结果的统计分析证明了该方法的性能。最后，使用18个基准特征选择问题来测试算法，并将其与文献中提出的最新方法进行比较。

创意/价值

在本文中，通过搜索搜索空间的不同区域，为基于单解的算法提供了寻找全局最优的更多机会。