Parameterized Schemes of Metaheuristics: Basic Ideas and Applications With Genetic Algorithms, Scatter Search, and GRASP
Parameterized Schemes of Metaheuristics: Basic Ideas and Applications With Genetic Algorithms, Scatter Search, and GRASP
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DOI：
10.1109/tsmca.2012.2217322
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发表时间：
2013-02
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通讯作者：
F. Almeida;D. Giménez;J. López-Espín;Melquíades Pérez Pérez-Melquíades-Pérez-Pérez-2311409
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文献类型：
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作者：
F. Almeida;D. Giménez;J. López-Espín;Melquíades Pérez Pérez-Melquíades-Pérez-Pérez-2311409
有些优化问题只能用元启发式方法来解决，为了获得满意的元启发式方法，有必要开发和试验各种方法，并针对每个特定的问题进行调整。元启发式的统一方案的使用通过重新利用基本功能促进了元启发式的发展。在我们的方案中，通过增加过渡参数对统一方案进行了改进。这些参数被包括在每个函数中，以参数的不同值提供不同的元启发式或元启发式的组合的方式。因此，统一的参数化方案简化了元启发式算法的发展和应用。在这篇文章中，我们揭示了元启发式的参数化的基本思想。通过应用局部和全局搜索方法(贪婪随机化自适应搜索程序[GRASH]、遗传算法和分散搜索)及其组合，对三个科学问题进行了测试：从一组变量值获得满意的联立方程模型，具有独立任务和内存约束的任务到处理机分配问题，以及p-Hub中值位置分配问题。
Some optimization problems can be tackled only with metaheuristic methods, and to obtain a satisfactory metaheuristic, it is necessary to develop and experiment with various methods and to tune them for each particular problem. The use of a unified scheme for metaheuristics facilitates the development of metaheuristics by reutilizing the basic functions. In our proposal, the unified scheme is improved by adding transitional parameters. Those parameters are included in each of the functions, in such a way that different values of the parameters provide different metaheuristics or combinations of metaheuristics. Thus, the unified parameterized scheme eases the development of metaheuristics and their application. In this paper, we expose the basic ideas of the parameterization of metaheuristics. This methodology is tested with the application of local and global search methods (greedy randomized adaptive search procedure [GRASP], genetic algorithms, and scatter search), and their combinations, to three scientific problems: obtaining satisfactory simultaneous equation models from a set of values of the variables, a task-to-processor assignment problem with independent tasks and memory constrains, and the p-hub median location-allocation problem.