Simulated Annealing for Constructing Mixed Covering Arrays

2012 ◽  
pp. 657-664 ◽  
Author(s):  
Himer Avila-George ◽  
Jose Torres-Jimenez ◽  
Vicente Hernández ◽  
Loreto Gonzalez-Hernandez
Keyword(s):  
Simulated Annealing ◽  
Covering Arrays

scholarly journals A Simulated Annealing Algorithm to Construct Covering Perfect Hash Families

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Jose Torres-Jimenez ◽  
Idelfonso Izquierdo-Marquez
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Greedy Algorithms ◽  
Medium Size ◽  
Combinatorial Designs ◽  
Exploration And Exploitation ◽  
Covering Arrays ◽  
Annealing Algorithm ◽  
Perfect Hash Families

Covering perfect hash families (CPHFs) are combinatorial designs that represent certain covering arrays in a compact way. In previous works, CPHFs have been constructed using backtracking, tabu search, and greedy algorithms. Backtracking is convenient for small CPHFs, greedy algorithms are appropriate for large CPHFs, and metaheuristic algorithms provide a balance between execution time and quality of solution for small and medium-size CPHFs. This work explores the construction of CPHFs by means of a simulated annealing algorithm. The neighborhood function of this algorithm is composed of three perturbation operators which together provide exploration and exploitation capabilities to the algorithm. As main computational results we have the generation of 64 CPHFs whose derived covering arrays improve the best-known ones. In addition, we use the simulated annealing algorithm to construct quasi-CPHFs from which quasi covering arrays are derived that are then completed and postoptimized; in this case the number of new covering arrays is 183. Together, the 247 new covering arrays improved the upper bound of 683 covering array numbers.

Using simulated annealing for computing cost-aware covering arrays

2016 ◽  
Vol 49 ◽  
pp. 1129-1144 ◽  
Author(s):  
Gulsen Demiroz ◽  
Cemal Yilmaz
Keyword(s):  
Simulated Annealing ◽  
Covering Arrays

scholarly journals New bounds for binary covering arrays using simulated annealing

2012 ◽  
Vol 185 (1) ◽  
pp. 137-152 ◽  
Author(s):  
Jose Torres-Jimenez ◽  
Eduardo Rodriguez-Tello
Keyword(s):  
Simulated Annealing ◽  
Covering Arrays

scholarly journals Metaheuristic algorithms for building Covering Arrays: A review

2016 ◽  
Vol 25 (43) ◽  
pp. 31-45
Author(s):  
Jimena Adriana Timaná-Peña ◽  
Carlos Alberto Cobos-Lozada ◽  
Jose Torres-Jimenez
Keyword(s):  
Simulated Annealing ◽  
Harmony Search ◽  
Test Cases ◽  
Covering Arrays ◽  
Swarm Algorithms ◽  
Ant Colony Algorithms ◽  
Logical Unit ◽  
Minimum Number ◽  
Np Complete ◽  
Selection Of

Covering Arrays (CA) are mathematical objects used in the functional testing of software components. They enable the testing of all interactions of a given size of input parameters in a procedure, function, or logical unit in general, using the minimum number of test cases. Building CA is a complex task (NP-complete problem) that involves lengthy execution times and high computational loads. The most effective methods for building CAs are algebraic, Greedy, and metaheuristic-based. The latter have reported the best results to date. This paper presents a description of the major contributions made by a selection of different metaheuristics, including simulated annealing, tabu search, genetic algorithms, ant colony algorithms, particle swarm algorithms, and harmony search algorithms. It is worth noting that simulated annealing-based algorithms have evolved as the most competitive, and currently form the state of the art.

scholarly journals New Bounds for Ternary Covering Arrays Using a Parallel Simulated Annealing

2012 ◽  
Vol 2012 ◽  
pp. 1-19 ◽  
Author(s):  
Himer Avila-George ◽  
Jose Torres-Jimenez ◽  
Vicente Hernández
Keyword(s):  
Simulated Annealing ◽  
Direct Methods ◽  
Combinatorial Structure ◽  
Covering Array ◽  
Covering Arrays ◽  
Construction Problem ◽  
Cooperative Approach ◽  
Benchmark Instances ◽  
Best Execution ◽  
Greedy Methods

A covering array (CA) is a combinatorial structure specified as a matrix ofNrows andkcolumns over an alphabet onvsymbols such that for each set oftcolumns everyt-tuple of symbols is covered at least once. Given the values oft,k, andv, the optimal covering array construction problem (CAC) consists in constructing a CA (N;t,k,v) with the minimum possible value ofN. There are several reported methods to attend the CAC problem, among them are direct methods, recursive methods, greedy methods, and metaheuristics methods. In this paper, There are three parallel approaches for simulated annealing: the independent, semi-independent, and cooperative searches are applied to the CAC problem. The empirical evidence supported by statistical analysis indicates that cooperative approach offers the best execution times and the same bounds as the independent and semi-independent approaches. Extensive experimentation was carried out, using 182 well-known benchmark instances of ternary covering arrays, for assessing its performance with respect to the best-known bounds reported previously. The results show that cooperative approach attains 134 new bounds and equals the solutions for other 29 instances.

Penjadwalan Produksi Menggunakan Metode Simulated Annealing pada Unit Produksi Daun Pintu di PT. ML

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Rosnani Ginting ◽  
Chairul Rahmadsyah Manik
Keyword(s):  
Simulated Annealing ◽  
Problem Solving ◽  
Production Scheduling ◽  
Production Control ◽  
Delivery Time ◽  
Production Schedule ◽  
A Company ◽  
Predetermined Time ◽  
Factory Floor

Penjadwalan merupakan aspek yang sangat penting karena didalamnya terdapat elemen perencanaan dan pengendalian produksi bagi suatu perusahaan yang dapat mengirim barang sesuai dengan waktu yang telah ditentukan, untuk memperoleh waktu total penyelesaian yang minimum. Masalah utama yang dihadapi oleh PT. ML adalah keterlambatan penyelesaian order yang mempengaruhi delivery time ke tangan costumer karena pelaksanaan penjadwalan produksi dilantai pabrik belum menghasilkan makespan yang sesuai dengan order yang ada. Oleh kaena itu dituntut untuk mencari solusi pemecahan masalah optimal dalam penentuan jadwal produksi untuk meminimisasi total waktu penyelessaian (makespan) semua order. Dalam penelitian ini, penjadwalan menggunakan metode Simulated Annealing (SA) diharapkan dapat menghasilkan waktu total penyelesaian lebih cepat dari penjadwalan yang ada pada perusahaan.   Scheduling is a very important aspect because in it there are elements of planning and production control for a company that can send goods in accordance with a predetermined time, to obtain a minimum total time of completion. The main problem faced by PT. ML is the delay in completing orders that affect delivery time to customer because the implementation of production scheduling on the factory floor has not produced the makespan that matches the existing order. Therefore, it is required to find optimal problem solving solutions in determining the production schedule to minimize the total time of elimination (makespan) of all orders. In this study, scheduling using the Simulated Annealing (SA) method is expected to produce a total time of completion faster than the existing scheduling in the company.

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