CONSTRUCTION OF MIXED COVERING ARRAYS OF STRENGTHS 2 THROUGH 6 USING A TABU SEARCH APPROACH

2012 ◽  
Vol 04 (03) ◽  
pp. 1250033 ◽  
Author(s):  
LORETO GONZALEZ-HERNANDEZ ◽  
NELSON RANGEL-VALDEZ ◽  
JOSE TORRES-JIMENEZ
Keyword(s):  
Tabu Search ◽  
Search Algorithm ◽  
Fine Tuning ◽  
Optimal Size ◽  
Software System ◽  
Covering Arrays ◽  
Combinatorial Structures ◽  
Finite Set ◽  
Best Parameter ◽  
Search Approach

The development of a new software system involves extensive tests of the software functionality in order to identify possible failures. Also, a software system already built requires a fine tuning of its configurable options to give the best performance in the environment where it is going to work. Both cases require a finite set of tests that avoids testing all the possible combinations (which is time consuming); to this situation mixed covering arrays (MCAs) are a feasible alternative. MCAs are combinatorial structures having a case per row. MCAs are small, in comparison with exhaustive search, and guarantee a level of interaction among the involved parameters (a difference with random testing). We present a tabu search algorithm (TSA) for the construction of MCAs. Also, we report the fine tuning process used to identify the best parameter values for TSA. The analyzed TSA parameters were three different initialization functions, five different tabu list sizes and the mixture of four neighborhood functions. The performance of TSA was evaluated with two benchmarks previously reported. The results showed that TSA improved the algorithms IPOG-F, ITCH, Jenny, TConfig, and TVG in relation with the size of the constructed matrices. Particularly, TSA found the optimal size in 20 of the 23 cases tested.

A Reinforced Tabu Search Approach for 2D Strip Packing

2012 ◽  
pp. 171-188
Author(s):  
Giglia Gómez-Villouta ◽  
Jean-Philippe Hamiez ◽  
Jin-Kao Hao
Keyword(s):  
Tabu Search ◽  
Search Algorithm ◽  
Fitness Function ◽  
Packing Problem ◽  
Two Dimensional ◽  
Strip Packing ◽  
Benchmark Instances ◽  
Finite Set ◽  
Infinite Height ◽  
Search Approach

This paper discusses a particular “packing” problem, namely the two dimensional strip packing problem, where a finite set of objects have to be located in a strip of fixed width and infinite height. The variant studied considers regular items, rectangular to be precise, that must be packed without overlap, not allowing rotations. The objective is to minimize the height of the resulting packing. In this regard, the authors present a local search algorithm based on the well-known tabu search metaheuristic. Two important components of the presented tabu search strategy are reinforced in attempting to include problem knowledge. The fitness function incorporates a measure related to the empty spaces, while the diversification relies on a set of historically “frozen” objects. The resulting reinforced tabu search approach is evaluated on a set of well-known hard benchmark instances and compared with state-of-the-art algorithms.

scholarly journals A Reinforced Tabu Search Approach for 2D Strip Packing

2010 ◽  
Vol 1 (3) ◽  
pp. 20-36 ◽  
Author(s):  
Giglia Gómez-Villouta ◽  
Jean-Philippe Hamiez ◽  
Jin-Kao Hao
Keyword(s):  
Tabu Search ◽  
Search Algorithm ◽  
Fitness Function ◽  
Packing Problem ◽  
Local Search Algorithm ◽  
Strip Packing ◽  
Benchmark Instances ◽  
Finite Set ◽  
Infinite Height ◽  
Search Approach

This paper discusses a particular “packing” problem, namely the two dimensional strip packing problem, where a finite set of objects have to be located in a strip of fixed width and infinite height. The variant studied considers regular items, rectangular to be precise, that must be packed without overlap, not allowing rotations. The objective is to minimize the height of the resulting packing. In this regard, the authors present a local search algorithm based on the well-known tabu search metaheuristic. Two important components of the presented tabu search strategy are reinforced in attempting to include problem knowledge. The fitness function incorporates a measure related to the empty spaces, while the diversification relies on a set of historically “frozen” objects. The resulting reinforced tabu search approach is evaluated on a set of well-known hard benchmark instances and compared with state-of-the-art algorithms.

scholarly journals Solution-Guided Multi-Point Constructive Search for Job Shop Scheduling

2007 ◽  
Vol 29 ◽  
pp. 49-77 ◽  
Author(s):  
J. C. Beck
Keyword(s):  
Tabu Search ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Algorithm ◽  
Benchmark Problems ◽  
Performance Impact ◽  
Shop Scheduling ◽  
Resource Limited ◽  
Best Parameter ◽  
Better Than

Solution-Guided Multi-Point Constructive Search (SGMPCS) is a novel constructive search technique that performs a series of resource-limited tree searches where each search begins either from an empty solution (as in randomized restart) or from a solution that has been encountered during the search. A small number of these "elite'' solutions is maintained during the search. We introduce the technique and perform three sets of experiments on the job shop scheduling problem. First, a systematic, fully crossed study of SGMPCS is carried out to evaluate the performance impact of various parameter settings. Second, we inquire into the diversity of the elite solution set, showing, contrary to expectations, that a less diverse set leads to stronger performance. Finally, we compare the best parameter setting of SGMPCS from the first two experiments to chronological backtracking, limited discrepancy search, randomized restart, and a sophisticated tabu search algorithm on a set of well-known benchmark problems. Results demonstrate that SGMPCS is significantly better than the other constructive techniques tested, though lags behind the tabu search.

Evolutionary Approaches for the Extraction of Classification Rules

2014 ◽  
Vol 5 (1) ◽  
pp. 1-19
Author(s):  
Sadjia Benkhider ◽  
Ahmed Riadh Baba-Ali ◽  
Habiba Drias
Keyword(s):  
Tabu Search ◽  
Search Algorithm ◽  
Substantial Reduction ◽  
Classification Rules ◽  
Running Time ◽  
Parallel Scheme ◽  
Knowing That ◽  
Human Interpretation ◽  
Number Of Cycles ◽  
Search Approach

This paper provides evolutionary approaches in order to extract comprehensible and accurate classification rules. Indeed to construct a model of classification tone must extract not only accurate rules but comprehensible also, to help the human interpretation of the model and the decision make process. In this paper the authors describe a purely genetic approach, then a tabu search approach and finaly a memetic algorithm to extract classification rules. The memetic approach is a hybridization of a genetic algorithm (GA) and a local search based on a tabu search algorithm. Knowing that the amount of treated data is always huge in data mining applications, the authors propose to decrease the running time of the GA using a parallel scheme. In the authors' scheme the concept of generation has been removed and replaced by the cycle one and each individual owns a lifespan represented by a number of cycles affected to it randomly at its birth and at the end of which it disappears from the population. Consequently, only certain individuals of the population are evaluated within each iteration of the algorithm and not all our heterogeneous population. This causes the substantial reduction of the total running time of the algorithm since the evaluations of all individuals of each generation necessitates more than 80% of the total running time of a classical GA. This approach has been developed with the goal to present a new and efficient parallel scheme of the classical GA with better performances in terms of running time.

Determination of Optimal Public Transportation Routes Using Firefly and Tabu Search Algorithms

2020 ◽  
Vol 4 (5) ◽  
pp. 884-891
Author(s):  
Salwa Salsabila Mansur ◽  
Sri Widowati ◽  
Mahmud Imrona
Keyword(s):  
Tabu Search ◽  
Public Transportation ◽  
Traffic Congestion ◽  
Search Algorithm ◽  
Urban Transportation ◽  
The Public ◽  
Comparison Results ◽  
Congestion Problems ◽  
Transportation Routes

Traffic congestion problems generally caused by the increasing use of private vehicles and public transportations. In order to overcome the situation, the optimization of public transportation’s route is required particularly the urban transportation. In this research, the performance analysis of Firefly and Tabu Search algorithm is conducted to optimize eleven public transportation’s routes in Bandung. This optimization aims to increase the dispersion of public transportation’s route by expanding the scope of route that are crossed by public transportation so that it can reach the entire Bandung city and increase the driver’s income by providing the passengers easier access to public transportations in order to get to their destinations. The optimal route is represented by the route with most roads and highest number of incomes. In this research, the comparison results between the reference route and the public transportation’s optimized route increasing the dispersion of public transportation’s route to 60,58% and increasing the driver’s income to 20,03%.

scholarly journals A Modified Niching Crow Search Approach to Well Placement Optimization

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 857
Author(s):  
Jahedul Islam ◽  
Md Shokor A. Rahaman ◽  
Pandian M. Vasant ◽  
Berihun Mamo Negash ◽  
Ahshanul Hoqe ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Multimodal Optimization ◽  
Test Functions ◽  
Well Placement ◽  
Suggested Approach ◽  
Placement Optimization ◽  
Well Placement Optimization ◽  
Search Approach

Well placement optimization is considered a non-convex and highly multimodal optimization problem. In this article, a modified crow search algorithm is proposed to tackle the well placement optimization problem. This article proposes modifications based on local search and niching techniques in the crow search algorithm (CSA). At first, the suggested approach is verified by experimenting with the benchmark functions. For test functions, the results of the proposed approach demonstrated a higher convergence rate and a better solution. Again, the performance of the proposed technique is evaluated with well placement optimization problem and compared with particle swarm optimization (PSO), the Gravitational Search Algorithm (GSA), and the Crow search algorithm (CSA). The outcomes of the study revealed that the niching crow search algorithm is the most efficient and effective compared to the other techniques.

scholarly journals Multi-Resolution SPH Simulation of a Laser Powder Bed Fusion Additive Manufacturing Process

2021 ◽  
Vol 11 (7) ◽  
pp. 2962
Author(s):  
Mohamadreza Afrasiabi ◽  
Christof Lüthi ◽  
Markus Bambach ◽  
Konrad Wegener
Keyword(s):  
Search Algorithm ◽  
Computational Effort ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Neighbor Search ◽  
Particle Hydrodynamics ◽  
Sph Simulation ◽  
Particle Refinement ◽  
Search Approach

This paper presents an efficient mesoscale simulation of a Laser Powder Bed Fusion (LPBF) process using the Smoothed Particle Hydrodynamics (SPH) method. The efficiency lies in reducing the computational effort via spatial adaptivity, for which a dynamic particle refinement pattern with an optimized neighbor-search algorithm is used. The melt pool dynamics is modeled by resolving the thermal, mechanical, and material fields in a single laser track application. After validating the solver by two benchmark tests where analytical and experimental data are available, we simulate a single-track LPBF process by adopting SPH in multi resolutions. The LPBF simulation results show that the proposed adaptive refinement with and without an optimized neighbor-search approach saves almost 50% and 35% of the SPH calculation time, respectively. This achievement enables several opportunities for parametric studies and running high-resolution models with less computational effort.

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