scholarly journals TABU SEARCH APPROACH FOR TYPE 2 PROBLEMS OF ASSEMBLY LINE BALACING

2011 ◽  
Vol 14 (2) ◽  
pp. 22-28
Author(s):  
Hung Vo Duong
Keyword(s):  
Problem Solving ◽  
Tabu Search ◽  
Assembly Line ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Heuristic Method ◽  
Tabu Search Algorithm ◽  
Combinatorial Optimization Problems

In this research, Tabu search algorithm, a heuristic method for solving combinatorial optimization problems, has been applied for type 2 problems of assembly line balancing. For type 2 problems, two methodologies are developed for problem solving. Method 1 is direct solving for type 2 problems, and method 2 gives solving through type 1 problems. As such, Tabu search algorithm for type 1 problem is employed for problem solving at second stage. The success of this research points out empty workstations (unnecessary) to reduce investment cost and operational costs. Moreover, the range of cycle time and number of workststions are provided for selection.

TABU SEARCH ALGORITHM FOR OPTIMIZING PILE FOUNDATION LAYOUT ON FOOTING OF RESIDENTIAL HOUSE

2015 ◽  
Vol 2 (1) ◽  
Author(s):  
Takahiro Hara ◽  
Buntara S. Gan
Keyword(s):  
Tabu Search ◽  
Pile Foundation ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Tabu Search Algorithm ◽  
Design Practice ◽  
Local Optima ◽  
Combinatorial Optimization Problems ◽  
Recent Advancement ◽  
Residential House

In practice, positioning pile foundation on footing layout of a residential house is often based on empirical judgment and long working experiences of a designer. After the pile requirements calculated, some conditions like number of piles, spacing placement of all piles on the footing, and locations such as under columns, corner, or intersection have to be determined. Besides, the coincidence of shear and gravity centers is also another essential design consideration, which is often ignored in design practice. The present study is aimed at eliminating the horizontal torsion effects during the earthquakes occurrences by reducing the eccentricity distance between the centers of gravity and shear. The scope of present study excludes any foundation or structural related design and analysis. Recent advancement of computing capabilities of computers has made the structural analysis become a convenient tool at hand for designers. In this study, the Tabu Search algorithm was adopted as an optimization tool for pile placing on the footing of residential houses. Tabu Search algorithm was known as an efficient method for solving combinatorial optimization problems where it can find some quality solutions in relatively short running time without getting stuck in local optima. An example of footing layout design was demonstrated to show the effectiveness of the present study as a decision-making tool in design practice.

The Ply Stacking Optimization for Composite Laminated Structure: Application of Tabu Search Algorithm

2011 ◽  
Vol 366 ◽  
pp. 514-517
Author(s):  
Jing Zhang
Keyword(s):  
Tabu Search ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Orientation Angle ◽  
Composite Fiber ◽  
Neighborhood Search ◽  
Optimized Design ◽  
Tabu Search Algorithm ◽  
Laminated Structure ◽  
Combinatorial Optimization Problems

In this paper, tabu search algorithm, the laminated overlay has been optimized. The total thickness of the laminated structure under certain circumstances, to composite laminates maximum system reliability indices as the objective function, the composite fiber orientation angle and relative thickness of the optimized design. It is through the local neighborhood search mechanism and the corresponding tabus to avoid circuitous search criteria and standards to cut through the broken tabu excellent release from the state, thereby ensuring a variety of effective exploration in order to ultimately achieve global optimization. This article is laminated overlay design provide a new and efficient framework, the framework of the algorithm relative to the previously used method has obvious advantages: After a limited number of cycles that can converge to satisfactory results, the optimization process shows good robustness of the algorithm, and the method to solve the structural optimization of composite materials and other combinatorial optimization problems provide a new way of thinking. This paper describes the basic idea of tabu search algorithm, composition, processes, principles, and so on.

scholarly journals Linking Search Space Structure, Run-Time Dynamics, and Problem Difficulty: A Step Toward Demystifying Tabu Search

2005 ◽  
Vol 24 ◽  
pp. 221-261 ◽  
Author(s):  
J. P. Watson ◽  
L. D. Whitley ◽  
A. E. Howe
Keyword(s):  
Random Walk ◽  
Tabu Search ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Search Space ◽  
Tabu Search Algorithm ◽  
Problem Difficulty ◽  
Time Dynamics ◽  
Combinatorial Optimization Problems ◽  
Run Time

Tabu search is one of the most effective heuristics for locating high-quality solutions to a diverse array of NP-hard combinatorial optimization problems. Despite the widespread success of tabu search, researchers have a poor understanding of many key theoretical aspects of this algorithm, including models of the high-level run-time dynamics and identification of those search space features that influence problem difficulty. We consider these questions in the context of the job-shop scheduling problem (JSP), a domain where tabu search algorithms have been shown to be remarkably effective. Previously, we demonstrated that the mean distance between random local optima and the nearest optimal solution is highly correlated with problem difficulty for a well-known tabu search algorithm for the JSP introduced by Taillard. In this paper, we discuss various shortcomings of this measure and develop a new model of problem difficulty that corrects these deficiencies. We show that Taillard's algorithm can be modeled with high fidelity as a simple variant of a straightforward random walk. The random walk model accounts for nearly all of the variability in the cost required to locate both optimal and sub-optimal solutions to random JSPs, and provides an explanation for differences in the difficulty of random versus structured JSPs. Finally, we discuss and empirically substantiate two novel predictions regarding tabu search algorithm behavior. First, the method for constructing the initial solution is highly unlikely to impact the performance of tabu search. Second, tabu tenure should be selected to be as small as possible while simultaneously avoiding search stagnation; values larger than necessary lead to significant degradations in performance.

A quantum-inspired Tabu search algorithm for solving combinatorial optimization problems

2013 ◽  
Vol 18 (9) ◽  
pp. 1771-1781 ◽  
Author(s):  
Hua-Pei Chiang ◽  
Yao-Hsin Chou ◽  
Chia-Hui Chiu ◽  
Shu-Yu Kuo ◽  
Yueh-Min Huang
Keyword(s):  
Combinatorial Optimization ◽  
Tabu Search ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Tabu Search Algorithm ◽  
Combinatorial Optimization Problems

Modified Firefly Algorithm With Chaos Theory for Feature Selection

2019 ◽  
Vol 10 (2) ◽  
pp. 1-20 ◽  
Author(s):  
Sujata Dash ◽  
Ruppa Thulasiram ◽  
Parimala Thulasiraman
Keyword(s):  
Feature Selection ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Heuristic Method ◽  
High Dimensional ◽  
Support Vector ◽  
Combinatorial Optimization Problems ◽  
Meta Search ◽  
Combined Algorithm

Conventional algorithms such as gradient-based optimization methods usually struggle to deal with high-dimensional non-linear problems and often land up with local minima. Recently developed nature-inspired optimization algorithms are the best approaches for finding global solutions for combinatorial optimization problems like microarray datasets. In this article, a novel hybrid swarm intelligence-based meta-search algorithm is proposed by combining a heuristic method called conditional mutual information maximization with chaos-based firefly algorithm. The combined algorithm is computed in an iterative manner to boost the sharing of information between fireflies, enhancing the search efficiency of chaos-based firefly algorithm and reduces the computational complexities of feature selection. The meta-search model is implemented using a well-established classifier, such as support vector machine as the modeler in a wrapper approach. The chaos-based firefly algorithm increases the global search mobility of fireflies. The efficiency of the model is studied over high-dimensional disease datasets and compared with standard firefly algorithm, particle swarm optimization, and genetic algorithm in the same experimental environment to establish its superiority of feature selection over selected counterparts.

Microcanonical Optimization Applied to the Traveling Salesman Problem

1998 ◽  
Vol 09 (01) ◽  
pp. 133-146 ◽  
Author(s):  
Alexandre Linhares ◽  
José R. A. Torreão
Keyword(s):  
Simulated Annealing ◽  
Traveling Salesman Problem ◽  
Optimization Problems ◽  
Search Algorithm ◽  
New Physics ◽  
Traveling Salesman ◽  
Tabu Search Algorithm ◽  
Combinatorial Optimization Problems ◽  
Microcanonical Annealing ◽  
The Traveling Salesman Problem

Optimization strategies based on simulated annealing and its variants have been extensively applied to the traveling salesman problem (TSP). Recently, there has appeared a new physics-based metaheuristic, called the microcanonical optimization algorithm (μO), which does not resort to annealing, and which has proven a superior alternative to the annealing procedures in various applications. Here we present the first performance evaluation of μO as applied to the TSP. When compared to three annealing strategies (simulated annealing, microcanonical annealing and Tsallis annealing), and to a tabu search algorithm, the microcanonical optimization has yielded the best overall results for several instances of the euclidean TSP. This confirms μO as a competitive approach for the solution of general combinatorial optimization problems.

scholarly journals Design of Flotation Circuits Using Tabu-Search Algorithms: Multispecies, Equipment Design, and Profitability Parameters

Minerals ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 181 ◽  
Author(s):  
Freddy Lucay ◽  
Edelmira Gálvez ◽  
Luis Cisternas
Keyword(s):  
Tabu Search ◽  
Approximate Method ◽  
Short Term Memory ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Adaptive Procedure ◽  
Problem Size ◽  
Combinatorial Optimization Problems

The design of a flotation circuit based on optimization techniques requires a superstructure for representing a set of alternatives, a mathematical model for modeling the alternatives, and an optimization technique for solving the problem. The optimization techniques are classified into exact and approximate methods. The first has been widely used. However, the probability of finding an optimal solution decreases when the problem size increases. Genetic algorithms have been the approximate method used for designing flotation circuits when the studied problems were small. The Tabu-search algorithm (TSA) is an approximate method used for solving combinatorial optimization problems. This algorithm is an adaptive procedure that has the ability to employ many other methods. The TSA uses short-term memory to prevent the algorithm from being trapped in cycles. The TSA has many practical advantages but has not been used for designing flotation circuits. We propose using the TSA for solving the flotation circuit design problem. The TSA implemented in this work applies diversification and intensification strategies: diversification is used for exploring new regions, and intensification for exploring regions close to a good solution. Four cases were analyzed to demonstrate the applicability of the algorithm: different objective function, different mathematical models, and a benchmarking between TSA and Baron solver. The results indicate that the developed algorithm presents the ability to converge to a solution optimal or near optimal for a complex combination of requirements and constraints, whereas other methods do not. TSA and the Baron solver provide similar designs, but TSA is faster. We conclude that the developed TSA could be useful in the design of full-scale concentration circuits.

A TABU SEARCH ALGORITHM FOR POST-PROCESSING MULTIPLE SEQUENCE ALIGNMENT

2005 ◽  
Vol 03 (01) ◽  
pp. 145-156 ◽  
Author(s):  
TARIQ RIAZ ◽  
WANG YI ◽  
KUO-BIN LI
Keyword(s):  
Tabu Search ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Solution Space ◽  
Adaptive Memory ◽  
Multiple Sequence ◽  
Local Optima ◽  
Combinatorial Optimization Problems

Tabu search is a meta-heuristic approach that is proven to be useful in solving combinatorial optimization problems. We implement the adaptive memory features of tabu search to refine a multiple sequence alignment. Adaptive memory helps the search process to avoid local optima and explores the solution space economically and effectively without getting trapped into cycles. The algorithm is further enhanced by introducing extended tabu search features such as intensification and diversification. The neighborhoods of a solution are generated stochastically and a consistency-based objective function is employed to measure its quality. The algorithm is tested with the datasets from BAliBASE benchmarking database. We have observed through experiments that tabu search is able to improve the quality of multiple alignments generated by other software such as ClustalW and T-Coffee. The source code of our algorithm is available at .

scholarly journals Penyelesaian Masalah TSP Pada Rute Kunjungan ATM Dengan Pendekatan Heuristik (Tabu Search)

2017 ◽  
Vol 2 (1) ◽  
pp. 13
Author(s):  
Jhon Pontas Simbolon ◽  
Muhammad Zarlis
Keyword(s):  
Tabu Search ◽  
Minimum Distance ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Heuristic Method ◽  
Search Process ◽  
Local Optimum ◽  
Heuristic Methods ◽  
Exact Methods

Determination of optimum route is a problem that can be found in a variety of activities. Principal of the problem is how to organize the trip so the distance is the minimum distance that the optimum is best found on a map or graph. There are many algorithms available to solve them. Algorithm is divided into two parts, the exact methods and heuristic methods. Heuristic method is considered the best method because it can work quickly. Tabu search is a heuristic method that is often used in solving optimization problems. The algorithm works by improving a solution by using memory to avoid that the search process does not get stuck at a local optimum value by rejecting new solutions that may be in memory (taboo) so that the new solution will be more dispersed. The author will implement a tabu search algorithm to provide a better alternative solution to solve the problems of the effectiveness of the distribution of charging money at the ATM machine.

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