scholarly journals Hybridizing Beam Search with Tabu Search for the Irregular Packing Problem

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yunqing Rao ◽  
Peng Wang ◽  
Qiang Luo
Keyword(s):  
Tabu Search ◽  
Hybrid Algorithm ◽  
Numerical Experiments ◽  
Search Algorithm ◽  
Packing Problem ◽  
Benchmark Problems ◽  
Beam Search ◽  
Hybrid Search

The irregular packing problem involves arranging all the irregular pieces on a plate with the objective of maximizing the use of material. In this article, the layout is formed by the ordered sequence of the irregular pieces which is obtained by a hybrid search algorithm and where the order is decoded by a proposed placement principle. First, a novel no-fit-polygon (NFP) generator is introduced. Then, a placement principle is presented with the new NFP generator. Finally, a search algorithm hybridized with beam search (BS) and tabu search (TS) is proposed to search over the sequence. The numerical experiments with many benchmark problems show that the hybrid algorithm is an applicative and effective approach for solving the irregular packing problem. The hybrid algorithm can produce competitive solutions in less time than many other typical algorithms.

Hybrid Particle Swarm: Tabu Search Optimization Algorithm for Parameter Estimation

2013 ◽  
Author(s):  
Anish Sebastian ◽  
Marco P. Schoen
Keyword(s):  
Tabu Search ◽  
Hybrid Algorithm ◽  
Search Algorithm ◽  
Single Agent ◽  
Particle Swarm ◽  
Estimation Problem ◽  
Benchmark Problems ◽  
Force Output ◽  
Semg Signal ◽  
Advantages And Disadvantages

A hybrid intelligent algorithm is proposed. The algorithm utilizes a particle swarm and a Tabu search algorithm. Swarm based algorithms and single agent based algorithms each, have distinct advantages and disadvantages. The goal of the presented work is to combine the strengths of the two different algorithms in order to achieve a more effective optimization routine. The developed hybrid algorithm is tailored such that it has the capability to adapt to the given cost function during the optimization process. The proposed algorithm is tested on a set of different benchmark problems. In addition, the hybrid algorithm is utilized for solving the estimation problem encountered for estimating the finger force output given a surface electromyogram (sEMG) signal at the input. This estimation problem is commonly encountered while developing a control system for a prosthetic hand.

scholarly journals GASAT: A Genetic Local Search Algorithm for the Satisfiability Problem

2006 ◽  
Vol 14 (2) ◽  
pp. 223-253 ◽  
Author(s):  
Frédéric Lardeux ◽  
Frédéric Saubion ◽  
Jin-Kao Hao
Keyword(s):  
Tabu Search ◽  
Local Search ◽  
Hybrid Algorithm ◽  
State Of The Art ◽  
Search Algorithm ◽  
Satisfiability Problem ◽  
Local Search Algorithm ◽  
Overall Performance ◽  
Genetic Local Search ◽  
Search Stage

This paper presents GASAT, a hybrid algorithm for the satisfiability problem (SAT). The main feature of GASAT is that it includes a recombination stage based on a specific crossover and a tabu search stage. We have conducted experiments to evaluate the different components of GASAT and to compare its overall performance with state-of-the-art SAT algorithms. These experiments show that GASAT provides very competitive results.

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.

A Filter-and-Fan Metaheuristic for the 0-1 Multidimensional Knapsack Problem

2012 ◽  
Vol 3 (4) ◽  
pp. 43-63 ◽  
Author(s):  
Mahdi Khemakhem ◽  
Boukthir Haddar ◽  
Khalil Chebil ◽  
Saïd Hanafi
Keyword(s):  
Tabu Search ◽  
Knapsack Problem ◽  
Search Algorithm ◽  
Neighborhood Search ◽  
Benchmark Problems ◽  
Tree Search ◽  
Search Procedure ◽  
Multidimensional Knapsack Problem ◽  
Multidimensional Knapsack ◽  
Adaptive Neighborhood

This paper proposes a new hybrid tree search algorithm to the Multidimensional Knapsack Problem (MKP) that effectively combines tabu search with a dynamic and adaptive neighborhood search procedure. The authors’ heuristic, based on a filter-and-fan (F&F) procedure, uses a Linear Programming-based Heuristic to generate a starting solution to the F&F process. A tabu search procedure is used to try to enhance the best solution value provided by the F&F method that generates compound moves by a strategically truncated form of tree search. They report the first application of the F&F method to the MKP. Experimental results obtained on a wide set of benchmark problems clearly demonstrate the competitiveness of the proposed method compared to the state-of-the-art heuristic methods.

A beam search algorithm for the circular packing problem

2009 ◽  
Vol 36 (5) ◽  
pp. 1513-1528 ◽  
Author(s):  
Hakim Akeb ◽  
Mhand Hifi ◽  
Rym M’Hallah
Keyword(s):  
Search Algorithm ◽  
Packing Problem ◽  
Beam Search ◽  
Circular Packing

scholarly journals A Tabu-Genetic Hybrid Search Algorithm for Job-shop Scheduling Problem

2019 ◽  
Vol 95 ◽  
pp. 04007
Author(s):  
Yan Ge ◽  
Aimin Wang ◽  
Zijin Zhao ◽  
Jieran Ye
Keyword(s):  
Genetic Algorithm ◽  
Tabu Search ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Algorithm ◽  
Search Performance ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Hybrid Search ◽  
Shop Scheduling

To deal with the job-shop scheduling problem (JSP), a tabu-genetic hybrid search algorithm is proposed. The algorithm generates several initial solutions distributed in the whole solution space for tabu search by genetic algorithm, which avoids the over-dependence on the initial solution of tabu search algorithm. With the mechanism mentioned above, the algorithm proposed has both global search performance of genetic algorithm and local search performance of labu search algorithm. Finally, a program was developed with the achral data of FT (10x 10). to verify the feasibility and effectiveness of the algorithm. The result shows that the algorithm achieves satisfactory results in all indexes mentioned above.

scholarly journals Newly Elaborated Hybrid Algorithm for Optimization of Robot Arm’s Trajectory in Order to Increase Efficiency and Provide Sustainability in Production

2021 ◽  
Vol 13 (15) ◽  
pp. 8193
Author(s):  
Rabab Benotsmane ◽  
László Dudás ◽  
György Kovács
Keyword(s):  
Tabu Search ◽  
Cycle Time ◽  
Hybrid Algorithm ◽  
Search Algorithm ◽  
Raw Materials ◽  
Industrial Robots ◽  
Motion Path ◽  
Environmental Damage ◽  
Robot Arm ◽  
Manufacturing Companies

Nowadays, resources for production (raw materials, human, energy, etc.) are limited, while population, consumption and environmental damage are continuously increasing. Consequently, the current practices of resource usage are not sustainable. Therefore, manufacturing companies have to change to environmentally friendly and innovative technologies and tools, e.g., industrial robots. Robots are necessary in the production sector and also in terms of sustainability: (1) the application of robots is needed to compensate for the lack of human resources; (2) robots can increase productivity significantly; and (3) there are several hazardous (e.g., chemical, physical) industrial tasks for which robots are more adequate than human workforce. This article introduces a newly elaborated Hybrid Algorithm for optimization of a robot arm’s trajectory by the selection of that trajectory that has the smallest cycle time. This Hybrid Algorithm is based on the Tabu Search Algorithm and also uses two added methods—Point Insertion and Grid Refinement—simultaneously to find more precisely the optimal motion path of the robot arm in order to further reduce the cycle time and utilize the joints’ torque more efficiently. This Hybrid Algorithm is even more effective than applying the Tabu Search method alone and results in even higher efficiency improvement. The Hybrid Algorithm is executed using MATLAB software by creating a dynamic model of a 5 degree-of-freedom robot arm. The main contribution of the research is the elaboration of the new Hybrid Algorithm, which results in the minimization of robot arms’ motion cycle times, causing a significant increase in productivity and thus a reduction in specific production cost; furthermore, obstacles in the workspace can be avoided. The efficiency of the Hybrid Algorithm is validated by a case study showing that application of the new algorithm resulted in 32% shorter motion cycle time.

Solving Two-Dimensional Rectangle Packing Problem Using Nature-Inspired Metaheuristic Algorithms

2018 ◽  
Vol 03 (02) ◽  
pp. 1850009 ◽  
Author(s):  
Amandeep Kaur Virk ◽  
Kawaljeet Singh
Keyword(s):  
Genetic Algorithm ◽  
Tabu Search ◽  
Search Algorithm ◽  
Packing Problem ◽  
Cuckoo Search ◽  
Metaheuristic Algorithms ◽  
Two Dimensional ◽  
Rectangle Packing ◽  
Metaheuristic Methods ◽  
Rectangle Packing Problem

This paper applies cuckoo search and bat metaheuristic algorithms to solve two-dimensional non-guillotine rectangle packing problem. These algorithms have not been found to be used before in the literature to solve this important industrial problem. The purpose of this work is to explore the potential of these new metaheuristic methods and to check whether they can contribute in enhancing the performance of this problem. Standard benchmark test data has been used to solve the problem. The performance of these algorithms was measured and compared with genetic algorithm and tabu search techniques which can be found to be used widely in the literature to solve this problem. Good optimal solutions were obtained from all the techniques and the new metaheuristic algorithms performed better than genetic algorithm and tabu search. It was seen that cuckoo search algorithm excels in performance as compared to the other techniques.

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