scholarly journals An Improved Ant Colony Optimized Tabu Search Algorithm for Makespan Improvement in Job Shop

2019 ◽  
Vol 9 (2) ◽  
pp. 670-674
Keyword(s):  
Tabu Search ◽  
Completion Time ◽  
Job Shop ◽  
Short Term Memory ◽  
Search Algorithm ◽  
Ant Colony ◽  
Rollback Recovery ◽  
Short Term ◽  
Term Memory ◽  
Recovery Technique

In industries, the completion time of job problems is increased drastically in the production unit. In many existing kinds of research, the completion time i.e. makespan of the job is minimized using straight paths which is time-consuming. In this paper, we addressed this problem using an Improved Ant Colony Optimization and Tabu Search (ACOTS) algorithm by identifying the fault occurrence position exactly to rollback. Also, we used a short term memory-based rollback recovery technique to roll back to its own short term memory to reduce the completion time of the job. Short term memory is used to visit the recent movements in Tabu search. Our proposed ACOTS-Cmax approach is efficient and consumed less completion time compared to the ACO algorithm

scholarly journals Makespan Minimization in Job Shop Scheduling

2021 ◽  
Vol 11 (1) ◽  
pp. 228-230
Author(s):  
K. Sathya Sundari
Keyword(s):  
Completion Time ◽  
Job Shop ◽  
Short Term Memory ◽  
Job Shop Scheduling ◽  
Rollback Recovery ◽  
Makespan Minimization ◽  
Recovery Strategy ◽  
Short Term ◽  
Term Memory ◽  
Shop Scheduling

In industries, the completion time of job problems in the manufacturing unit has risen significantly. In several types of current study, the job's completion time, or makespan, is reduced by taking straight paths, which is time-consuming. In this paper, we used an Improved Ant Colony Optimization and Tabu Search (ACOTS) algorithm to solve this problem by precisely defining the fault occurrence location in order to rollback. We have used a short-term memory-based rollback recovery strategy to minimise the job's completion time by rolling back to its own short-term memory. The recent movements in Tabu quest are visited using short term memory. As compared to the ACO algorithm, our proposed ACOTS-Cmax solution is more efficient and takes less time to complete.

Long short-term memory neural network-based multi-level model for smart irrigation

2020 ◽  
Vol 34 (36) ◽  
pp. 2050418
Author(s):  
Ravneet Kaur Sidhu ◽  
Ravinder Kumar ◽  
Prashant Singh Rana
Keyword(s):  
Neural Network ◽  
Irrigation Water ◽  
Short Term Memory ◽  
Search Algorithm ◽  
Irrigation Schedule ◽  
Moisture Stress ◽  
Domain Expert ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Rice is a staple food crop around the world, and its demand is likely to rise significantly with growth in population. Increasing rice productivity and production largely depends on the availability of irrigation water. Thus, the efficient application of irrigation water such that the crop doesn’t experience moisture stress is of utmost importance. In the present study, a long short-term memory (LSTM)-based neural network with logistic regression has been used to predict the daily irrigation schedule of drip-irrigated rice. The correlation threshold of 0.75 was used for the selection of features, which helped in limiting the number of input parameters. Also, a dataset based on the recommendation of a domain expert, and another used by the tool Agricultural Production Systems Simulator (APSIM) was used for comparison. Field data comprising of weather station data and past irrigation schedules has been used to train the model. Grid search algorithm has been used to optimize the hyperparameters of the model. Nested cross-validation has been used for validating the results. The results show that the correlation-based selected dataset is as effective as the domain expert-recommended dataset in predicting the water requirement using LSTM as the base model. The models were evaluated on different parameters and a multi-criteria decision evaluation (Technique for Order of Preference by Similarity to Ideal Solution [TOPSIS]) was used to find the best performing.

scholarly journals Tabu Search: A Meta Heuristic for Netlist Partitioning

VLSI Design ◽  
2000 ◽  
Vol 11 (3) ◽  
pp. 259-283 ◽  
Author(s):  
Shawki Areibi ◽  
Anthony Vannelli
Keyword(s):  
Simulated Annealing ◽  
Tabu Search ◽  
Short Term Memory ◽  
Network Partitioning ◽  
Test Results ◽  
Circuit Partitioning ◽  
Short Term ◽  
Local Optima ◽  
Term Memory

The main goal of the paper is to explore the effectiveness of a new method called Tabu Search [1] on partitioning and compare it with two techniques widely used in CAD tools for circuit partitioning i.e., Sanchis Interchange method and Simulated Annealing, in terms of the running time and quality of solution. The proposed method integrates the well known iterative multi-way interchange method with Tabu Search and leads to a very powerful network partitioning heuristic. It is characterized by an ability to escape local optima which usually cause simple descent algorithms to terminate by using a short term memory of recent solutions. Moreover, Tabu Search permits backtracking to previous solutions, which explore different directions and generates better partitions.The quality of the test results on MCNC benchmark circuits are very promising in most cases. Tabu Search yields netlist partitions that contain 20%–67% fewer cut nets and are generated 2/3 to (1/2) times faster than the best netlist partitions obtained by using an interchange method. Comparable partitions to those obtained by Simulated Annealing are obtained 5 to 20 times faster.

A New Scatter Search Design for Multiobjective Combinatorial Optimization with an Application to Facility Location

2020 ◽  
Author(s):  
A. D. López-Sánchez ◽  
J. Sánchez-Oro ◽  
M. Laguna
Keyword(s):  
Tabu Search ◽  
Multiobjective Optimization ◽  
Operations Research ◽  
Short Term Memory ◽  
Scatter Search ◽  
Combination Method ◽  
Heuristic Optimization ◽  
Short Term ◽  
Term Memory ◽  
Search Design

Metaheuristic optimization is at the heart of the intersection between computer science and operations research. The INFORMS Journal of Computing has been fundamental in advancing the ideas behind metaheuristic methodologies. Fred Glover’s “Tabu Search—Part I” was published more than 30 years ago in the first volume of the then ORSA Journal on Computing. This article, one of the most cited in the area of heuristic optimization, paved the way for many contributions to the methodology and practice of operations research. As a continuation of this stream of research, we describe a new scatter search design for multiobjective optimization. The design includes a short-term memory tabu search and a path relinking combination method. We show how the strategies and mechanisms within scatter search and tabu search can be combined to produce a highly effective approach to multiobjective optimization.

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