scholarly journals An Ant Colony Optimization For Job Scheduling To Minimize Makespan Time

2015 ◽  
pp. 13-15
Author(s):  
V. SELVI ◽  
R. UMARANI
Keyword(s):  
Ant Colony Optimization ◽  
Numerical Experiments ◽  
Job Scheduling ◽  
Optimal Path ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Ant Colony ◽  
Ant Algorithm ◽  
Makespan Minimization ◽  
Job Scheduling Problem

This paper deals with the makespan minimization for Job Scheduling . Research on optimization techniques of the Job Scheduling Problem (JSP) is one of the most significant and promising areas of an optimization. Instead of the traditional optimization method, this paper presents an investigation into the use of an Ant Colony optimization (ACO) to optimize the JSP. The numerical experiments of ACO were implemented in a small JSP. In the natural environment, the ants have a tremendous ability to team up to find an optimal path to food resources. An ant algorithm stimulates the behavior of ants. The main objective of this paper is to minimize the makespan time of a given set of jobs and achieved optimal results are encroached.

scholarly journals Hadoop Job Scheduling Using Improvised Ant Colony Optimization

2021 ◽  
Vol 12 (2) ◽  
pp. 3417-3424
Author(s):  
G. Joel Sunny Deol, Et. al.
Keyword(s):  
Ant Colony Optimization ◽  
Execution Time ◽  
Job Scheduling ◽  
Optimization Method ◽  
Large Datasets ◽  
Ant Colony ◽  
Distributed File System ◽  
Performance Parameters ◽  
Programming Framework ◽  
Hadoop Distributed File System

Hadoop Distributed File System is used for storage along with a programming framework MapReduce for processing large datasets allowing parallel processing. The process of handling such complex and vast data and maintaining the performance parameters up to certain level is a difficult task. Hence, an improvised mechanism is proposed here that will enhance the job scheduling capabilities of Hadoop and optimize allocation and utilization of resources. Significantly, an aggregator node is added to the default HDFS framework architecture to improve the performance of Hadoop Name node. In this paper, four entities viz., the name node, secondary name node, aggregator nodes, and data nodes have been modified. Here, the aggregator node assigns jobs to data node, while Name node tracks aggregator nodes. Also, based on the job size and expected execution time, an improvised ant colony optimization method is developed for scheduling jobs.In the end, the results demonstrate notable improvisation over native Hadoop and other approaches.

Nondestructive Analysis of Dielectric Bodies by Means of an Ant Colony Optimization Method

2012 ◽  
pp. 308-325
Author(s):  
Matteo Pastorino ◽  
Andrea Randazzo
Keyword(s):  
Ant Colony Optimization ◽  
Inverse Scattering ◽  
Scattering Problem ◽  
Inverse Scattering Problem ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Ant Colony ◽  
Nondestructive Analysis ◽  
Dielectric Bodies ◽  
Stochastic Optimization Algorithm

Electromagnetic approaches based on inverse scattering are very important in the field of nondestructive analysis of dielectric targets. In most cases, the inverse scattering problem related to the reconstruction of the dielectric properties of unknown targets starting from measured field values can be recast as an optimization problem. Due to the ill-posedness of this inverse problem, the application of global optimization techniques seems to be a very suitable choice. In this chapter, the authors review the use of the Ant Colony Optimization method, which is a stochastic optimization algorithm that has been found to provide very good results in a plethora of applications in the area of electromagnetics as well as in other fields of electrical engineering.

Schedulers Based on Ant Colony Optimization for Parameter Sweep Experiments in Distributed Environments

2013 ◽  
pp. 410-448 ◽  
Author(s):  
Elina Pacini ◽  
Cristian Mateos ◽  
Carlos García Garino
Keyword(s):  
Ant Colony Optimization ◽  
Job Scheduling ◽  
Input Parameter ◽  
Ant Colony ◽  
Approximation Techniques ◽  
Processing Times ◽  
Scientists And Engineers ◽  
Computing Environments ◽  
Job Scheduling Problem ◽  
Parameter Values

Scientists and engineers are more and more faced to the need of computational power to satisfy the ever-increasing resource intensive nature of their experiments. An example of these experiments is Parameter Sweep Experiments (PSE). PSEs involve many independent jobs, since the experiments are executed under multiple initial configurations (input parameter values) several times. In recent years, technologies such as Grid Computing and Cloud Computing have been used for running such experiments. However, for PSEs to be executed efficiently, it is necessary to develop effective scheduling strategies to allocate jobs to machines and reduce the associated processing times. Broadly, the job scheduling problem is known to be NP-complete, and thus many variants based on approximation techniques have been developed. In this work, the authors conducted a survey of different scheduling algorithms based on Swarm Intelligence (SI), and more precisely Ant Colony Optimization (ACO), which is the most popular SI technique, to solve the problem of job scheduling with PSEs on different distributed computing environments.

scholarly journals Pengembangan Metode Ant Colony Optimization Pada Klasifikasi Tanaman Mangga Menggunakan K-Nearest Neighbor

2017 ◽  
Vol 8 (4) ◽  
Author(s):  
Febri Liantoni ◽  
Luky Agus Hermanto
Keyword(s):  
Edge Detection ◽  
Ant Colony Optimization ◽  
Nearest Neighbor ◽  
Optimization Method ◽  
Ant Colony ◽  
K Nearest Neighbor ◽  
Edge Image ◽  
Image Detection ◽  
Mango Leaves ◽  
Leaf Midrib

Abstract. Leaf is one important part of a plant normally used to classify the types of plants. The introduction process of mango leaves of mangung and manalagi mango is done based on the leaf edge image detection. In this research the conventional edge detection process was replaced by ant colony optimization method. It is aimed to optimize the result of edge detection of mango leaf midrib and veins image. The application of ant colony optimization method successfully optimizes the result of edge detection of a mango leaf midrib and veins structure. This is demonstrated by the detection of bony edges of the leaf structure which is thicker and more detailed than using a conventional edge detection. Classification testing using k-nearest neighbor method obtained 66.67% accuracy. Keywords: edge detection, ant colony optimization, classification, k-nearest neighbor. Abstrak. Pengembangan Metode Ant Colony Optimization Pada Klasifikasi Tanaman Mangga Menggunakan K-Nearest Neighbor. Daun merupakan salah satu bagian penting dari tanaman yang biasanya digunakan untuk proses klasifikasi jenis tanaman. Proses pengenalan daun mangga gadung dan mangga manalagi dilakukan berdasarkan deteksi tepi citra struktur tulang daun. Pada penelitian ini proses deteksi tepi konvensional digantikan dengan metode ant colony optimization. Hal ini bertujuan untuk optimasi hasil deteksi tepi citra tulang daun mangga. Penerapan metode ant colony optimization berhasil mengoptimalkan hasil deteksi tepi struktur tulang daun mangga. Hal ini ditunjukkan berdasarkan dari hasil deteksi tepi citra struktur tulang daun yang lebih tebal dan lebih detail dibandingkan menggunakan deteksi tepi konvensional. Pengujian klasifikasi dengan metode k-nearest neighbor didapatkan nilai akurasi sebesar 66,67%.Kata Kunci: deteksi tepi, ant colony optimization, klasifiaksi, k-nearest neighbor.

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