Improved auto control ant colony optimization using lazy ant approach for grid scheduling problem

Tiwari, Pawan Kumar; Vidyarthi, Deo Prakash

doi:10.1016/j.future.2016.01.017

Cited by 41 publications

(15 citation statements)

References 21 publications

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“…Definición 2. La planificación es la que realiza la optimización de algunas características y parámetros relacionados con el sistema o el trabajo, tales como: rendimiento, sistema de utilización, tiempo de respuesta, tiempo de espera, tiempo de respuesta, la equidad, nivel de seguridad, disponibilidad [15]. Definición 3.…”

Section: A Conceptounclassified

Estrategias de planificación para datos y procesos en computación Grid: estado del arte

Bermúdez

Pérez

2019

Ing.USBMed

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Resumen. La distribución y la naturaleza compartida y heterogénea de la computación grid, hace que su objetivo de ofrecer aplicaciones con poder computacional colectivo, sea un gran reto. El objetivo es presentar el resultado de una investigación sistemática sobre aspectos teóricos de las temáticas que convergen en el diseño y desarrollo de planificadores de datos y procesos en computación grid. Se tomó como referente la metodología planteada para desarrollo de estados de arte, que cuenta con dos fases principales: heurística y hermenéutica. En el proceso se analizaron los fundamentos teóricos, tales como: la arquitectura, las etapas que conforman el proceso realizado por parte de un planificador grid y los tipos de planificación existentes, todo ello con el objeto de abordar los diferentes modelos y enfoques computacionales, heurísticos y metaheurísticos que permiten un funcionamiento eficiente y eficaz de maneraóptima de los planificadores grid, permitiendo mitigar en cierto grado los problemas presentados en la planificación grid. La optimización de la planificación de recursos grid, es unárea que se encuentra en desarrollo, dado que las problemáticas principales como: demora en la planificación y el proceso de optimización, están aún en etapa de desarrollo. Palabras Clave. Computación grid; heurística; metaheurística; planificación grid; procesos; trabajos.Abstract. The distribution, the shared and heterogeneous nature of grid computing, makes its objective of offering applications with a collective computational power, be a great challenge. The objetive is to present the result of the systematic research about theoretical aspects about converge topics in the design and development of data and process planners in GRID computation. The methodology proposed for the development of art states was taken as a reference, which has two main phases: heuristics and hermeneutics. In this process, the theoretical foundations are analyzed like the architecture, the stages that conforms the process performed by a GRID planner and the existing planner types; all this with the aim of addressing the different computational, heuristic and metaheuristic models and approaches that allows the efficient and effective operation of GRID planners, allowing to mitigate in certain degree the problems presented in GRID planning. The optimization of grid resource planning is an area that is under development, given that the main problems such as: delay in planning and the optimization process, are still in the development stage.

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Section: A Conceptounclassified

Estrategias de planificación para datos y procesos en computación Grid: estado del arte

Bermúdez

Pérez

2019

Ing.USBMed

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“…In [16], the authors employed the concept of lazy ants for finding solutions. However, it introduces additional preprocessing time due to travel iterations.…”

Section: Categorymentioning

confidence: 99%

A slave ants based ant colony optimization algorithm for task scheduling in cloud computing environments

Moon

Gil

et al. 2017

Hum. Cent. Comput. Inf. Sci.

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Since cloud computing provides computing resources on a pay per use basis, a task scheduling algorithm directly affects the cost for users. In this paper, we propose a novel cloud task scheduling algorithm based on ant colony optimization that allocates tasks of cloud users to virtual machines in cloud computing environments in an efficient manner. To enhance the performance of the task scheduler in cloud computing environments with ant colony optimization, we adapt diversification and reinforcement strategies with slave ants. The proposed algorithm solves the global optimization problem with slave ants by avoiding long paths whose pheromones are wrongly accumulated by leading ants.

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“…And 3 is a constant slightly greater than 1. As shown in Figure 4, the next node of 17 may be 11,12,13,16,18,21,22, and 23, where node 13 has two same directions as the end, so the direction information is 2 3 . And as such, the direction information of nodes 11, 12, 18, and 23 is 1 3 , and for 16, 21, and 22 it is 0 2 .…”

Section: Improvement Aspectsmentioning

confidence: 99%

“…However, the application of ant colony algorithm is much more than that. As discussed by Tiwari and Vidyarthi [18], the ant colony optimization algorithm solved 2 Mathematical Problems in Engineering the scheduling problem effectively through a kind of special ants that remain alive till the fitter lazy ants are generated in the successive generations. Wang et al [19] proposed a modified ACS scheme that can identify the true attack path even without the entire network routing information.…”

Section: Introductionmentioning

confidence: 99%

An Improved Ant Colony Algorithm for Solving the Path Planning Problem of the Omnidirectional Mobile Vehicle

Zhao

Cheng

Chen

2016

Mathematical Problems in Engineering

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This paper presents an improved ant colony algorithm for the path planning of the omnidirectional mobile vehicle. The purpose of the improved ant colony algorithm is to design an appropriate route to connect the starting point and ending point of the environment with obstacles. Ant colony algorithm, which is used to solve the path planning problem, is improved according to the characteristics of the omnidirectional mobile vehicle. And in the improved algorithm, the nonuniform distribution of the initial pheromone and the selection strategy with direction play a very positive role in the path search. The coverage and updating strategy of pheromone is introduced to avoid repeated search reducing the effect of the number of ants on the performance of the algorithm. In addition, the pheromone evaporation coefficient is segmented and adjusted, which can effectively balance the convergence speed and search ability. Finally, this paper provides a theoretical basis for the improved ant colony algorithm by strict mathematical derivation, and some numerical simulations are also given to illustrate the effectiveness of the theoretical results.

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Improved auto control ant colony optimization using lazy ant approach for grid scheduling problem

Cited by 41 publications

References 21 publications

Estrategias de planificación para datos y procesos en computación Grid: estado del arte

Estrategias de planificación para datos y procesos en computación Grid: estado del arte

A slave ants based ant colony optimization algorithm for task scheduling in cloud computing environments

An Improved Ant Colony Algorithm for Solving the Path Planning Problem of the Omnidirectional Mobile Vehicle

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