Jswa An Improved Algorithm For Grid Workflow Scheduling Using Ant Colony Optimization

In order to overcome the shortcomings of precocity and stagnation in ant colony optimization algorithm, an improved algorithm is presented. Considering the impact that the distance between cities on volatility coefficient, this study presents an model of adjusting volatility coefficient called Volatility Model based on ant colony optimization (ACO) and Max-Min ant system. There are simulation experiments about TSP cases in TSPLIB, the results show that the improved algorithm effectively overcomes the shortcoming of easily getting an local optimal solution, and the average solutions are superior to ACO and Max-Min ant system.

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An Intelligent Cloud Workflow Scheduling System With Time Estimation and Adaptive Ant Colony Optimization

IEEE Transactions on Systems Man and Cybernetics Systems ◽

10.1109/tsmc.2018.2881018 ◽

2019 ◽

pp. 1-16 ◽

Cited By ~ 4

Author(s):

Ya-Hui Jia ◽

Wei-Neng Chen ◽

Huaqiang Yuan ◽

Tianlong Gu ◽

Huaxiang Zhang ◽

...

Keyword(s):

Ant Colony Optimization ◽

Time Estimation ◽

Ant Colony ◽

Workflow Scheduling ◽

Scheduling System

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Improved Ant Colony Optimization and Application on TSP

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.182-183.2055 ◽

2012 ◽

Vol 182-183 ◽

pp. 2055-2058

Author(s):

Zhi Qiang Fu ◽

Lei An Liu

Keyword(s):

Ant Colony Optimization ◽

Ant Colony Algorithm ◽

Ant Colony ◽

Second Phase ◽

Ant System ◽

Ant Colonies ◽

Convergence Process ◽

Intelligent Optimization Algorithm ◽

Ant System Algorithm ◽

Improved Algorithm

Ant Colony Optimization is an intelligent optimization algorithm from the observations of ant colonies foraging behavior. However, ACO usually cost more searching time and get into early stagnation during convergence Process. We design the improved ant colony algorithm using perturbation method to avoid early stagnation, adjusting volatilization coefficient to increase the exploration of tours at first phase and searching speed at second phase, using hortation method to improved searching efficiency. We apply the improved algorithm on traveling salesman problem showing that the improved algorithm finds the best values more quickly and more stability than Max-Min Ant System algorithm.

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An Improved Ant Colony Optimization RWA Algorithm and its Application in Optical Network Wavelength Converter Allocation Strategy

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.263-266.986 ◽

2012 ◽

Vol 263-266 ◽

pp. 986-989

Author(s):

Jian Hua Shen ◽

Jian Yong Xu ◽

Jian Chen ◽

Yue Hui Li

Keyword(s):

Ant Colony Optimization ◽

Blocking Probability ◽

Random Perturbation ◽

Optical Network ◽

Ant Colony ◽

Local Optimum ◽

Allocation Strategy ◽

Wavelength Converter ◽

Improved Algorithm

An improved Ant Colony Optimization based RWA algorithm and its application in optical network wavelength converter allocation strategy is given. The link idle rate is introduced as new constraint along with the random perturbation to prevent searching converged into unexpected local optimum. Theoretical and simulation shows the improved algorithm has better blocking probability and resources utilization performance.

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Modified Ant Colony Optimization for Workflow Scheduling in Cloud Enviornment

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v14i10.6936 ◽

2015 ◽

Vol 14 (10) ◽

pp. 6176-6183

Author(s):

S.J. Mohana ◽

Dr.M. Saroja ◽

Dr.M. Venkatachalam

Keyword(s):

Cloud Computing ◽

Ant Colony Optimization ◽

Heuristic Algorithms ◽

Ant Colony ◽

Workflow Scheduling ◽

Ant Colony Optimization Algorithm ◽

Computing Environment ◽

Shared Resources ◽

Cloud Computing Environment ◽

Computing Model

Cloud computing is a type of parallel and distributed system consisting of a collection of interconnected and virtual computers. This technological trend has enabled the realization of a new computing model called cloud computing, in which shared resources, information,software & other devices are provided according to client requirement at specific time, are provided as general utilities that can be leased and released by users through the Internet in an on-demand fashion.Cloud workflow scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it.Allocation of resources to a large number of workflows in a cloud computing environment presents more difficulty than in network computational environments.A good task scheduler should adapt its scheduling strategy to the changing environment and the types of tasks. In this work, modified ant colony optimization for cloud task scheduling is proposed. The goal of modification is to enhance the performance of the basic ant colony optimization algorithm and optimize the task execution time in view of minimizing the makespan of a given tasks set.

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Ant Colony Algorithm Based on Semantic Relations and its Applications

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.644-650.2076 ◽

2014 ◽

Vol 644-650 ◽

pp. 2076-2080

Author(s):

Yong Jian Yang ◽

Jiu Xuan An ◽

Hong Ying Han

Keyword(s):

Ant Colony Optimization ◽

Ant Colony Algorithm ◽

Good Method ◽

Ant Colony ◽

Semantic Relations ◽

Ant Colony Optimization Algorithm ◽

Multi Stage ◽

The Traveling Salesman Problem ◽

Improved Ant Colony Algorithm ◽

Improved Algorithm

Ant colony optimization algorithm (ACO) is a good method to solve complex multi-stage decision problems. But this algorithm is easy to fall into the local minimum points and has slowly convergence speed, According to the semantic relations, an improved ant colony algorithm has been proposed in this paper. In contrast with the tradition algorithm, the improved algorithm is added with a new operator to update crucial parameters. The new operator is to find out the potential semantic relations behind the history information based on ontology technology. Ant colony optimization can be applied to many engineering fields,taking the Traveling Salesman Problem (TSP) as example, Our experiments show accuracy of improved ant colony algorithm that is superior to that obtained by the other classical versions, and competitive or better than the results achieved by the compared algorithm, this improved algorithm also can improve the searching efficiency.

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