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Workflow Scheduling Using Heuristics Based Ant Colony Optimization

N. Pughazendi, J. Elayaraja

Abstract


Ant Colony Optimization (ACO) is a Meta heuristic combinatorial optimization technique. It is a excellent grouping optimization procedure. A novel ant colony optimization is projected. To advance the penetrating routine the principles of evolutionary algorithm and simulated resistant algorithm have been pooled with the distinctive continuous Ant colony optimization algorithm. In this new algorithm, the ant individual is transformed by adaptive cauchi transformation and thickness selection. To verify the new algorithm the typical functions such as objective function and path construction functions are used. And then the results are versified with continuous ant colony optimization algorithm. The results show that the convergent speed and computing precision of new algorithm are all very good. We can use the algorithm to solve the real time problems like routing, assignment, scheduling.

Keywords


Ant Colony Optimization (ACO), Grid Computing, Workflow Scheduling

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References


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