Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/347669
Title: A novel evolutionary hybrid ant breeding algorithm for generalized assignment problems
Researcher: Kalaiarasi S
Guide(s): P. Sriramya
Keywords: Automation and Control Systems
Computer Science
Engineering and Technology
University: Saveetha University
Completed Date: 2021
Abstract: The generalized assignment problem (GAP) is a classical assignment problem in which the assignments of many jobs are possible, to an agent. The GAP consists of a set of agents and a set of tasks. In GAP, the cost required by each agent to perform the jobs, the amount of resources consumed by each agent to perform and the resource capacity available with each of the agents were known. The assignment of jobs to the agents is to be made in such a way that all the jobs are to be performed and the total resource needed by any agent does not exceed its capacity, such that the sum of the costs corresponding to the assignments is less.The Hybrid Ant Breeding Algorithm works linearly and the time complexity required by this algorithm is O(n logn). Initially the algorithm takes the running time of O(n) and the second phase takes O(log n) as running time. So the overall running time of the proposed Novel Evolutionary Hybrid Ant Breeding Algorithm is O(n logn). The memory space utilized by the algorithm is the space complexity. The space complexity of the proposed Novel Evolutionary Hybird Ant Breeding Algorithm is O(n2). newlineThe newly proposed algorithm is an evolutionary algorithm that finds the optimal solution for Knapsack Problem, Bin Packing Problem, Multiple Travelling Salesman Problem and the Forest Fire Detection problem with minimal number of iterations, less computational time and shorter distance, respectively. In future the proposed algorithm can be applied for intrusion detection and many more optimization problem. newline
Pagination: 
URI: http://hdl.handle.net/10603/347669
Appears in Departments:Department of Engineering

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10_chapter1.pdf.pdf364.89 kBAdobe PDFView/Open
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12_chapter 3.pdf.pdf550.95 kBAdobe PDFView/Open
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17_chapter 8.pdf.pdf661.67 kBAdobe PDFView/Open
18_conclusion.pdf.pdf197.56 kBAdobe PDFView/Open
19_bio.pdf.pdf363.45 kBAdobe PDFView/Open
80_recommendation.pdf197.56 kBAdobe PDFView/Open
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