Preeti Tiwari - Academia.edu (original) (raw)

Papers by Preeti Tiwari

Ant Colony Optimization Algorithm is a meta-heuristic, multi-agent technique that can be applied ... more Ant Colony Optimization Algorithm is a meta-heuristic, multi-agent technique that can be applied for solving difficult NP-Hard Combinatorial Optimization Problems like Traveling Salesman Problem (TSP), Job Shop Scheduling Problem (JSP), Vehicle Routing Problem (VRP) and many more. The Positive Feedback Mechanism and Distributed Computing ability makes it very robust in nature. The artificial ants implement a randomized construction heuristic which makes probabilistic decisions as a function of artificial pheromone trails to solve the problems that are dependent on the input data. In spite of ACO having global searching ability and high convergence speed towards optimal solutions, it has some limitations like low population scattering ability and no systematic way of startup. To overcome these problems, various hybrids of ACO with other algorithms like Dynamic Programming, Genetic Algorithm and Particle Swarm Optimization have been proposed to provide better results than using ACO in isolation. This paper studies various approaches for the development of Hybrids of ACO Algorithm for different types of applications and effects thereof.

The augmentation of digital information on the Web has proliferated informational needs and expec... more The augmentation of digital information on the Web has proliferated informational needs and expectations of the seekers, resulting in insistent need of more advanced search tools, that are able to respond to the informational requirements within an organization. The user may formulate a search query in a way that can obscure the useful documents to be retrieved. The objective of query optimization is to transform the query into an effective form to improve the quality of recovered information and to reduce the computational burden in processing document text at query time. Genetic algorithms are efficient and robust methods, employed widely in optimization of a variety of search problems, motivated by Darwin’s principles of natural selection and survival of the fittest. This paper reviews relevance of genetic algorithms to improve upon the user queries in the field of Information Retrieval.

With the advancement of Computer Networks and increase in size of databases, the decentralization... more With the advancement of Computer Networks and increase in size of databases, the decentralization of databases has led to the development of Distributed Database over multiple machines where distribution of the database is Transparent to the users. The query optimization problem in large-scale distributed databases is NP-hard in nature and difficult to solve. Research is being carried out to find an appropriate algorithm to seek an optimal solution especially when the size of the database increases [4].An Ant Colony Optimization Algorithm meets the requirement mentioned above because of its characteristics of positive feedback, distributed computing and combination with heuristics. However, when ACO is implemented in Distributed Database queries, the Initial Information needed by ACO to generate an optimal result set is not systematic and organized which leads to slower convergence speed in the beginning of the processing to generate an optimal solution. In this paper, hybrids of An...

The query optimization problem in large-scale distributed databases is NP nature and difficult to... more The query optimization problem in large-scale distributed databases is NP nature and difficult to solve. The complexity of the optimizer increases as the number of relations and number of joins in a query increases. being carried out to find an appropriate algorithm to seek an optimal solution especially when the size of the database increases. Various Optimization Strategies have been reviewed in this paper and the studies show that the performance of distributed query optimization is improved when Ant Colony Optimization Algorithm is integrated with other optimization algorithms.

With the advancement of Computer Networks and increase in size of databases, the decentralization... more With the advancement of Computer Networks and increase in size of databases, the decentralization of databases has led to the development of Distributed Database over multiple machines where distribution of the database is Transparent to the users. The query optimization problem in large-scale distributed databases is NP-hard in nature and difficult to solve. Research is being carried out to find an appropriate algorithm to seek an optimal solution especially when the size of the database increases [4]. An Ant Colony optimization Algorithm meets the requirement mentioned above because of its characteristics of positive feedback, distributed computing and combination with heuristics. However, when ACO is implemented in Distributed Database queries, the Initial Information needed by ACO to generate an optimal result set is not systematic and organized which leads to slower convergence speed in the beginning of the processing to generate an optimal solution. In this paper, hybrids of A...