Many database applications require similarity based retrieval on stored text and/or multimedia objects. This is an area of increasing research interest in the sectors of database, data mining, information retrieval and knowledge discovery. This paper presents a brief survey on the existing approximate string matching algorithms by primarily demonstrating three families of algorithms -the Brute force, the Lipschitz Embeddings and the Ball Partitioning algorithms. While Brute Force performs approximate string matching based on distance measures of the query object from each string stored in the database, Lipschitz Embeddings uses a far more efficient approach which embeds the stored strings in database in vector space so that the distances of embedded strings approximates the actual distances. Ball Partitioning algorithm, much more efficient than Brute force but less efficient than Lipschitz algorithm, performs search in approximate string matching based on distances where queries operate on an arbitrary search hierarchy. The paper compares and makes an analysis of these three algorithms which are suitable for approximate matching of strings stored in database text files, an issue much required in the context of similarity based retrieval of objects. The work can be extended for future work by taking into account a larger number of algorithms suited to approximate string matching for the benefit of a wider scope of comparisons and picking out the most optimal one.
This paper introduces a variant of Artificial Bee Colony algorithm and compares its results with a number of swarm intelligence and population based optimization algorithms. The Artificial Bee Colony (ABC) is an optimization algorithm based on the intelligent food foraging behavior of honey bees. The proposed variant, Artificial Bee Colony Algorithm with Balanced Explorations and Exploitations (ABC-BEE) makes attempts to dynamically balance the mutation step size with which the artificial bees explore the search space. Mutation with small step size produces small variations of existing solutions which is better for exploitations, while large mutation steps are likely to produce large variations that facilitate better explorations of the search space. ABC-BEE fosters both large and small mutation steps as well as adaptively controls the step lengths based on their effectiveness to produce better solutions. ABC-BEE has been evaluated and compared on a number of benchmark functions with the original ABC algorithm, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Particle Swarm Inspired Evolutionary Algorithm (PS-EA). Results indicate that the proposed scheme facilitates more effective mutations and performs better optimization outperforming all the other algorithms in comparison.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.