Evolutionary Algorithms for Query Op-timization in Distributed Database Sys-tems: A review

Abstract

Query optimization; Distributed database; EvolutionaryComputation; Bio-inspired algorithms Evolutionary Algorithms are bio-inspired optimization problem-solving approaches that exploit principles of biological evolution, such as natural selection and genetic inheritance. This review paper provides the application of evolutionary and swarms intelligence based query optimization strategies in Distributed Database Systems. The query optimization in a distributed environment is challenging task and hard problem. However, Evolutionary approaches are promising for the optimization problems. The problem of query optimization in a distributed database environment is one of the complex problems. There are several techniques which exist and are being used for query optimization in a distributed database. The intention of this research is to focus on how bio-inspired computational algorithms are used in a distributed database environment for query optimization. This paper provides working of bio-inspired computational algorithms in distributed database query optimization which includes genetic algorithms, ant colony algorithm, particle swarm optimization and Memetic Algorithms.

Modified Firefly Algorithm for Optimizing Biomedical Breast Cancer Queries

“…A comparison of evolutionary algorithms applied to the problem of query optimization in distributed databases was [15] discussed in literature. The research focussed on bio-inspired computational algorithms used in optimizing distributed database queries.…”