Distributed databases principles and systems
Distributed databases principles and systems
Query optimization by simulated annealing
SIGMOD '87 Proceedings of the 1987 ACM SIGMOD international conference on Management of data
Optimization of large join queries
SIGMOD '88 Proceedings of the 1988 ACM SIGMOD international conference on Management of data
Randomized algorithms for optimizing large join queries
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
Principles of distributed database systems (2nd ed.)
Principles of distributed database systems (2nd ed.)
Query Optimization in Database Systems
ACM Computing Surveys (CSUR)
Performance Issues in Distributed Query Processing
IEEE Transactions on Parallel and Distributed Systems
Distributed Query Processing Optimization Objectives
Proceedings of the Fourth International Conference on Data Engineering
A Distributed Query Processing Strategy Using Placement Dependency
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Fuzzy Discrete Particle Swarm Optimization for Solving Traveling Salesman Problem
CIT '04 Proceedings of the The Fourth International Conference on Computer and Information Technology
A hybrid particle swarm optimization for job shop scheduling problem
Computers and Industrial Engineering
A discrete version of particle swarm optimization for flowshop scheduling problems
Computers and Operations Research
Multiobjective optimization using dynamic neighborhood particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Genetic algorithms for large join query optimization
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Genetic programming in database query optimization
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
A novel set-based particle swarm optimization method for discrete optimization problems
IEEE Transactions on Evolutionary Computation
Adaptive particle swarm optimization algorithm for dynamic environments
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Adaptive comprehensive learning particle swarm optimizer with history learning
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems
IEEE Transactions on Evolutionary Computation
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A large number of queries are posed on databases spread across the globe. In order to process these queries efficiently, optimal query processing strategies that generate efficient query processing plans are being devised. In distributed relational database systems, due to replication of relations at multiple sites, the relations required to answer a query may necessitate accessing of data from multiple sites. This leads to an exponential increase in the number of possible alternative query plans for processing a query. Though it is not computationally feasible to explore all possible query plans in such a large search space, the query plan that provides the most cost-effective option for query processing is considered necessary and should be generated for a given query. In this paper, an attempt has been made to generate such optimal query plans using Set based Comprehensive Learning Particle Swarm Optimization S-CLPSO. Experimental comparisons of this algorithm with the GA based distributed query plan generation algorithm shows that for higher number of relations, the S-CLPSO based algorithm is able to generate comparatively better quality Top-K query plans.