research-article

Multi-join Query in Database Based on Genetic and Ant Colony Algorithm Optimization

Authors:

Lingling Shi,

Dongzhi HeAuthors Info & Claims

ICBDC '19: Proceedings of the 4th International Conference on Big Data and Computing

Pages 29 - 33

https://doi.org/10.1145/3335484.3335499

Published: 10 May 2019 Publication History

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Abstract

Faced with massive data and the complexity of query requirements, how to improve the query speed of database has become a research hotspot. This paper analyses the artificial intelligence algorithms, genetic ant colony algorithm (GA-ACA), which is used for database query optimization. The GA-ACA is prone to decrease the diversity in multi-connection search of database, which results in inefficiency and local extremum. To solve this problem, our paper proposes an improvement algorithm on multi-connection query. Based on the premise of population diversity, the algorithm analyses the population entropy and variance. And it chooses the equal probability crossover or the unequal probability crossover according to the evolutionary state, which effectively avoids the phenomenon of local optimum due to the iteration of similar individuals. This paper improves the crossover operation and redefines the generation mode of new population. Experiments show that the improved algorithm avoids the local optimal solution to some extent, meanwhile shortens the searching time.

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Cited By

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Zhang YLi X(2023)Database Multi-Connection Query Optimization Based on Improved Snake Optimization Algorithm2023 7th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE)10.1109/ICEMCE60359.2023.10490819(946-950)Online publication date: 20-Oct-2023
https://doi.org/10.1109/ICEMCE60359.2023.10490819

Index Terms

Multi-join Query in Database Based on Genetic and Ant Colony Algorithm Optimization
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Randomized search

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Published In

ICBDC '19: Proceedings of the 4th International Conference on Big Data and Computing

May 2019

353 pages

ISBN:9781450362788

DOI:10.1145/3335484

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Shenzhen University: Shenzhen University
Sun Yat-Sen University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 May 2019

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ICBDC 2019

ICBDC 2019: 2019 4th International Conference on Big Data and Computing

May 10 - 12, 2019

Guangzhou, China

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Zhang YLi X(2023)Database Multi-Connection Query Optimization Based on Improved Snake Optimization Algorithm2023 7th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE)10.1109/ICEMCE60359.2023.10490819(946-950)Online publication date: 20-Oct-2023
https://doi.org/10.1109/ICEMCE60359.2023.10490819

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Comparative study of diversity based parallel dual population genetic algorithm for unconstrained function optimisations

Application of Ant Colony Optimization Algorithm to Multi-Join Query Optimization

Hybrid Genetic Algorithm with Particle Swarm Optimization Technique