Query Optimization using Modified Ant Colony Algorithm

Wagh, Ajay; Nemade, Varsha

doi:10.5120/ijca2017914185

Cited by 5 publications

(5 citation statements)

References 15 publications

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“…Experiments have verified that this distribution has a good effect on balancing the mutation ability of global exploration and local fine search. erefore, we use dynamic adaptive Student's t-step to synthesize the variation effect [11,12]. e Student's T distribution was proposed in 1908, mainly to solve the problem that the sample size is small and the population variance is unknown, which makes it impossible to use Gaussian distribution data to model [13].…”

Section: Student's T Distributionmentioning

confidence: 99%

Key Technologies of Software Engineering Based on T-ACO Algorithm

2022

Mobile Information Systems

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The main core of software engineering key technologies is the development of software services, ensuring the scientificity, security, and stability of the application software engineering system. At present, China’s economic development urgently needs the support of software engineering technology. Based on the T-ACO algorithm, the scientificity of software engineering and the accuracy of data have been significantly improved compared with traditional software engineering technology. It plays an important role in promoting the follow-up software engineering technology. In order to effectively analyze the key technology of engineering software, an improved ant colony algorithm based on T distribution is proposed in this paper. Because the basic ant colony algorithm is easy to fall into the local optimum and the optimization accuracy is low, in the optimization process, at the beginning of the pheromone update, the introduction of the T distribution is helpful for the basic ant colony algorithm to make up for its shortcomings. Adding pheromone variables to the basic ant colony algorithm improves the diversity of the ant colony, thereby eliminating the limitations of local optimal solutions. At the same time, the T-ACO algorithm also improves the search accuracy and convergence speed of automatic data generation in software engineering. In this paper, the performance of the T-ACO algorithm is simulated by experiments. Experimental analysis shows that when the population size is small, the T-ACO algorithm may sometimes not converge to the optimal solution, but when the population size is large (≥50), the T-ACO algorithm may converge to the optimal solution. It can realize the coverage of the total path by the output test case set. While the other two algorithms can achieve full path coverage, they are not stable, resulting in an average coverage between 90% and 100%. The T-ACO algorithm not only has good accuracy in creating test case sets, but also has good algorithm performance, and it is suitable as a multipath test case creation algorithm.

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Section: Student's T Distributionmentioning

confidence: 99%

Key Technologies of Software Engineering Based on T-ACO Algorithm

2022

Mobile Information Systems

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“…e more ants pass by, the more pheromones they leave behind, and each choice of ants is to move towards a path with more pheromones [16]. TSP problem: in a given n cities, if the traveling salesman starts from one of the cities and then visits the remaining cities in turn, until returning to the original departure city, it is required that each city can only be visited once on the way to find the shortest access path [17].…”

Section: Acamentioning

confidence: 99%

Construction of Computer Algorithms in Bioinformatics of the Fusion Genetic Algorithm

Haimeng

2022

Mathematical Problems in Engineering

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With the continuous in-depth exploration of life sciences, bioinformatics based on life sciences, computer algorithms, and statistics have gradually developed. The research of bioinformatics mainly focuses on the study of genes, and the structural characteristics of genes lead to a large amount of extremely complex data in the study of bioinformatics. Analyzing data in bioinformatics research requires accurate calculation by computer algorithms. However, common computer algorithms such as the dynamic programming algorithm and the genetic algorithm have the disadvantages of large memory or inaccurate optimization. Combining the ant colony algorithm (ACA) and GA can give the advantages of the two methods that should be fully utilized to efficiently analyze the biological information data. In this paper, the ant colony fusion genetic algorithm (ACA-GA), GA, and dynamic programming algorithm are used to compare and analyze the sensitivity, convergence speed, sequence alignment accuracy, and required memory space of gene sequences. The experimental results show that compared with ACA-GA, the dynamic programming algorithm has the advantage of finding the optimal alignment of 100%, but the memory required is too large, the memory required is more than 10 times that of the ant colony fusion genetic algorithm and the sensitivity is not as good as ACA-GA. The convergence speed of ACA-GA is faster than the gene comparison speed of GA and the accuracy is 2.6% better than that of GA on average. ACA-GA has the advantages of GA and ACA, which can improve the computational efficiency of biological data in bioinformatics.

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“…A well-known application of ant colony optimization is shortest path problem which occurs mostly in the field of telecommunication networks. Ant colony optimization depends on the artificial system and it based on colonies of real ants (Wagh & Nemade, 2017). In (Wagh & Nemade, 2017), modified ant colony optimization algorithm is used for query optimization and comparison with other query optimization algorithms is done.…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

“…Ant colony optimization depends on the artificial system and it based on colonies of real ants (Wagh & Nemade, 2017). In (Wagh & Nemade, 2017), modified ant colony optimization algorithm is used for query optimization and comparison with other query optimization algorithms is done. The results show that modified any colony algorithm performs well in terms of computation time as compared to other algorithms.…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

“…As query optimization is really a complex task in a distributed database environment, there are so many techniques which are used for query optimization purpose. A modified version of ant colony algorithm (ACO) is proposed in (Wagh & Nemade, 2017) in order to find an appropriate solution for query optimization. A comparison is then done among the existing ant colony algorithm and modified ant colony algorithm and the results show that modified ant colony algorithm consume less computation time as compared to others and simple ant colony algorithm.…”

Section: Application Of Aco For Query Optimization In Distributed Datmentioning

confidence: 99%

See 1 more Smart Citation

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

Ali

Kiran

Shahzad

2018

ADCAIJ

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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.

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Query Optimization using Modified Ant Colony Algorithm

Cited by 5 publications

References 15 publications

Key Technologies of Software Engineering Based on T-ACO Algorithm

Key Technologies of Software Engineering Based on T-ACO Algorithm

Construction of Computer Algorithms in Bioinformatics of the Fusion Genetic Algorithm

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

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