A Hybrid Swarm Intelligent Method Based on Genetic Algorithm and Artificial Bee Colony

Zhao, Hang; Pei, Zhili; Jiang, Jingqing; Guan, Renchu; Wang, Chaoyong; Shi, Xiaohu

doi:10.1007/978-3-642-13495-1_68

Cited by 36 publications

(10 citation statements)

References 11 publications

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“…The related employed bee becomes a scout bee by leaving the food source in order to search a new food source using Equation (4). 49 Therefore, the number of food sources is half of the population number…”

Section: The Scout Bee Phasementioning

confidence: 99%

Join query optimization in the distributed database system using an artificial bee colony algorithm and genetic operators

Panahi

Navimipour

2019

Concurrency and Computation

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As the main factor in the distributed database systems, query optimization is aimed at finding an optimal execution plan to reduce the runtime. In such systems, because of the repeated relations on various sites, the query optimization is very challenging. Moreover, the query optimization issue with large-scale distributed databases is an NP-hard problem. Therefore, in this paper, an Artificial Bee Colony Algorithm based on Genetic Operators (ABC-GO) is proposed to find a solution to join the query optimization problems in the distributed database systems. The ABC algorithm has the global-local search capabilities and genetic operators to create new candidate solutions for improving the performance of the ABC algorithm. The obtained results have shown that the cost of the query evaluation is minimized and the quality of Top-K query plans is improved for a given distributed query. Moreover, this method decreases the overhead.However, it needs a longer execution time. KEYWORDSartificial bee colony optimization, distributed database, genetic operator, query plan optimization INTRODUCTIONDue to the rapid growth of data in current systems, 1,2 a set of the interrelated databases, known as a distributed database, is stored on multiple computers to improve performance, reliability, accessibility, and modularity in such cases compared to the conventional centralized database system. 3-7 In today's world, every user of the system, whether an employee or a customer, needs access to the company's databases. The data in distributed databases may be repeated at different sites according to a distributed allocation plan. 8-10 As a result, an association among different sites is needed to create answers to a user query. 11 The query optimization is a critical subject for the distributed database management system. Therefore, the poor performance of database for answering the query is the result of poor selection of a query execution plan. 12The query is a statement or a group of statements that performs some simple database operations such as deletion, updating, and retrieving data. It plays a critical role in data management and retrieval. Commonly, the distributed queries are more complicated than centralized queries due to data distribution over different sites. 13 Consequently, the impact of efficient query processing is increasing in a large number of applications.A common method for identifying the Top-k objects is that all database objects are scored on some function. Therefore, the DQP (distributed query process) strategy aims to generate the query processing plans that reduce the amount of data transmission between sites and, as a result, reduces the response time of the distributed query. The query optimization is a critical subject for database management systems, 14 whose purpose is to define an appropriate execution plan for the user's query. 15 The cost of each plan should be produced due to more than one plan for such a query, which is significantly dependent on the amount of participation and data transfer betwe...

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Section: The Scout Bee Phasementioning

confidence: 99%

Join query optimization in the distributed database system using an artificial bee colony algorithm and genetic operators

Panahi

Navimipour

2019

Concurrency and Computation

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“…Many modifications have been proposed coupling ABC either with classical optimization methods for local search such as simplex (HSABC by Kang et al, 2009), the Rosenbrock method (RABC by Kang et al, 2011) and pattern search (HJABC by Kang et al, 2013), or with other robust and efficient well-known techniques for global search, such as genetic algorithms (GA-ABC by Zhao et al, 2010) or evolutionary strategies (BE-ABC by Li and Li, 2012). Among these methods, the RABC based on the Rosenbrock directional search (Rosenbrock, 1960) shows fast convergence to the global optimum.…”

Section: Abc and Its Modifications In The Literaturementioning

confidence: 99%

A hybrid swarm-based algorithm for single-objective optimization problems involving high-cost analyses

Ampellio

Vassio

2016

Swarm Intell

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In many technical fields, single-objective optimization procedures in continuous domains involve expensive numerical simulations. In this context, an improvement of the Artificial Bee Colony (ABC) algorithm, called the Artificial super-Bee enhanced Colony (AsBeC), is presented. AsBeC is designed to provide fast convergence speed, high solution accuracy and robust performance over a wide range of problems. It implements enhancements of the ABC structure and hybridizations with interpolation strategies. The latter are inspired by the quadratic trust region approach for local investigation and by an efficient global optimizer for separable problems. Each modification and their combined effects are studied with appropriate metrics on a numerical benchmark, which is also used for comparing AsBeC with some effective ABC variants and other derivative-free algorithms. In addition, the presented algorithm is validated on two recent benchmarks adopted for competitions in international conferences. Results shows remarkable competitiveness and robustness for AsBeC.

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“…The comparative results show the ABC algorithm is superior than the other two population based algorithms (differential evolution (DE) and particle swarm optimization (PSO)) for constrained optimization problems. Consequently, using hybrid artificial bee colony and genetic algorithm (GA) on the constrained function is more efficient than using GA only [26]. The real-parameter optimization through the modified ABC algorithm also acquires suitable results on hybrid functions compared to other state-of-the-art algorithms [27].…”

Section: The Symbiotic Based Artificial Bee Colonymentioning

confidence: 99%

Advanced Hierarchical Fuzzy Classification Model Adopting Symbiosis Based DNA-ABC Optimization Algorithm

Feng¹,

Li²

2016

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This paper offers a symbiosis based hybrid modified DNA-ABC optimization algorithm which combines modified DNA concepts and artificial bee colony (ABC) algorithm to aid hierarchical fuzzy classification. According to literature, the ABC algorithm is traditionally applied to constrained and unconstrained problems, but is combined with modified DNA concepts and implemented for fuzzy classification in this present research. Moreover, from the best of our knowledge, previous research on the ABC algorithm has not combined it with DNA computing for hierarchical fuzzy classification to explore the merits of cooperative coevolution. Therefore, this paper is the first to apply the mechanism of symbiosis to create a hybrid modified DNA-ABC algorithm for hierarchical fuzzy classification applications. In this study, the partition number and the shape of the membership function are extracted by the symbiosis based hybrid modified DNA-ABC optimization algorithm, which provides both sufficient global exploration and also adequate local exploitation for hierarchical fuzzy classification. The proposed optimization algorithm is applied on five benchmark University of Irvine (UCI) data sets, and the results prove the efficiency of the algorithm.

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A Hybrid Swarm Intelligent Method Based on Genetic Algorithm and Artificial Bee Colony

Cited by 36 publications

References 11 publications

Join query optimization in the distributed database system using an artificial bee colony algorithm and genetic operators

Join query optimization in the distributed database system using an artificial bee colony algorithm and genetic operators

A hybrid swarm-based algorithm for single-objective optimization problems involving high-cost analyses

Advanced Hierarchical Fuzzy Classification Model Adopting Symbiosis Based DNA-ABC Optimization Algorithm

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