scholarly journals The Dataset of the Experimental Evaluation of Software Components for Application Design Selection Directed by the Artificial Bee Colony Algorithm

Data ◽  
2020 ◽  
Vol 5 (3) ◽  
pp. 59
Author(s):  
Alexander Gusev ◽  
Dmitry Ilin ◽  
Evgeny Nikulchev
Keyword(s):  
Swarm Intelligence ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Operating Conditions ◽  
Software Components ◽  
Bee Colony ◽  
Component Selection ◽  
Effective Selection ◽  
Intelligence Approach ◽  
Selection Of

The paper presents the swarm intelligence approach to the selection of a set of software components based on computational experiments simulating the desired operating conditions of the software system being developed. A mathematical model is constructed, aimed at the effective selection of components from the available alternative options using the artificial bee colony algorithm. The model and process of component selection are introduced and applied to the case of selecting Node.js components for the development of a digital platform. The aim of the development of the platform is to facilitate countrywide simultaneous online psychological surveys in schools in the conditions of unstable internet connection and the large variety of desktop and mobile client devices, running different operating systems and browsers. The module whose development is considered in the paper should provide functionality for the archiving and checksum verification of the survey forms and graphical data. With the swarm intelligence approach proposed in the paper, the effective set of components was identified through a directional search based on fuzzy assessment of the three experimental quality indicators. To simulate the desired operating conditions and to guarantee the reproducibility of the experiments, the virtual infrastructure was configured. The application of swarm intelligence led to reproducible results for component selection after 312 experiments instead of the 1080 experiments needed by the exhaustive search algorithm. The suggested approach can be widely used for the effective selection of software components for distributed systems operating in the given conditions at this stage of their development.

Research on Swarm Intelligence Algorithm with an Artificial Bee Colony Algorithm for Lot Streaming Problem in Job Shop

2014 ◽  
Vol 951 ◽  
pp. 239-244 ◽  
Author(s):  
Xiao Qiang Xu ◽  
De Ming Lei
Keyword(s):  
Swarm Intelligence ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Job Shop ◽  
Lot Streaming ◽  
Lot Scheduling ◽  
Bee Colony ◽  
Intelligence Algorithm ◽  
Second Period ◽  
Swarm Intelligence Algorithm

The lot streaming (LS) problem in job shop with equal-size sub-lots and intermittent idling is considered. An effective swarm intelligence algorithm with an artificial bee colony (ABC) algorithm is proposed for the minimization of total penalties of tardiness and earliness. In the first period of ABC, the employed bee phase and the onlooker bee phase are both for lot/sub-lot scheduling. In the second period, the LS conditions are determined in the employed bee phase and the lot/sub-lot is scheduled in the onlooker phase. The worst solution of the swarm is replaced with the elite one every few cycles. Computational results show the promising advantage of ABC.

scholarly journals Artificial Bee Colony Algorithm with Time-Varying Strategy

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Quande Qin ◽  
Shi Cheng ◽  
Qingyu Zhang ◽  
Li Li ◽  
Yuhui Shi
Keyword(s):  
Swarm Intelligence ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Experimental Studies ◽  
Population Based ◽  
Time Varying ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Linear And Nonlinear ◽  
Proper Balance

Artificial bee colony (ABC) is one of the newest additions to the class of swarm intelligence. ABC algorithm has been shown to be competitive with some other population-based algorithms. However, there is still an insufficiency that ABC is good at exploration but poor at exploitation. To make a proper balance between these two conflictive factors, this paper proposed a novel ABC variant with a time-varying strategy where the ratio between the number of employed bees and the number of onlooker bees varies with time. The linear and nonlinear time-varying strategies can be incorporated into the basic ABC algorithm, yielding ABC-LTVS and ABC-NTVS algorithms, respectively. The effects of the added parameters in the two new ABC algorithms are also studied through solving some representative benchmark functions. The proposed ABC algorithm is a simple and easy modification to the structure of the basic ABC algorithm. Moreover, the proposed approach is general and can be incorporated in other ABC variants. A set of 21 benchmark functions in 30 and 50 dimensions are utilized in the experimental studies. The experimental results show the effectiveness of the proposed time-varying strategy.

scholarly journals Optimization of discrete wavelet transform features using artificial bee colony algorithm for texture image classification

2019 ◽  
Vol 9 (6) ◽  
pp. 5253
Author(s):  
Fthi M. Albkosh ◽  
Muhammad Suzuri Hitam ◽  
Wan Nural Jawahir Hj Wan Yussof ◽  
Abdul Aziz K Abdul Hamid ◽  
Rozniza Ali
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Texture Classification ◽  
Optimization Technique ◽  
Image Texture ◽  
Discrete Wavelet ◽  
Bee Colony ◽  
Selection Of

Selection of appropriate image texture properties is one of the major issues in texture classification. This paper presents an optimization technique for automatic selection of multi-scale discrete wavelet transform features using artificial bee colony algorithm for robust texture classification performance. In this paper, an artificial bee colony algorithm has been used to find the best combination of wavelet filters with the correct number of decomposition level in the discrete wavelet transform.  The multi-layered perceptron neural network is employed as an image texture classifier.  The proposed method tested on a high-resolution database of UMD texture. The texture classification results show that the proposed method could provide an automated approach for finding the best input parameters combination setting for discrete wavelet transform features that lead to the best classification accuracy performance.

An Overview of the Last Advances and Applications of Artificial Bee Colony Algorithm

2017 ◽  
pp. 520-540
Author(s):  
Airam Expósito Márquez ◽  
Christopher Expósito-Izquierdo
Keyword(s):  
Swarm Intelligence ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Fish Schools ◽  
Natural Systems ◽  
Bee Colony ◽  
Bee Foraging ◽  
Artificial Nature ◽  
Continuous Optimization Problems

Swarm Intelligence is defined as collective behavior of decentralized and self-organized systems of a natural or artificial nature. In the last years and today, Swarm Intelligence has proven to be a branch of Artificial Intelligence that is able to solving efficiently complex optimization problems. Some of well-known examples of Swarm Intelligence in natural systems reported in the literature are colony of social insects such as bees and ants, bird flocks, fish schools, etc. In this respect, Artificial Bee Colony Algorithm is a nature inspired metaheuristic, which imitates the honey bee foraging behaviour that produces an intelligent social behaviour. ABC has been used successfully to solve a wide variety of discrete and continuous optimization problems. In order to further enhance the structure of Artificial Bee Colony, there are a variety of works that have modified and hybridized to other techniques the standard version of ABC. This work presents a review paper with a survey of the modifications, variants and applications of the Artificial Bee Colony Algorithm.

ABCluster: the artificial bee colony algorithm for cluster global optimization

2015 ◽  
Vol 17 (37) ◽  
pp. 24173-24181 ◽  
Author(s):  
Jun Zhang ◽  
Michael Dolg
Keyword(s):  
Global Optimization ◽  
Swarm Intelligence ◽  
Heuristic Algorithm ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Applied Mathematics ◽  
Fundamental Importance ◽  
Interesting Problem ◽  
Bee Colony

Global optimization of cluster geometries is of fundamental importance in chemistry and an interesting problem in applied mathematics. We apply a swarm-intelligence based heuristic algorithm, i.e. the artificial bee colony algorithm to solve this problem for various kinds of clusters.

Sign in / Sign up

Export Citation Format

Share Document