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Inverse alloy solidification problem including the material shrinkage phenomenon solved by using the bee algorithm

INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2017年 87卷 295-301页

作者： Zielonka, Adam Hetmaniok, Edyta Slota, Damian Silesian Tech Univ Inst Math Kaszubska 23 PL-44100 Gliwice Poland

In the paper we solve the one-phase inverse problem of alloy solidifying within the casting mould, including the shrinkage of metal which results from the difference between densities of the liquid and solid phases. The process is modeled by means of the solidification in the temperature interval basing on the heat conduction equation with the source element enclosed, whereas the shrinkage of metal is modeled by the proper application of the mass balance equation. The investigated inverse problem consists in reconstruction of the heat transfer coefficient on the boundary of the casting mould on the basis of measurements of temperature read from the sensor placed in the middle of the mould. Functional expressing the error of approximate solution is minimized with the aid of Artificial Bee Colony Optimization algorithm.

关键词： Solidification Binary alloy Material shrinkage Swarm intelligence abc algorithm

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An artificial bee colony algorithm for design and optimize the fixed area layout problems

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2015年第9-12期78卷 2079-2095页

作者： Saravanan, M. Arulkumar, P. V. Sri Subramanya Coll Engn & Technol Palani Tamil Nadu India Bharath Niketan Engn Coll Dept Mech Engn Aundipatty India

The placement of production equipments plays a major role in designing a layout in cellular manufacturing. The better placement increases the productivity. This article introduces a new algorithm to design and optimize a fixed area cellular layout problem by using an artificial bee colony (abc) technique which is based on the intelligent foraging behavior of a honeybee. The objective of this article is to determine the physical arrangement of work centers by minimizing the total traveling distance of the product. Volume of the product and distance between the work centers are the important factors that affect layout design. Some relative importance factors like priority of products, hazardous moves, and back-tracking moves are considered in this article. Layout moment ratio helps to compare the different proposed layouts. The higher layout moment ratio is the more desirable layout. Also this article compares the results of abc technique with genetic algorithm (GA) and simulated annealing (SA) algorithm based on the total moment value, layout moment ratio, number of iterations, computation time, and back-tracking movements. Finally, it concluded that abc is a better technique to solve fixed area cellular layout problems than the mentioned algorithms with high dimensionality.

关键词： Cellular manufacturing abc algorithm GA SA algorithm Relative importance factors

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Optimal sizing of grid integrated hybrid PV-biomass energy system using artificial bee colony algorithm

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IET RENEWABLE POWER GENERATION 2016年第5期10卷 642-650页

作者： Singh, Shakti Kaushik, Subhash Chandra Thapar Univ Dept Elect & Instrumentat Engn Patiala 147004 Punjab India Indian Inst Technol Ctr Energy Studies Delhi India

This study presents an optimal sizing methodology for a stand-alone and grid connected PV-biomass hybrid energy system that serves the electricity demand of a typical village. However, this method is scalable and can be used in any test system. A recently developed artificial bee colony (abc) algorithm is used to detect out the optimum hybrid system configuration with the least levelised cost of energy while minimising annualised cost of the system. It has been observed from the results that a grid connected hybrid PV-biomass system is cost effective and reliable choice for rural electrification as compared with stand-alone hybrid PV-biomass energy system. It has been emerged from this study that the proposed system offers reliable and affordable electricity in a sustainable way by harnessing locally available natural resources. A brief comparison of results obtained from the abc algorithm and hybrid optimisation model for electric renewable (HOMER) has been carried out. Moreover, it is also observed from the results that the abc algorithm provides better results as compared with HOMER.

关键词： photovoltaic power systems hybrid power systems bioenergy conversion optimisation grid integrated hybrid PV-biomass energy system artificial bee colony algorithm electricity demand abc algorithm hybrid optimisation model for electric renewable HOMER

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Channel assignment based on bee algorithms in multi-hop cognitive radio networks

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IET COMMUNICATIONS 2014年第13期8卷 2356-2365页

作者： Ghasemi, Ahmad Masnadi-Shirazi, Mohammad Ali Biguesh, M. Qassemi, Foad Shiraz Univ Dept Elect & Comp Engn Shiraz Iran Kerman Grad Univ Technol Dept Civil Engn Kerman Iran

Spectrum management policies are responsible for poor utilisation of the radio spectrum. By carrying out dynamic spectrum management (DSM), cognitive radio (CR) can increase the radio spectrum in wireless systems efficiently. CR technology accounts for the improvement in the spectrum utilisation significantly. One issue of DSM in CR is the assignment of frequency channels among its users. Herein, a general model and four utility functions for optimal channel assignment in open spectrum systems such as CR networks have been defined. First, a new utility function with a better fairness than the other functions is proposed. Then, two new different channel assignment methods, based on the artificial bee colony (abc) and bee swarm optimisation (BSO) algorithms, are proposed, whereas other certain evolutionary algorithms and colour sensitive graph colouring (CSGC) are used to compare the performances. In order to decrease the search space, based on the channel availability and interference constraints a mapping process between the channel assignment matrix and the position of the bees has been proposed. Our simulation results, compared to the optimal solutions, show that our algorithms drastically improve network performance by reducing interference.

关键词： cognitive radio evolutionary computation matrix algebra channel allocation channel assignment matrix multihop cognitive radio networks spectrum management policies dynamic spectrum management DSM spectrum utilisation CR open spectrum systems artificial bee colony algorithm abc algorithm bee swarm optimisation algorithm BSO algorithm evolutionary algorithms colour sensitive graph colouring CSGC channel availability interference constraints

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Dynamic data clustering by combining improved discrete artificial bee colony algorithm with fuzzy logic

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2018年第3期12卷 164-172页

作者： Amiri, Ehsan Dehkordi, Mohammad Naderi Islamic Azad Univ Nourabad Mamasani Branch Comp Engn Dept Nourabad Mamasani Iran Islamic Azad Univ Najafabad Branch Comp Engn Fac Najafabad Iran

Data clustering is a method of partitioning data into different groups pursuant to some similarity or dissimilarity measure. Nowadays, several different technics are invented and introduced for data clustering such as heuristics and meta-heuristics. Many clustering algorithms fail when dealing with multi-dimensional data. In this research, we proposed an innovative fuzzy method with improved discrete artificial bee colony (ID is abc) for data clustering called FID is abc. The D is abc is a new version of artificial bee colony (abc) that first introduced to sort out the uncapacitated facility location problem (UFLP) and improved by the efficient genetic selection to solve dynamic clustering problem. The performance of our algorithm is evaluated and compared with some well-known algorithms. The results show that our algorithm has better performance in comparison with them.

关键词： data clustering artificial bee colony abc algorithm dataset fuzzy logic artificial intelligence

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The use of artificial bee colony algorithm to speed up the nanopowders synthesis during high energy ball milling

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POWDER TECHNOLOGY 2014年 264卷 61-70页

作者： Abdellahi, Majid Bahmanpour, Maryam Bahmanpour, Marjan Islamic Azad Univ Dept Mat Engn Najafabad Branch Najafabad Iran Islamic Azad Univ Dept Math Khorasgan Isfahan Branch Esfahan Iran Islamic Azad Univ Dept Comp Engn Najafabad Branch Najafabad Iran

The purpose of the present paper is to use artificial bee colony algorithm to speed up the nanopowders synthesis in the planetary mills. At first a direct relationship between the inverse of the milling time and the power of the planetary mill was established, which allows the validation of theoretical models proposed in the literature for the energy transfer in milling devices and the comparison of milling equipment efficiencies. Afterwards based on the obtained relation, eight design parameters in milling, namely, number of balls, ball diameter, vial radius, vial height, ball diameter distribution coefficient, plate spinning rate, vial spinning rate and distance between the center of the plate and the center of the vial were optimized. For evaluating the optimized parameters and their practical effectiveness, a case study was conducted. Result showed that by applying the optimal parameters instead of experimental (non-optimized) ones, a route with less time was obtained for the production of nanostructured powders in planetary mills. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Nanostructured powders Milling Optimization abc algorithm

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A robust service composition and optimal selection method for cloud manufacturing

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2022年第4期60卷 1134-1152页

作者： Yang, Bo Wang, Shilong Li, Shi Jin, Tianguo Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China Beijing Aerosp XinFeng Mech Equipment Co Ltd Beijing Peoples R China Harbin Inst Technol Sch Mechatron Engn Harbin Peoples R China

During the process of cloud manufacturing, various uncertainties in the real world could have a significant impact on the smooth execution of task, and could render the planned composite manufacturing service (CMS) inefficient or even ineffective. Therefore, this paper proposes an optimal selection method to enhance the robustness of CMS during the planning stage. Firstly, the structure of robust CMS is proposed by arranging the preferred and alternative services for each subtask, and a robust service composition and optimal selection (rSCOS) model of cloud manufacturing is constructed by defining the expected Quality of Service. Then, the gabc-GWO (guiding artificial bee colony - grey wolf optimisation) algorithm is proposed to solve the rSCOS model efficiently, in which three improvement strategies for abc algorithm are designed according to the characteristics of GWO. Finally, two experiments are implemented and the results show that QoS of the preferred scheme of robust CMS is approximately 1.29% lower than that of CMS on average, while its robustness is improved by 1.81% and 13.14% depending on the two robustness indexes. Compared with other commonly-used intelligence optimisation algorithms, gabc-GWO algorithm possesses better search performance without significantly increasing time consumption, which makes it more suitable for solving rSOCS problems.

关键词： Cloud manufacturing service exception service composition and optimal selection robustness abc algorithm GWO

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Artificial bee colony optimization-inspired synergetic study of fractional-order economic production quantity model

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SOFT COMPUTING 2020年第20期24卷 15341-15359页

作者： Rahaman, Mostafijur Mondal, Sankar Prasad Shaikh, Ali Akbar Pramanik, Prasenjit Roy, Samarjit Maiti, Manas Kumar Mondal, Rituparna De, Debashis Indian Inst Engn Sci & Technol Dept Math Sibpur 711103 Howrah India Maulana Abul Kalam Azad Univ Technol Dept Appl Sci Nadia 741249 W Bengal India Univ Burdwan Dept Math Burdwan W Bengal India Vidyasagar Univ Dept Appl Math Oceanol & Comp Programming Paschim Medinipur W Bengal India Maulana Abul Kalam Azad Univ Technol Dept Comp Sci & Engn Nadia 741249 W Bengal India Mahishadal Raj Coll Dept Math Purba Medinipur 721628 W Bengal India Sardar Vallabhbhai Natl Inst Technol Appl Math & Humanities Dept Surat Gujrat India

Inventory control is one of the most widely recognized issues in the reality. This investigation manages the utilization of fractional derivatives and integration on an inventory control problem. The memory of a dynamical model is a highly concerned issue which is commonly neglected by the models described in terms of integer-order differential equation. The memory capturing the power of fractional derivative (in Caputo's sense) is utilized here to describe an economic production quantity model with deterioration when the demand depends on price and stock and production is stock dependent. Also, this study covers the integer-order model with the same assumptions as a memoryless model and a particular case of the fractional model. Due to the complex nature of the model, numerical optimization with the help of a modified artificial bee colony algorithm is done instead of the analytical approach of optimization. Finally, we have performed a sensitivity analysis in order to make a fruitful conclusion.

关键词： Fractional-order differential equation EPQ model Laplace transformation abc algorithm

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A novel relationship strength model for online social networks

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MULTIMEDIA TOOLS AND APPLICATIONS 2017年第16期76卷 17577-17594页

作者： Ju, Chunhua Tao, Wanqiong Zhejiang Gongshang Univ Contemporary Business & Trade Res Ctr Hangzhou 310018 Peoples R China Zhejiang Gongshang Univ Contemporary Business & Collaborat Innovat Res Ct Hangzhou 310018 Peoples R China Zhejiang Gongshang Univ Coll Comp Sci & Informat Engn Hangzhou 310018 Peoples R China

One of the key foundations of personalized recommendation in a social network is the relationship strength between social network users. The improvement for recommendation accuracy is mostly tied to the precise evaluation of the relationship strengths. With most of the selected factors affecting the relationship strength between users are too simple, the existed researches show low accuracy in calculating the strength, especially those factors related to topic and indirect links. We propose an online social networks users relationship strength estimation model which incorporates topic classification and indirect relationship. We adopt K-means clustering method using abc algorithm to cluster all the interactive activity documents and calculate the correlation between clusters and activity topic name. After that, we compute the relationship strength between users which belong to the same topic on top of the user profile and interaction data. To accomplish this we employ a language model based on sentiment classification approach and take similarity, timeliness, and interactivity into account. We conduct experiments on two microblog datasets and the results show that the proposed model is promising and can be used to improve the performances of various applications.

关键词： Online social networks Topic classification Indirect relationship K-means clustering method abc algorithm Sentiment classification

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A novel approach to accelerate calibration process of a k-nearest neighbours classifier using GPU

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2017年 104卷 114-129页

作者： Singh, Amreek Deep, Kusum Grover, Pallavi Indian Inst Technol Roorkee Roorkee 247667 Uttar Pradesh India Snow & Avalanche Study Estab Chandigarh 160036 India

General purpose data parallel computing with graphical processing unit (GPU) is much structured today with NVIDIA (R) CUDA and other parallel programming frameworks. Exploiting the CUDA programming framework, the present work proposes a novel methodology formulated around the GPU hardware architecture and memory hierarchy to accelerate the calibration process of a classification model named eNN10. Primarily developed for avalanche forecasting, eNN10 is based on brute force k-nearest neighbours (k-NN) approach and employs snow-meteorological variables to search for past days with similar conditions. The events associated with past similar days are then analysed to generate forecast. The model is required to be calibrated regularly to ensure higher degree of forecast accuracy in terms of Heidke skill score (HSS). The calibration of eNN10 is carried out by Artificial Bee Colony (abc) algorithm, a swarm intelligence driven population based metaheuristic algorithm, and it requires thousands of HSS evaluations during the complete calibration process. A MATLAB sequential code for calibration runs for over 400 minutes and the proposed methodology delivered about 10 x acceleration in calibration process. The methodology combines primitives of parallel implementations of brute force k-NN algorithm with that of population based metaheuristic algorithms and is scalable to deal with other similar real-world problems. The major objective of this paper is to highlight the methodology and associated future research areas. (C) 2017 Elsevier Inc. All rights reserved.

关键词： NVIDIA CUDA GPU SIMD abc algorithm k-nearest neighbours Avalanche forecasting

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