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Optimization of pressure paths in hydrodynamic deep drawing assisted by radial pressure with inward flowing liquid using a hybrid method

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2018年第5-8期97卷 2587-2601页

作者： Sadegh-Yazdi, Milad Bakhshi-Jooybari, Mohammad Shakeri, Mohsen Gorji, Hamid Khademi, Maziar Babol Noshirvani Univ Technol Dept Mech Engn Adv Mat Forming Res Ctr Babol Sar *** Iran Babol Noshirvani Univ Technol Dept Mech Engn Fuel Cell Res Ctr Babol Sar *** Iran

Hydroforming is a convenient method for applying fluid to produce parts with high strength-to-weight ratio. Hydrodynamic deep drawing assisted by radial pressure with inward flowing liquid process is considered as a type of hydroforming. In this method, radial and chamber pressures are two most important parameters. The values of these parameters at any moment play important roles on the quality of final part. In this study, based on a hybrid method, the chamber and radial pressure paths in hydrodynamic deep drawing assisted by radial pressure with inward flowing liquid process are optimized. In this method, an adaptive simulation that is integrated with the fuzzy control system with the artificial bee colony (abc) algorithm is used to determine the optimized radial and chamber pressure paths. The achievement of a conical part with minimum thinning and without wrinkling has been defined as the optimization goal. The validity of radial and chamber pressure paths obtained from optimization algorithm is verified through an experiment. Results showed that utilization of the optimized loading path yields a part with lower maximum thinning and without wrinkling.

关键词： Hydroforming Optimization Fuzzy control system abc algorithm Adaptive simulation

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Security in Cognitive Radio Network: Defense against Primary User Emulation attacks using Genetic Artificial Bee Colony (Gabc) algorithm

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 109卷 479-487页

作者： Elghamrawy, Sally M. MISR Higher Inst Engn & Technol Mansoura Egypt Sci Res Grp Egypt SRGE Giza Egypt

Due to the exponential increase in the number of wireless devices and in data rates demands, the shortage of the free frequency spectrum resources becomes a critical problem. Cognitive Radio Network (CRN) is considered as an emerging technology that eases off the problem of spectrum shortage by optimizing the usage of the available spectrum through giving the unlicensed Secondary Users (SU) the opportunity to exploit the spectrum without causing any interference with the Primary Users' (PU) usage. Securing the CRN is a crucial problem that must be addressed. Primary User Emulation (PUE) attacks are one of the main threats in deploying CRN, where the Malicious Users mimic the PU signal to confuse the other SUs and corrupt the spectrum sensing process. Many efforts had been made for securing the spectrum sensing process. In this paper, a hybrid Genetic Artificial Bee Colony (Gabc) algorithm is proposed to optimize the spectrum utilization by detecting the PUE attacks and enhancing the probability of detection. Gabc integrates the Genetic operators with abc algorithm to reach the balance between exploitation and exploration to find the optimal solution. The simulations results show promising performance of Gabc in optimizing spectrum sensing, when compared with recent detection algorithms. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Securing spectrum sensing Primary user emulation PUE Cognitive radio network (CRN) Genetic algorithm abc algorithm

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Modeling and optimization of hydrogenation of CO2: Estimation of kinetic parameters via Artificial Bee Colony (abc) and Differential Evolution (DE) algorithms

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2019年第10期44卷 4630-4649页

作者： Najari, Sara Grof, Gyula Saeidi, Samrand Gallucci, Fausto Tarbiat Modares Univ Dept Chem Engn Tehran 14115114 Iran Budapest Univ Technol & Econ Dept Energy Engn Budapest Hungary Eindhoven Univ Technol Dept Chem Engn & Chem Inorgan Membranes & Membrane Reactors Eindhoven Netherlands

Global warming, climate change, fossil fuel depletion and steep hikes in the price of environmentally friendly hydrocarbons motivate researchers to investigate CO2 hydrogenation for hydrocarbons production. However, due to the reaction complexities and varieties of produced species, the process mechanism and subsequently estimation of the kinetic parameters have been controversial yet. Therefore, estimating the kinetic parameters using Artificial Bee Colony (abc) and Differential Evolution (DE) optimization algorithms based on Langmuir-Hinshelwood-Hougen-Watson (LHHW) mechanism is proposed as a possible remedy to fulfil the requirements. To this end, a one-dimensional heterogeneous model comprising detailed reaction rates of reverse water gas shift (RWGS), Fisher-Tropsch (FT) reactions and direct hydrogenation (DH) of CO2 is developed. It is observed that abc exhibiting 6.3% error in predicting total hydrocarbons selectivity is superior to DE algorithm with 32.9% error. Therefore, the model employed the estimated kinetic parameters obtained via abc algorithm, is exploited for products distribution analysis. Results reveal that maximum 73.21% hydrocarbons (C-1-C-4) selectivity can be achieved at 573 K and 1 MPa with 0.85% error compared to the experimental value of 72.59%. Accordingly, the proposed model can be exploited as a powerful tool for evaluating and predicting the performance of CO2 hydrogenation to hydrocarbons process. (C) 2019 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： CO2 hydrogenation RWGS FT Optimization abc algorithm DE 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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Inverse alloy solidification problem including the material shrinkage phenomenon solved by using the bee algorithm

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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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Pricing and inventory control decisions in the stochastic hybrid production systems with multiple recovery options

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RAIRO-OPERATIONS RESEARCH 2021年第5期55卷 2685-2709页

作者： Naseri, Foruzan Esmaeili, Maryam Seifbarghy, Mehdi Heydari, Tahereh Alzahra Univ Dept Ind Engn Fac Engn Tehran Iran Payame Noor Univ PNU Fac Engn Dept Ind Engn POB 19395-3697 Tehran Iran

Although pricing and inventory control are crucial decisions in each production system, these decisions are usually investigated separately. This paper considers pricing and inventory control decisions simultaneously in a hybrid production system. The hybrid production system has two recovery options, remanufacturing and refurbishing sites. The demand follows Poisson distribution, which depends on the sale price of each product. Returned products arrive according to a Poisson process. Each returned product can be remanufactured, refurbished, or disposed. The time to manufacturing, refurbishing, and remanufacturing a product also follows an exponential distribution. By modeling the system as a Markov chain, the long-run expected profit function is obtained in terms of the dispose-down-to level of returned products and the order-up-to level and the sale price of serviceable products. A three-dimensional state space of the Markov Chain dependent to the sale price is developed considering pricing and inventory control decisions simultaneously with remanufacturing and refurbishing returned products. Since the model is a mixed integer nonlinear programming and known as complex models, the Artificial Bee Colony (abc) algorithm, simulation and complete search method are used to solve the problem. The results show that by increasing the purchase price of the returned products, the amount of returned products will increase. If the refurbishing cost of the returned products is high or the disposal cost is low, less inventory should be kept in the system with a high price of serviceable products. If the lost sale cost is high, the more inventory should be maintained. Moreover, by decreasing the price elasticity of demand, the customer's demand increases, and then more inventory should be maintained in the system.

关键词： Hybrid production system pricing abc algorithm base stock

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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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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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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 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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