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A MULTI-OBJECTIVE APPROACH TO THE ASSIGNMENT OF STOCK KEEPING UNITS TO UNIDIRECTIONAL PICKING LINES

SOUTH AFRICAN JOURNAL OF INDUSTRIAL ENGINEERING 2017年第1期28卷 190-209页

作者： le Roux, G. J. Visagie, S. E. Univ Stellenbosch Dept Logist Stellenbosch South Africa

An order picking system in a distribution centre consisting of parallel unidirectional picking lines is considered. The objectives are to minimise the walking distance of the pickers, the largest volume of stock on a picking line over all picking lines, the number of small packages, and the total penalty incurred for late distributions. The problem is formulated as a multi-objective multiple knapsack problem that is not solvable in a realistic time. Population-based algorithms, including the artificial bee colony algorithm and the genetic algorithm, are also implemented. The results obtained from all algorithms indicate a substantial improvement on all objectives relative to historical assignments. The genetic algorithm delivers the best performance.

关键词： PICKERS (Weaving) STOCKS (Finance) bees algorithm GENETIC algorithms PROBLEM solving

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Dynamical and topological robustness of the mammalian cell cycle network: A reverse engineering approach

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BIOSYSTEMS 2014年第1期115卷 23-32页

作者： Ruz, Gonzalo A. Goles, Eric Montalva, Marco Fogel, Gary B. Univ Adolfo Ibanez Fac Ingn & Ciencias Santiago Chile Nat Select Inc San Diego CA 92121 USA

A common gene regulatory network model is the threshold Boolean network, used for example to model the Arabidopsis thaliana floral morphogenesis network or the fission yeast cell cycle network. In this paper, we analyze a logical model of the mammalian cell cycle network and its threshold Boolean network equivalent. Firstly, the robustness of the network was explored with respect to update perturbations, in particular, what happened to the attractors for all the deterministic updating schemes. Results on the number of different limit cycles, limit cycle lengths, basin of attraction size, for all the deterministic updating schemes were obtained through mathematical and computational tools. Secondly, we analyzed the topology robustness of the network, by reconstructing synthetic networks that contained exactly the same attractors as the original model by means of a swarm intelligence approach. Our results indicate that networks may not be very robust given the great variety of limit cycles that a network can obtain depending on the updating scheme. In addition, we identified an omnipresent network with interactions that match with the original model as well as the discovery of new interactions. The techniques presented in this paper are general, and can be used to analyze other logical or threshold Boolean network models of gene regulatory networks. (C) 2013 Elsevier Ireland Ltd. All rights reserved.

关键词： Gene regulatory networks Boolean networks Threshold networks Update robustness Topology robustness bees algorithm

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A novel method for automatic modulation recognition

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APPLIED SOFT COMPUTING 2012年第1期12卷 453-461页

作者： Sherme, Ataollah Ebrahimzade Babol Univ Technol Fac Elect & Comp Engn Babol Sar Iran

Automatic recognition of the digital modulation plays an important role in various applications. This paper investigates the design of an accurate system for recognition of digital modulations. First, it is introduced an efficient pattern recognition system that includes two main modules: the feature extraction module and the classifier module. Feature extraction module extracts a suitable combination of the higher order moments up to eighth, higher order cumulants up to eighth and instantaneous characteristics of digital modulations. These combinations of the features are applied for the first time in this area. In the classifier module, two important classes of supervised classifiers, i.e., multi-layer perceptron (MLP) neural network and hierarchical multi-class support vector machine based classifier are investigated. By experimental study, we choose the best classifier for recognition of the considered modulations. Then, we propose a hybrid heuristic recognition system that an optimization module is added to improve the generalization performance of the classifier. In this module we have used a new optimization algorithm called bees algorithm. This module optimizes the classifier design by searching for the best value of the parameters that tune its discriminant function, and upstream by looking for the best subset of features that feed the classifier. Simulation results show that the proposed hybrid intelligent technique has very high recognition accuracy even at low levels of SNR with a little number of the features. (C) 2011 Elsevier B. V. All rights reserved.

关键词： Modulation recognition Pattern recognition bees algorithm Hierarchical support vector machine based classifier Combination of the higher order moments (up to eighth) and higher order cumulants (up to eighth) Spectral characteristics

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Bee-Inspired algorithms Applied to Vehicle Routing Problems: A Survey and a Proposal

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MATHEMATICAL PROBLEMS IN ENGINEERING 2017年第1期2017卷 1-20页

作者： Masutti, Thiago A. S. de Castro, Leandro N. Univ Prebiteriana Mackenzie Nat Comp & Machine Learning Lab LCoN Grad Program Elect Engn & Comp R Consolacao 930 BR-01302000 Sao Paulo SP Brazil

Vehicle routing problems constitute a class of combinatorial optimization tasks that search for optimal routes (e.g., minimal cost routes) for one or more vehicles to attend a set of nodes (e.g., cities or customers). Finding the optimal solution to vehicle routing tasks is an NP-hard problem, meaning that the size of problems that can be solved by exhaustive search is limited. From a practical perspective, this class of problems has a wide and important set of applications, from the distribution of goods to the integrated chip design. Rooted on the use of collective intelligence, swarm-inspired algorithms, more specifically bee-inspired approaches, have been used with good performance to solve such problems. In this context, the present paper provides a broad review on the use of bee-inspired methods for solving vehicle routing problems, introduces a new approach to solve one of the main tasks in this area (the travelling salesman problem), and describes open problems in the field.

关键词： bees algorithm VEHICLE routing problem MATHEMATICAL optimization PHYSICAL distribution of goods PROBLEM solving

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An Adaptive Threshold-Based Modified Artificial Bee Colony Optimization Technique for Virtual Machine Placement in Cloud Datacenters

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IEEE ACCESS 2024年 12卷 94296-94309页

作者： Khalid Karim, Faten Sivakumar, Nithya Rekha Alshetewi, Sameer Ibrahim, Ahmed Zohair Venkatesan, Geetha Princess Nourah Bint Abdulrahman Univ PNU Coll Comp & Informat Sci Dept Comp Sci POB 8428 Riyadh 11671 Saudi Arabia Minist Def Gen Informat Technol Dept Excellence Serv Directorate Execut Affairs Riyadh 11564 Saudi Arabia REVA Univ Sch Appl Sci Dept Comp Sci Bengaluru 560064 India

The usage of cloud computing service platforms are exponentially growing to provide on-demand services for end-users for using advanced technologies. These platform services are achieved through resource virtualization to maximize the resource usage and minimize energy requirements. Energy consumption is a key factor for designing efficient and manageable cloud data centers. Optimal techniques are used for placing virtual machines in physical machines to reduce the energy consumption ratio of physical hosts. This paper proposes a novel efficient virtual machines placement algorithm for a cloud computing environment. This method exploits a modified artificial bee colony optimization algorithm for identifying under-utilized physical machines based on energy consumption and resource allocation charts. An adaptive threshold method is then proposed to select suitable threshold levels for energy consumption to identify under-utilized physical host machines. A comparative analysis with state of art methods is carried out by using the CloudSim 3.0 simulator. Simulation results show the superiority of our method, able to achieve the highest accuracy values of 97.2% for accuracy and of 97.9% for precision rate, thus confirming the efficacy of our approach for virtual machine placement in cloud environments.

关键词： Cloud computing Optimization methods Energy consumption Virtual machining Virtualization Data centers Heuristic algorithms bees algorithm Resource management Virtual environments Adaptive threshold modified artificial bee colony optimization VM placement resource management virtual service handling optimization

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Metaheuristic approach in machinability evaluation of silicon carbide particle/glass fiber-reinforced polymer matrix composites during electrochemical discharge machining process

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MEASUREMENT & CONTROL 2019年第7-8期52卷 1167-1176页

作者： Antil, Parvesh Singh, Sarbjit Singh, Sunpreet Prakash, Chander Pruncu, Catalin Iulian Chaudhary Charan Singh Haryana Agr Univ Coll Agr Engn & Technol Hisar Hisar India Punjab Engn Coll Chandigarh India Lovely Profess Univ Sch Mech Engn Phagwara India Univ Birmingham Sch Engn Dept Mech Engn Birmingham W Midlands England Imperial Coll London Dept Mech Engn Exhibit Rd London SW7 2AZ England

The advanced manufacturing and machining techniques are adopting a population-based metaheuristic algorithm for production, predicting and decision-making. Using the same approach, this paper deals with the application of bees algorithm and differential evolution to forecast the optimal parametric values aiming to obtain maximum material removal rate during electrochemical discharge machining of silicon carbide particle/glass fiber-reinforced polymer matrix composite. The bees algorithm follows swarm-based approach, while differential evolution works on a population-based approach. The experimental design was prepared on the basis of Taguchi's methodology using an L-16 orthogonal array. For the experimental analysis, the main variables in the process, that is, electrolyte concentration (g/L), inter-electrode gap (mm), duty factor and voltage (volts), were selected as main input parameters, and material removal rate (mg/min) was adjudged as output quality characteristic. A comparative investigation reveals that the maximum material removal rate was obtained by the parametric value proposed by differential evolution that follows the bees algorithm and Taguchi's methodology. Furthermore, the results prove that the differential evolution algorithm has better collective assessment capability with a rapid converging rate.

关键词： bees algorithm differential evolution electrochemical discharge machining Taguchi's methodology

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Heuristic Artificial Bee Colony algorithm for Uncovering Community in Complex Networks

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MATHEMATICAL PROBLEMS IN ENGINEERING 2017年第1期2017卷 1-12页

作者： Guo, Yuquan Li, Xiongfei Tang, Yufei Li, Jun Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Florida Atlantic Univ Comp & Elect Engn & Comp Sci Dept Boca Raton FL 33431 USA Jilin Univ Finance & Econ Sch Management Sci & Informat Engn Changchun 130117 Peoples R China Changchun Univ Sci & Technol Dept Appl Math Changchun 130022 Peoples R China

Community structure is important for us to understand the functions and structure of the complex networks. In this paper, Heuristic Artificial Bee Colony (HABC) algorithm based on swarm intelligence is proposed for uncovering community. The proposed HABC includes initialization, employed bee searching, onlooker searching, and scout bee searching. In initialization stage, the nectar sources with simple community structure are generated through network dynamic algorithm associated with complete subgraph. In employed bee searching and onlooker searching stages, the searching function is redefined to address the community problem. The efficiency of searching progress can be improved by a heuristic function which is an average agglomerate probability of two neighbor communities. Experiments are carried out on artificial and real world networks, and the results demonstrate that HABC will have better performance in terms of comparing with the state-of-the-art algorithms.

关键词： HEURISTIC programming bees algorithm SWARM intelligence SUBGRAPHS MATHEMATICAL analysis

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Optimization of the geometrical parameters for elevated temperature hydro-mechanical deep drawing process of 2024 aluminum alloy

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING 2021年第2期235卷 151-161页

作者： Yaghoubi, S. Fereshteh-Saniee, F. Bu Ali Sina Univ Fac Engn Dept Mech Engn Hamadan 65178 Hamadan Iran

This research is concerned with the effects of the geometrical parameters of the die in elevated temperature Hydro-Mechanical Deep Drawing (HMDD) process of 2024 aluminum alloy. A Group Method of Data Handling (GMDH) process was used to train a neural network in order to study the process behavior. Based on the maximum reduction in sheet thickness and the uniformity of the final product, an objective function was constructed. The bees algorithm (BA) was used to achieve the optimal values for process variables. To verify the simulation results, they were compared with the experimental findings gained via this research and an appropriate correlation was observed between these results. This comparison showed that, by optimization of the geometrical parameters of the process, the value of the combined objective function was the best one compared with all of the cases tried in the present investigation.

关键词： Hydro-mechanical deep drawing (HMDD) maximum reduction in thickness thickness variation GMDH neural network bees algorithm

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DEVELOPMENT OF A DECISION SUPPORT SYSTEM FOR SOLVING CONTAINER LOADING PROBLEMS

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TRANSPORT 2010年第2期25卷 138-147页

作者： Dereli, Tuerkay Das, Guelesin Sena Gaziantep Univ Dept Ind Engn TR-27310 Gaziantep Turkey TUBITAK EU Framework Programmes Natl Coordinat Off Ankara Turkey

The globalization of supply chains and rising fuel costs are forcing container carriers both to minimize the number of trips and to maximize available container space. This makes container loading (CL) a critical process especially in real-life applications. Container loading (CL), which is a difficult problem to be solved, has many applications in container transportation and distribution industries. This article presents a container loading support system (CLSS). The proposed CLSS composes of three main components, including a hybrid bees algorithm as the main computational algorithm, the graphical user interface (GUI) and a simulation program. The aim of the designed system is to make the packing pattern more visible to the user in order to simplify the loading process. An illustrative example a CL problem from literature - is also provided to introduce the operation of the system and to prove its efficiency.

关键词： decision support system container loading problem bees algorithm graphical user interface swarm intelligence

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An Efficient Approach for Energy Consumption Optimization and Management in Residential Building Using Artificial Bee Colony and Fuzzy Logic

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MATHEMATICAL PROBLEMS IN ENGINEERING 2016年第1期2016卷 1-13页

作者： Wahid, Fazli Kim, Do Hyeun Jeju Natl Univ Dept Comp Engn Jeju 690756 South Korea

The energy management in residential buildings according to occupant's requirement and comfort is of vital importance. There are many proposals in the literature addressing the issue of user's comfort and energy consumption (management) with keeping different parameters in consideration. In this paper, we have utilized artificial bee colony (ABC) optimization algorithm for maximizing user comfort and minimizing energy consumption simultaneously. We propose a complete user friendly and energy efficient model with different components. The user set parameters and the environmental parameters are inputs of the ABC, and the optimized parameters are the output of the ABC. The error differences between the environmental parameters and the ABC optimized parameters are inputs of fuzzy controllers, which give the required energy as the outputs. The purpose of the optimization algorithmis to maximize the comfort index and minimize the error difference between the user set parameters and the environmental parameters, which ultimately decreases the power consumption. The experimental results show that the proposed model is efficient in achieving high comfort index along with minimized energy consumption.

关键词： DWELLINGS ENERGY consumption FUZZY logic bees algorithm ENERGY management POWER resources

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