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Optimum design of a reinforced concrete beam using artificial bee colony algorithm

COMPUTERS AND CONCRETE 2012年第3期10卷 295-306页

作者： Ozturk, H. T. Durmus, Ay. Durmus, Ah. Karadeniz Tech Univ Dept Civil Engn Trabzon Turkey

Optimum cost design of a simply supported reinforced concrete beam is presented in this paper. In the formulation of the optimum design problem, the height and width of the beam, and reinforcement steel area are treated as design variables. The design constraints are implemented according to ACI 318-08 and studies in the literature. The objective function is taken as the cost of unit length of the beam consisting the cost of concrete, steel and shuttering. The solution of the design problem is obtained using the artificial bee colony algorithm which is one of the recent additions to metaheuristic techniques. The artificial bee colony algorithm is imitated the foraging behaviors of bee swarms. In application of this algorithm to the constraint problem, Deb's constraint handling method is used. Obtained results showed that the optimum value of numerical example is nearly same with the existing values in the literature.

关键词： optimization metaheuristic artificial bee colony algorithm reinforced concrete beams

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An effective artificial bee colony algorithm for the flexible job-shop scheduling problem

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2012年第1-4期60卷 303-315页

作者： Wang, Ling Zhou, Gang Xu, Ye Wang, Shengyao Liu, Min Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 100084 Peoples R China

An effective artificial bee colony (ABC) algorithm is proposed in this paper for solving the flexible job-shop scheduling problem with the criterion to minimize the maximum completion time (makespan). The ABC algorithm stresses the balance between global exploration and local exploitation. First, multiple strategies are utilized in a combination to generate the initial solutions with certain quality and diversity as the food sources. Second, crossover and mutation operators are well designed for machine assignment and operation sequence to generate the new neighbor food sources for the employed bees. Third, a local search strategy based on critical path is proposed and embedded in the searching framework to enhance the local intensification capability for the onlooker bees. Meanwhile, an updating mechanism of population by generating the scout bees with the initialing strategy is proposed to enrich the searching behavior and avoid the premature convergence of the algorithm. In addition, a well-designed left-shift decoding scheme is employed to transform a solution to an active schedule. Numerical simulation results based on well-known benchmarks and comparisons with some existing algorithms demonstrate the effectiveness of the proposed ABC algorithm.

关键词： Flexible job-shop scheduling problem Makespan artificial bee colony algorithm Machine assignment Operation sequence Critical path

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USING AN EFFICIENT artificial bee colony algorithm FOR PROTEIN STRUCTURE PREDICTION ON LATTICE MODELS

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2012年第3B期8卷 2049-2064页

作者： Lin, Cheng-Jian Su, Shih-Chieh Natl Chin Yi Univ Technol Dept Comp Sci & Informat Engn Taichung 41170 Taiwan Natl Chung Cheng Univ Dept Comp Sci & Informat Engn Chiayi 62102 Taiwan

The well-known artificial bee colony (ABC) algorithm is one of the most recently introduced swarm-based algorithms. The ABC system combines both local and global search methods in an attempt to balance exploration and exploitation processes, and hopefully, it can be successfully applied to solve real-world problems. In the past, the Science Magazine named the protein folding problem, (PFP) as one of the 125 biggest unsolved problems in science. The PEP addresses the question of how the amino acid sequence (A A S) of a specific protein dictates its structure. In the study, we present a modified ABC (MABC) algorithm for the PEP with both 2D and 3D HP models. We demonstrate that our algorithm can be applied successfully to the protein folding problem based on the hydrophobic-polar lattice model. The simulation results show that the modified artificial bee colony algorithm can successfully be applied to the protein folding problem.

关键词： Protein folding Protein structure prediction HP-model Swarm intelligence artificial bee colony algorithm

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Mitigating closed-loop supply chain risk through assessment of production cost, disruption cost, and reliability

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2024年 270卷

作者： Dou, Runliang Liu, Xin Hou, Yanchao Wei, Yixin Tianjin Univ Coll Management & Econ 92 Weijin Rd Tianjin 300072 Peoples R China

Modern supply chains are affected by high disruption risks owing to their production complexity and globalization characteristics. In addressing supply chain disruption risks, we observed mutual conflict among reliability, disruption costs, and production costs. Therefore, the relationship among production costs, disruption costs, and reliability must be balanced to accommodate supply chain disruptions more effectively. This study establishes a four-tier closed-loop supply chain multi-objective optimization model based on a propagation structure. This study is the first to unify these three objectives for supply chain optimization. Additionally, the artificial bee colony (ABC) algorithm and gradient descent method are combined for the solution. To demonstrate the effectiveness of this method, the classical ABC algorithm is compared with a simulated annealing algorithm through a case study in the semiconductor industry. Our research findings indicate that considering the industrial applicability of the semiconductor industry, increasing supply chain reliability and disruption cost weights, and assigning overall supply chain orders from a holistic perspective can effectively address supply chain disruption risk.

关键词： Supply chain Disruption artificial bee colony algorithm Closed-loop Semiconductor industry

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Metaheuristic Optimization-based Fault-tolerant Formation Control of Multiple UAVs

Metaheuristic Optimization-based Fault-tolerant Formation Co...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Saied, Majd Rasheed, Farah Francis, Clovis Lebanese Int Univ LIU Sch Engn Dept Elect & Elect Engn Bekaa Lebanon Lebanese Univ Fac Engn Sci Res Ctr Engn Hadath Lebanon

ISBN: (数字)9781665405935

ISBN: (纸本)9781665405935

In this work, we propose a fault-tolerant formation control strategy against severe and non-severe actuator failures, applied to multiple hexarotor Unmanned Aerial Vehicles (UAVs). Initially, the UAVs are operating in a prescribed leaderless formation topology using the artificial bee colony (ABC) optimization algorithm. As long as the UAV cannot accomplish the desired task due to severe motor failures, a formation reconfiguration is achieved by an optimal assignment scheme so that each UAV can be assigned to an optimal position. If non-severe actuator faults occur, different reconfiguration techniques are tested to evaluate the effects of fault occurrence on the endurance of the UAV. The performance of the proposed strategy is evaluated by simulations on a team of six hexarotor UAVs on Matlab/Simulink.

关键词： Actuators Fault tolerance Fault tolerant systems Autonomous aerial vehicles artificial bee colony algorithm Formation control Trajectory

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Energy Efficiency in Cloud Computing using Hybrid algorithm 5

Energy Efficiency in Cloud Computing using Hybrid Algorithm

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5th International Conference on Inventive Computation Technologies (ICICT)

作者： Alzadjali, Najiba Said Hamed Jabeen, Nusrat T. Vijaykumar, Hannah Maheswari, K. Univ Technol & Appl Sci Al Musannah Muladdah Oman Anna Adarsh Coll Women Dept Comp Sci Chennai Tamil Nadu India

ISBN: (数字)9781665408370

ISBN: (纸本)9781665408387;9781665408370

With the rise of cloud computing, the high-energy consumption of data centers has become a serious concern. The cloud is an energy-intensive technology, with data centers housing hundreds of hosts. Energy usage is quite high 24 hours a day, seven days a week. As a result, there is a need to investigate various ways to minimize cloud energy consumption and to develop an algorithm to reduce cloud energy consumption. This work presents a hybrid method for energy efficiency in cloud computing that uses an artificial bee colony and a gravitational search algorithm. The proposed hybrid algorithm shows better energy efficiency compared to existing methodologies. Our proposed methodology consumes 15% lower energy than ABC algorithm and 27% lower than Gravitational search method in cloud computing. On other hand, number of active host and has been reduced w.r.t no. of Virtual Machine.

关键词： Cloud computing Energy consumption Data centers Search methods Quality of service artificial bee colony algorithm Energy efficiency

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Energy Efficiency in Rail Systems with Coasting Control Method Using GA and ABC Optimizations

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2021年第4期28卷 1127-1135页

作者： Arikan, Yagmur Sen, Tolga Cam, Ertugrul Sivas Cumhuriyet Univ Dept Informat Syst & Technol Sivas Turkey Kirikkale Univ Dept Elect & Elect Engn Kirikkale Turkey Samsun Univ Dept Elect & Elect Engn Samsun Turkey

Today, reducing the energy consumption of rail systems is one of the issues that attract researchers' attention. There are many methods to reduce energy consumption and coasting control method has been used in this study. The driving modelling of the vehicle has been carried out by considering all parameters. A new objective function has been determined and for optimization, genetic algorithm (GA) and artificial bee colony (ABC) algorithm have been preferred. The study has been tested with the data of Ankaray metro line. When the proposed optimized driving has been compared with practical driving of the vehicle, the energy savings rate is 13.79% in GA and 13.45% in ABC for a driving. Despite these significant savings ratios, the increase in travel time has been calculated at 1.7% in GA and 1.55% in ABC. When the obtained savings rates are considered annually, this study may greatly contribute to sustainable life.

关键词： artificial bee colony algorithm coasting control dynamic model energy consumption genetic algorithm rail systems

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Hybrid models for intraday stock price forecasting based on artificial neural networks and metaheuristic algorithms *

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PATTERN RECOGNITION LETTERS 2021年 147卷 124-133页

作者： Chandar, Kumar S. CHRIST Deemed Univ Sch Business & Management Bangalore Karnataka India

Stock market prediction is one of the critical issues in fiscal market. It is important issue for the traders and investors. artificial Neural Networks (ANNs) associated with nature inspired algorithms are playing an increasingly vital role in many areas including medical field, security systems and stock market. Several prediction models have been developed by researchers to forecast stock market trend. However, few studies have focused on improving stock market prediction accuracy especially when utilizing artificial neural networks to perform the analysis. This paper proposed nine new integrated models for forecasting intraday stock price based on the potential of three ANNs, Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), Time Delay Neural Network (TDNN) and nature inspired algorithms such as Genetic algorithm (GA), Particle Swarm Optimization (PSO) and artificial bee colony (ABC).The developed models were named as GA-BPNN, PSO-BPNN, ABC-BPNN, GA-RBFNN, PSORBFNN, ABC-RBFNN, GA-TDNN, PSO-TDNN and ABC-TDNN. Nature inspired algorithms are employed for optimizing the parameters of ANNs. Technical indicators calculated from historical data are fed as input to developed models. Proposed hybrid models validated on four datasets representing different sectors in NSE. Four statistical metrics, Root Mean Square Error (RMSE), Hit Rate (HR), Error Rate (ER) and prediction accuracy were utilized to gauge the performance of the developed models. Results proved that the PSO-BPNN model yielded the highest prediction accuracy in estimating intraday stock price. The other models, GA-BPNN, ABC-BPNN, GA-RBFNN, PSO-RBFNN, ABC-RBFNN, GA-TDNN, PSO-TDNN and ABC-TDNN produced lower performance with mean prediction accuracy of 97.24%, 98.37%, 84.01%, 85.15%, 84.01%, 83.87%, 89.95% and 78.61% respectively. (c) 2021 Elsevier B.V. All rights reserved.

关键词： artificial bee colony algorithm artificial neural network Genetic algorithm Hit rate Intraday stock prediction Nature inspired algorithm Stock market prediction

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An improved artificial bee colony algorithm for the blocking flowshop scheduling problem

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2012年第9-12期60卷 1149-1159页

作者： Han, Yu-Yan Pan, Quan-Ke Li, Jun-Qing Sang, Hong-yan Liaocheng Univ Coll Comp Sci Liaocheng 252059 Peoples R China Liaocheng Univ Sch Math Sci Liaocheng 252059 Peoples R China

This paper presents an improved artificial bee colony (IABC) algorithm for solving the blocking flowshop problem with the objective of minimizing makespan. The proposed IABC algorithm utilizes discrete job permutations to represent solutions and applies insert and swap operators to generate new solutions for the employed and onlooker bees. The differential evolution algorithm is employed to obtain solutions for the scout bees. An initialization scheme based on the problem-specific heuristics is presented to generate an initial population with a certain level of quality and diversity. A local search based on the insert neighborhood is embedded to improve the algorithm's local exploitation ability. The IABC is compared with the existing hybrid discrete differential evolution and discrete artificial bee colony algorithms based on the well-known flowshop benchmark of Taillard. The computational results and comparison demonstrate the superiority of the proposed IABC algorithm for the blocking flowshop scheduling problems with makespan criterion.

关键词： Blocking flowshop Makespan artificial bee colony algorithm Differential evolution

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A novel artificial bee colony algorithm with space contraction for unknown parameters identification and time-delays of chaotic systems

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APPLIED MATHEMATICS AND COMPUTATION 2012年第2期219卷 552-568页

作者： Gao, Fei Fei, Feng-xia Xu, Qian Deng, Yan-fang Qi, Yi-bo Balasingham, Ilangko Wuhan Univ Technol Sch Sci Dept Math Wuhan 430070 Hunan Peoples R China Norwegian Univ Sci & Technol Signal Proc Grp Dept Elect & Telecommun N-7491 Trondheim Norway

This paper is concerned with the uncertain parameters and time-delay of nonlinear chaotic systems, which is of vital significance in chaos control and synchronization. In this article, a novel artificial bee colony algorithm (ABC), with space contraction and optimization technique based on the foraging behavior of honeybees, is newly proposed to solve the estimation problem via a nonnegative multi-modal nonlinear optimization, which finds a best combination of parameters such that an objective function is minimized. The illustrative examples, in Lorenz, Chen, Lu chaos systems and Mackey-Glass time-delay chaos system, are given though ABC and ABC with space contraction self-adaptively (ABCSC) respectively. Simulation are done and comparisons to some existing results by a recent version of ABC, HTCMIABC demonstrate that ABCSC is superior to ABC and HTCMIABC for unknown parameters and time-delays of the chaotic systems accurately and effectively. And it is a promising tool for chaotic system identification as well as other numerical optimization problems in mathematics. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Unknown parameters identification Time-delays Chaos systems artificial bee colony algorithm Space contraction self-adaptively

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