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An optimized multiobjective CPU job scheduling using evolutionary algorithms

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2018年第1期26卷 101-114页

作者： Venkatraman, Santhi Selvagopal, Dharshikha PSG Coll Technol Dept Comp Sci & Engn Coimbatore Tamil Nadu India

Scheduling in a multiprocessor parallel computing environment is an NP-hard optimization problem. The main objective of this work is to obtain a schedule in a distributed computing system (DCS) environment that minimizes the makespan and maximizes the throughput. We study the use of two of the evolutionary swarm optimization techniques, the firefly algorithm and the artificial bee colony (ABC) algorithm, to optimize the scheduling in a DCS. We also enhance the traditional ABC algorithm by merging the genetic algorithm techniques of crossover and mutation with the employed bee phase and the onlooker phase, respectively. The resulting enhanced ABC algorithm is used as the scheduling algorithm and is evaluated against the firefly and ABC algorithms. The results obtained show that in a distributed environment with a large number of jobs and resources, multiobjective scheduling using evolutionary algorithms can perform well in terms of minimizing makespan and maximizing throughput.

关键词： Firefly algorithm makespan throughput artificial bee colony algorithm crossover mutation

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Empirical analysis of five nature-inspired algorithms on real parameter optimization problems

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artificial INTELLIGENCE REVIEW 2018年第3期50卷 383-439页

作者： Agarwal, Parul Mehta, Shikha Jaypee Inst Informat Technol Dept Comp Sci & Informat Technol Sect 62 Noida 201301 India

In the past few years nature-inspired algorithms are seen as potential tools to solve computationally hard problems. Tremendous success of these algorithms in providing near optimal solutions has inspired the researchers to develop new algorithms. However, very limited efforts have been made to identify the best algorithms for diverse classes of problems. This work attempts to assess the efficacy of five contemporary nature-inspired algorithms i.e. bat algorithm (BA), artificial bee colony algorithm (ABC), cuckoo search algorithm (CS), firefly algorithm (FA) and flower pollination algorithm (FPA). The work evaluates the performance of these algorithms on CEC2014 30 benchmark functions which include unimodal, multimodal, hybrid and composite problems over 10, 30, 50 and 100 dimensions. Control parameters of all algorithms are self-adapted so as to obtain best results over benchmark functions. The algorithms have been evaluated along three perspectives (a) statistical significance using Wilcoxon rank sum test (b) computational time complexity (c) convergence rate of algorithms. Experimental results and analysis revealed that ABC algorithm perform best for majority of the problems on high dimension, while on small dimension, CS is the best choice. FPA attain the next best position follow by BA and FA for all kinds of functions. Self adaptation of above algorithms also revealed the best values of input parameters for various algorithms. This study may aid experts and scientists of computational intelligence to solve intricate optimization problems.

关键词： artificial bee colony algorithm Computational time complexity Wilcoxon rank sum test Convergence rate

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Prediction of spontaneous combustion in the coal stockpile based on an improved metabolic grey model

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2018年第Part B期116卷 564-577页

作者： Li, Shoujun Ma, Xiaoping Yang, Chunyu China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221008 Jiangsu Peoples R China Suqian Coll Sch Mech & Elect Engn Suqian 223800 Peoples R China

Spontaneous combustion of coal can lead to serious environmental pollution, safety hazards and huge economic losses. Hazard prevention and environmental protection requires effective monitoring network and efficient prediction methods. To acquire the real-time data of coal parameters we designed multi layered Zig-bee based wireless sensor network. The shape of the coal pile is formed by the industrial laser scanner followed by the formation of 3D temperature point cloud field via Kriging interpolation. Afterwards, an integrated evaluation indicator Heat Loss Capacity (HLC) is proposed, which synthesizes various factors inducing spontaneous combustion. The real-time and high accurate HLC prediction is realized by using metabolic method to update the raw sequence of the GM(1,1) grey prediction model in cycle with the latest acquired sensor data. Finally, a hybrid model ABC-MGM(1,1) combining the ABC optimization algorithm with the metabolic GM(1,1) model is proposed. Experiment shows that the ABC-MGM(1,1) model has better performance in parameter optimization as well as HLC prediction, especially for short-term prediction. The proposed method has been utilized to predict spontaneous combustion of the stockpiled coal in Xutang power plant of China. The timely parameter acquisition by 25 Zig-bee nodes and effective prediction by the proposed model shows great practicability in safety and hazard prevention. (C) 2018 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Prediction of spontaneous combustion artificial bee colony algorithm Metabolic GM(1,1) model Particle swarm optimization Genetic algorithm Environmental protection

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Effective de-noising of ECG by optimised adaptive thresholding on noisy modes

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2018年第5期12卷 640-644页

作者： Jain, Shweta Bajaj, Varun Kumar, Anil PDPM Indian Inst Informat Technol Design & Mfg Elect & Commun Engn Jabalpur India

Electrocardiograph (ECG) denoising is a preliminary step in QRS complex detection and ECG beat classification process. An ECG denoising methodology is proposed that uses ensemble empirical mode decomposition (EEMD) for decomposing input ECG to its intrinsic mode functions (IMFs). Then noisy-IMFs are identified by the proposed method that uses squared distance similarity measure. On selected noisy-IMFs, hard thresholding, by the evolutionary optimisation algorithms (EOAs)-based thresholds, is applied. For selecting the best performing EOA, algorithms namely artificial bee colony, particle swarm optimisation (PSO), and cuckoo search (CS) optimisation are implemented. The proposed methodology is tested with the Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) arrhythmia database and is evaluated in terms of signal-to-noise ratio (SNR) and mean-square error (MSE). In comparison with state-of-the-art methods, the proposed method is found to be the best attaining maximum SNR and MSE for PSO and CS algorithm optimised thresholding.

关键词： electrocardiography Hilbert transforms evolutionary computation artificial bee colony algorithm particle swarm optimisation search problems mean square error methods medical signal processing adaptive noise thresholding electrocardiograph denoising QRS complex detection ECG beat classification process ECG denoising methodology ensemble empirical mode decomposition EEMD intrinsic mode function IMF evolutionary optimisation algorithm EOA artificial bee colony algorithm particle swarm optimisation PSO cuckoo search optimisation CS optimisation MIT-BIH arrhythmia database signal-to-noise ratio SNR mean-square error method MSE method

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artificial bee colony algorithm for Multimachine Power System Stabilizer Design

Artificial Bee Colony Algorithm for Multimachine Power Syste...

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2011 International Conference on Computers, Communications, Control and Automation

作者： H.Alkhatib J.Duveau Laboratoire de Compatibilité Electromagnétique de Marseille Université Paul Cézanne

artificial bee colony(ABC) algorithm is being widely used for global optimization problems. In this paper, an ABC algorithm is presented for optimal design of multimachine Power System Stabilizers(PSSs). This novel optimization method, inspired by the intelligent behavior of honey bee swarms, is characterized as efficient in computation as well as simple in concept. It also uses less control parameters compared to other metaheuristic approaches. The performance of the proposed ABC based-PSS design is validated for a multimachine power system: 16-machine 68-bus system. Eigenvalue analysis and nonlinear system simulations demonstrate that the optimization by ABC algorithm outperforms genetic algorithms in enhancing the system dynamic stability.

关键词： artificial bee colony algorithm genetic algorithms power system dynamic stability power system stabilizer

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Improved artificial bee colony algorithm with Chaos

Improved Artificial Bee Colony Algorithm with Chaos

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International Workshop on Computer Science for Environmental Engineering and EcoInformatics (CSEEE 2011)

作者： Wu, Bin Fan, Shu-hai Nanjing Univ Technol Coll Econ & Mangement Nanjing 210009 Jiangsu Peoples R China

ISBN: (纸本)9783642226939

artificial bee colony (ABC) algorithm is the one of the newest nature inspired heuristics for optimization problem. Like the chaos in real bee colony behavior, this paper proposes a Chaotic ABC (CABC) that use chaotic searching behavior for candidate food position produced in order to improve the convergence characteristics and to prevent the ABC to get stuck on local solutions. To show the performance of our proposed CABC, a number of experiments are carried out on a set of well-known benchmark continuous optimization problems. Simulation results and comparisons with the standard ABC and several meta-heuristics show that the CABC can effectively enhance the searching efficiency and greatly improve the searching quality.

关键词： artificial bee colony algorithm Chaos Global Numerical Optimization

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A Discrete artificial bee colony algorithm For the Economic Lot Scheduling Problem

A Discrete Artificial Bee Colony Algorithm For the Economic ...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Tasgetiren, M. Fatih Bulut, Onder Fadiloglu, M. Murat Yasar Univ Dept Ind Engn Izmir Turkey Izmir Econ Univ Ind Engn Dept Izmir Turkey

ISBN: (纸本)9781424478354

In this study we present a discrete artificial bee colony (DABC) algorithm to solve the economic lot scheduling problem (ELSP) under extended basic period (EBP) approach and power-of-two (PoT) policy. In specific, our algorithm provides a cyclic production schedule of n items to be produced on a single machine such that the production cycle of each item is an integer multiple of a fundamental cycle. All the integer multipliers are in the form of power-of-two, and under EBP approach feasibility is guaranteed with a constraint that checks if the items assigned in each period can be produced within the length of the period. For this problem, which is NP-hard, our DABC algorithm employs a multi-chromosome solution representation to encode power-of-two multipliers and the production positions separately. Both feasible and infeasible solutions are maintained in the population through the use of some sophisticated constraint handling methods. A variable neighborhood search (VNS) algorithm is also fused into DABC algorithm to further enhance the solution quality. The experimental results show that the proposed algorithm is very competitive to the best performing algorithms from the existing literature under the EBP and PoT policy.

关键词： economic lot scheduling problem extended basic period power-of-two policy artificial bee colony algorithm

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Evaluating the capability of distribution networks to maintain power quality and voltage level, case study: Alborz Electric Power Distribution Company

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第16期12卷 3883-3891页

作者： Jamshidieini, Bahman Rezaie, Kamran Firuzabad, Mahmud Fotuhi Univ Tehran Fac Engn Tehran Iran Sharif Univ Technol Fac Elect Engn Tehran Iran

Distribution system operators that seek operational excellence are supposed to design and implement capable processes throughout their value stream and assess how well their real services match the design specifications. Process capability analysis is a critical stage in systematic and sophisticated quality engineering approaches such as six sigma, which are based on quantitative studies. The capability of a distribution network to maintain the power quality and voltage level within an acceptable range is one of the most important aspects of design and utilisation of electricity distribution network. Capability analysis has been successfully used in the previous studies to assess the quality of conformance in the power distribution network. However, these studies were limited to a voltage level and other aspects of quality such as total harmonic distortion were neglected. In addition, parametric methods were not available to overcome the problem of multimodality in voltage distribution, especially in the presence of distributed generation. In the current study, for the first time, a parametric approach based on decomposing complex distributions to a few Gaussian distributions by artificial bee colony optimisation were used for evaluating the capability of the distribution network to maintain voltage level and power quality indices in predefined ranges.

关键词： distribution networks power supply quality artificial bee colony algorithm six sigma (quality) Gaussian distribution electricity distribution network utilisation power quality indices voltage level Alborz Electric Power Distribution Company distribution system operators value stream process capability analysis quality engineering approaches six sigma artificial bee colony optimisation Gaussian distributions complex distributions distributed generation

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An artificial bee colony Approach for Classification of Remote Sensing Imagery

An Artificial Bee Colony Approach for Classification of Remo...

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International Conference on Electronics, Computers and artificial Intelligence (ECAI)

作者： Victor-Emil Neagoe Catalina-Elena Neghina Dept. of Appl. Electron. and Information Eng. Polytechnic University of Bucharest Bucharest Romania Dept. of Computer Sci. Electrical and Electron. Eng. “Lucian Blaga” University of Sibiu Sibiu Romania

ISBN: (纸本)9781538649022;9781538649015

This paper presents a novel artificial bee colony (ABC) approach for supervised classification of remote sensing images. One proposes to apply an ABC algorithm to optimize the coefficients of the set of polynomial discriminant functions. We have experimented the proposed ABC-based classifier algorithm for a Landsat 7 ETM+ image database, evaluating the influence of the ABC model parameters on the classifier performances. Such ABC model parameters are: numbers of employed/onlooker/scout bees, number of epochs, and polynomial degree. One has compared the best ABC classifier Overall Accuracy (OA) with the performances obtained using a set of benchmark classifiers (NN, NP, RBF, and SVM). The results have proved the effectiveness of the proposed approach.

关键词： Remote sensing Earth artificial satellites Support vector machines artificial bee colony algorithm artificial neural networks Classification algorithms

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Hybrid artificial bee colony algorithm for Neural Network Training

Hybrid Artificial Bee Colony Algorithm for Neural Network Tr...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Ozturk, Celal Karaboga, Dervis Erciyes Univ Dept Comp Engn Kayseri Turkey

ISBN: (纸本)9781424478354

A hybrid algorithm combining artificial bee colony (ABC) algorithm with Levenberq-Marquardt (LM) algorithm is introduced to train artificial neural networks (ANN). Training an ANN is an optimization task where the goal is to find optimal weight set of the network in training process. Traditional training algorithms might get stuck in local minima and the global search techniques might catch global minima very slow. Therefore, hybrid models combining global search algorithms and conventional techniques are employed to train neural networks. In this work, ABC algorithm is hybridized with the LM algorithm to apply training neural networks.

关键词： Neural network training Levenberq-Marquardt algorithm artificial bee colony algorithm Hybrid algorithms

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