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Multi-objective cuckoo optimizer for task scheduling to balance workload in cloud computing

COMPUTING 2024年第11期106卷 3447-3478页

作者： Mondal, Brototi Choudhury, Avishek Univ Calcutta Dept Comp Sci & Engn EM.Bypass Kolkata 700094 W Bengal India New Alipore Coll Dept Comp Sci L Block Kolkata 700053 W Bengal India

A cloud load balancer should be proficient to modify it's approach to handle the various task kinds and the dynamic environment. In order to prevent situations where computing resources are excess or underutilized, an efficient task scheduling system is always necessary for optimum or efficient utilization of resources in cloud computing. Task Scheduling can be thought of as an optimization problem. As task scheduling in the cloud is an NP-Complete problem, the best solution cannot be found using gradient-based methods that look for optimal solutions to NP-Complete problems in a reasonable amount of time. Therefore, the task scheduling problem should be solved using evolutionary and meta-heuristic techniques. This study proposes a novel approach to task scheduling using the cuckoo optimization algorithm. With this approach, the load is effectively distributed among the virtual machines that are available, all the while keeping the total response time and average task processing time(PT) low. The comparative simulation results show that the proposed strategy performs better than state-of-the-art techniques such as Particle Swarm optimization, Ant Colony optimization, Genetic algorithm and Stochastic Hill Climbing.

关键词： Cloud computing Task scheduling cuckoo optimization algorithm Response time Processing time 68 49

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optimization of peroxone reaction rate using metaheuristic approach in the dearomatization and discoloration process

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ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY 2018年第2期37卷 695-702页

作者： Kordkandi, Salman Alizadeh Motlagh, Amir Mohaghegh Sahand Univ Technol Dept Chem Engn Tabriz 513351996 Iran Sahand Univ Technol Dept Chem Engn EERC Tabriz 513351996 Iran Univ Cent Florida Dept Civil Environm & Construct Engn Orlando FL 32816 USA Calif State Univ Sacramento Dept Civil Engn Sacramento CA 95819 USA

The main objective of this research focused on the optimization of apparent reaction rate constants (k(app)) for degradation of a diazo compound, reactive yellow 42 (RY42) using peroxone reaction. Artificial Neural Network (ANN) was adopted to evaluate the effects of initial RY42 concentration, influent ozone mass flow rate, initial ozone (O-3)/peroxide hydrogen ratio (H2O2), and pH on the discoloration rate constant. A proposed model by ANN was chosen after analysis of the topology based on R-square (R-2) and mean squared error (MSE) criteria followed by coupling the obtained best net with a novel evolutionary algorithm named cuckoo optimization algorithm (COA). Although the relative importance of pH and O-3/H2O2 on k(app) was at the same level (about 18%), initial dye concentration (34%), and influent ozone mass flow rate (30%) were the most important factors. A systematic ANN-COA approach indicated that the optimum values of pH and O-3/H2O2 for 150 mgL(-1) of RY42 were 7.5 and 1.44 Lh(-1), respectively, resulted in relatively different k(app) for decolorization (0.18 min(-1)) and dearomatization (0.056 min(-1)). To confirm the validity of kinetic analysis, the predicted values were correlated with experimental values, which indicated the high degree of accuracy (R-2= 0.98) for optimization of the peroxone process. This research with the experimental and modeling studies can be used as a guide for the design of full-scale treatment processes for various types of industrial wastewater containing aromatic compounds with diazo groups. (c) 2017 American Institute of Chemical Engineers Environ Prog, 37: 695-702, 2018

关键词： peroxone processes diazo dye kinetic analysis artificial neural network cuckoo optimization algorithm

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SPWM Sampling Method Based on Area Difference optimization

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2023年第2期18卷 909-924页

作者： You, Hong Hu, Haize Fang, Mengge Hunan Inst Engn Sch Elect & Informat Engn Xiangtan Peoples R China Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan Peoples R China State Grid Yiyang Power Supply Co Yiyang Peoples R China

To solve the area solving error problem of the rule sampling method in the SPWM pulse calculation process, the paper propose an optimized rule sampling method to correct the area difference. The method mainly use infinitesimal components to continuously correct the area difference, and then reduce the error components. To find the best infinitesimal component value, the cuckoo optimization algorithm was used to iteratively calculate the infinitesimal component. To study the effectiveness of the proposed sampling method, MATLAB software was used to construct a sampling model with the natural sampling method as the benchmark, the relative error and the average error as the evaluation index, and the symmetric regular sampling method, the asymmetric sampling method and the tangent sampling method as the comparison method. The experimental results showed that compared with the three comparison methods, the proposed method reduce the absolute value of relative error at the turn-on time by 39%, 0.4% and 26.5%, and the absolute value of relative error at the turn-off time by 63.7%, 0.1% and 20.2%, which is closer to the natural sampling method. The proposed method can effectively reduce the sampling error in the pulse width modulation process and improve the inversion accuracy.

关键词： SPWM Regular sampling Area difference Infinitesimal component cuckoo optimization algorithm

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Prediction and minimization of blast-induced ground vibration using two robust meta-heuristic algorithms

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ENGINEERING WITH COMPUTERS 2017年第4期33卷 835-851页

作者： Faradonbeh, Roohollah Shirani Monjezi, Masoud Tarbiat Modares Univ Fac Engn Tehran Iran

During blasting operations in open pit mines, most of the blast energy (approximately 40%) is wasted due to ground vibration. This phenomenon causes damages to the surrounding structures and pit slope. Therefore, prediction of ground vibration with an appropriate degree of accuracy is important to identify safety area of blasting. In this paper, in the first step, a predictive equation based on gene expression programming (GEP) was developed to estimate ground vibrations of blasting operations conducted in Gol-E-Gohar iron mine. For this aim, 115 blasting operations were identified and the most effective parameters on peak particle velocity (PPV), i.e., burden, spacing, stemming, hole-depth, hole-diameter, powder factor, maximum charge per delay and distance from the blast face were collected from the mine. Capability of the developed GEP model was compared with a non-linear multiple regression (NLMR) model and five conventional equations. The obtained results revealed that the developed GEP model is more efficient compared to the other models in predicting PPV. In the second step, to optimize the GEP predictive model, cuckoo optimization algorithm (COA) was employed and proposed. To do this, several strategies were defined and then several optimized blasting patterns were determined for each strategy. It was found that by developing the COA model, a significant reduction can be happened in PPV values.

关键词： Blasting Ground vibration Gene expression programming Non-linear multiple regression Conventional equations cuckoo optimization algorithm

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optimization-based method for structural damage localization and quantification by means of static displacements computed by flexibility matrix

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ENGINEERING optimization 2016年第4期48卷 543-561页

作者： Hosseinzadeh, Ali Zare Amiri, Gholamreza Ghodrati Koo, Ki-Young Iran Univ Sci & Technol Sch Civil Engn Ctr Excellence Fundamental Studies Struct Engn Tehran Iran Univ Exeter Coll Engn Math & Phys Sci Vibrat Engn Sect Exeter Devon England

This article presents an effective method for structural damage identification. The damage diagnosis problem is introduced as an optimization problem which is based on computing static displacements by the flexibility matrix. By utilizing this matrix, the complexity of the static displacement measurements in real cases can be overcome. The optimization problem is solved by a fast evolutionary optimization strategy, named the cuckoo optimization algorithm. The performance of the presented method was demonstrated by studying the benchmark problem provided by the IASC-ASCE Task Group on Structural Health Monitoring, and a numerical example of a frame. Moreover, the robustness of the presented approach was investigated in the presence of some prevalent modelling errors, and also when noisy and incomplete modal data are available. Finally, the efficiency of the proposed method was verified by an experimental study of a five-storey shear building structure. All the obtained results show the good performance of the presented method.

关键词： damage detection modal data flexibility matrix static displacement cuckoo optimization algorithm

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Optimizing laser triangulation displacement sensor of 3D positioning and posture using COA Based BPNN

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SENSOR REVIEW 2020年第1期40卷 112-120页

作者： Selami, Yassine Lv, Na Tao, Wei Yang, Hongwei Zhao, Hui Shanghai Jiao Tong Univ Dept Instrument Sci & Technol Minhang Campus Shanghai Peoples R China

Purpose The purpose of this paper is to propose cuckoo optimization algorithm (COA)-based back propagation neural network (BPNN) to reduce the effect of the nonlinearities presented in laser triangulation displacement sensors. The 3D positioning and posture sensor allows access to the third dimension through depth measurement;the performance of the sensor varies according to the level of nonlinearities presented in the system, which leads to inaccuracies in measurement. Design/methodology/approach While applying optimization approach, the mathematical model and the relationship between the key parameters in the laser triangulation ranging and the indexes of the measuring system were analyzed. Findings Based on the performance of the parametric optimization method, the measurement repeatability reached 0.5 mu m with an STD value within 0.17 mu m, an expanded uncertainty of measurement was within 5 mu m, the angle error variation of the object's rotational plane was within 0.031 degrees and nonlinearity was recorded within 0.006 per cent in a full scale. The proposed approach reduced the effect of the nonlinearity presented in the sensor. Thus, the accuracy and speed of the sensor were greatly increased. The specifications of the optimized sensor meet the requirements for high-accuracy devices and allow wide range of industrial application. Originality/value In this paper, COA-based BPNN is proposed for laser triangulation displacement sensor optimization, on the basis of the mathematical model, clarifying the working space and working angle on the measurement system.

关键词： Neural network Back propagation cuckoo optimization algorithm Laser triangulation displacement sensor

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A Novel Smart Energy Management System in Pure Electric Motorcycle Using COA

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2019年第4期4卷 600-608页

作者： Farzaneh, Alireaz Farjah, Ebrahim Shiraz Univ Sch Elect & Comp Engn Zand Ave Shiraz *** Iran

Todays, Greenhouse Gases (GHG) emissions, densely populated urban areas air quality problems, and depleting fossil fuel concerns motivated motorcycle producers in crowded countries to explore electric propulsion systems. Due to energy storage system constraints, Pure Electric Motorcycles (PEM) range is restricted, so an effective EnergyManagement System is required to optimizePEMconsumed energy. As road curvature is an inevitable part of real roads, this article, proposes a novel Smart Energy Management System (SEMS) based on road geometry which first analyses road data to identify curvature and straight parts. Then, SEMS solves optimization problems separately and optimal speed is calculated in direct and curvature parts independently. The proposed SEMS can communicate to the driver as an eco-driving system by receiving trip time importance and presenting each road point optimal speed. Because of fast response necessity, cuckoo optimization algorithm (COA) is used in this article to solve SEMS optimization problem. For evaluation purposes, three scenarios are defined: The test road is travelled at motorcycle maximum allowable speed in the first scenario. In the 2nd one, PEM speed is also constant but it equals to direct road optimal speed instead of max. speed. Independent optimal speed in direct and curvature parts is considered in 3rd scenario. Simulation results show the significant effect of road curves on consumed energy and SEMS good performance in optimization of PEM consumed energy.

关键词： Energy Management System Electric Motorcycle Energy Consumption optimization Road Curve Range cuckoo optimization algorithm

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Cancer classification using a novel gene selection approach by means of shuffling based on data clustering with optimization

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APPLIED SOFT COMPUTING 2015年 35卷 43-51页

作者： Elyasigomari, V. Mirjafari, M. S. Screen, H. R. C. Shaheed, M. H. Univ London Sch Engn & Mat Sci London E1 4NS England

This research presents an innovative method for cancer identification and type classification using microarray data. The method is based on gene selection with shuffling in association with optimization based unconventional data clustering. A new hybrid optimization algorithm, COA-GA, is developed by synergizing recently invented cuckoo optimization algorithm (COA) with a more traditional genetic algorithm (GA) for data clustering to select the most dominant genes using shuffling. For gene classification, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) artificial neural networks are used. Literature suggests that data clustering using traditional approaches such as K-means, C-means and Hierarchical do not have any impact on classification accuracy. This is also confirmed in this investigation. However, results show that optimization based clustering with shuffling increase the classification accuracy significantly. The proposed algorithm (COA-GA) not only outperforms COA, GA and Particle Swarm optimization (PSO) in achieving better classification performance but also reaches a better global minimum with only few iterations. Higher accuracy is observed to have achieved with SVM classifier compared to MLP in all datasets used. Crown Copyright (C) 2015 Published by Elsevier B.V. All rights reserved.

关键词： Cancer classification Gene selection Clustering Evolutionary algorithms cuckoo optimization algorithm COA-GA

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Using artificial neural network predictive controller optimized with cuckoo algorithm for pressure tracking in gas distribution network

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JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING 2015年第Part3期27卷 1446-1454页

作者： Hadian, Mohsen AliAkbari, Naser Karami, Mahboobeh Petr Univ Technol Dept Instrumentat & Ind Automat Ahvaz Iran Petr Univ Technol Dept Gas Transportat & Proc Engn Ahvaz Iran

In order to model and analyze gas networks, several methods have already been developed and presented. Nevertheless, all these methods have their own specific applications and most of them are very complex and usually contain some errors. In this paper, in an attempt to resolve these problems, an Artificial Neural Network (ANN) has been used to model a gas distribution network. The algorithms utilized for ANN training, such as the gradient descent algorithm, are usually subjected to local minima;in this regard, the new cuckoo optimization algorithm (COA) is used in training the weights of the neural network. However, gas networks are often very large and operate a multitude of distant points, which explains why time delays in these networks are inevitable. Accordingly, in order for all points of the output (pressure) to achieve the desired value, a Model Predictive Controller was used. According to the results achieved, it can be said that the Artificial Neural Network cuckoo optimization algorithm (ANN_COA), in comparison to regular ANN, yields a more suitable performance and is less prone to error. In addition, the MPC controller is faster and suffers from fewer errors compared to the Proportional-Integral-Derivative (PID) controller while also preventing fluctuations in gas system input. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Gas distribution network Model predictive controller Artificial neural network cuckoo optimization algorithm Proportional-integral-derivative controller

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Event-based neural network predictive controller application for a distillation column

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ASIAN JOURNAL OF CONTROL 2021年第2期23卷 811-823页

作者： Hadian, Mohsen Mehrshadian, Milad Karami, Mahboobeh Biglary Makvand, Amin Petr Univ Technol Dept Instrumentat & Automat Ahwaz Iran Univ Calgary Dept Elect & Comp Engn Calgary AB Canada

In this work, the case study is a distillation column, which is a multi-input multi-output (MIMO) nonlinear process. An event-based neural network predictive controller is utilized for the case study, considering control and energy policies. Computation and communication reduction are the main purposes of the event-based strategy. The event-based model predictive controller also copes successfully with the multi-input multi-output (MIMO) time-delayed nonlinear processes. In order to achieve a suitable nonlinear data-based model of the process, an event-based neural network predictive controller is proposed. Moreover, new cuckoo optimization algorithm (COA) is employed to improve the efficiency of the neural network. Evaluation of the proposed controller has illustrated a satisfactory performance in both setpoint tracking and disturbance rejection while computational load has been significantly decreased.

关键词： event-based controller model predictive controller neural network cuckoo optimization algorithm distillation column

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