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BMDA: applying biogeography-based optimization algorithm and Mexican hat wavelet to improve dragonfly algorithm

SOFT COMPUTING 2020年第21期24卷 15979-16004页

作者： Shirani, Mohammad Reza Safi-Esfahani, Faramarz Islamic Azad Univ Fac Comp Engn Najafabad Branch Najafabad Iran Islamic Azad Univ Big Data Res Ctr Najafabad Branch Najafabad Iran

One of the methods for solving optimization problems is applying metaheuristic algorithms that find near to optimal solutions. Dragonfly algorithm is one of the metaheuristic algorithms which search problem space by the inspiration of hunting and emigration behavior of dragonflies in nature. However, it suffers from the premature convergence of the population to an undesirable point in the detection ability (global search). In this research, an improved dragonfly algorithm called BMDA (applying biogeography-based algorithm, Mexican hat wavelet, and Dragonfly algorithm) is presented to resolve the premature convergence in high workloads by creating a mutation phase based on the combination of the biogeography-based optimization (BBO) migration process and the Mexican hat wavelet transform in dragonfly algorithm (DA). The algorithm was evaluated for the mean error in comparison with standard dragonfly algorithm (DA), Memory-based Hybrid Dragonfly algorithm (MHDA), chaotic dragonfly algorithm version 9 (CDA9), Adaptive_DA algorithm, bat algorithm (BAT), particle swarm optimization algorithm (PSO), raven roosting optimization (RRO) and whale optimization algorithm (WOA) using the CEC2017 benchmark functions. The implementation results of the proposed BMDA algorithm applying different benchmark functions outweighed the DA-based algorithm, MHDA algorithm, CDA9 algorithm, Adaptive_DA algorithm, BAT algorithm, PSO algorithm, RRO, and WOA algorithms in terms of mean error.

关键词： Dragonfly algorithm biogeography-based optimization algorithm Mexican hat wavelet

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Dynamic scheduling of tasks in cloud computing applying dragonfly algorithm, biogeography-based optimization algorithm and Mexican hat wavelet

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JOURNAL OF SUPERCOMPUTING 2021年第2期77卷 1214-1272页

作者： Shirani, Mohammad Reza Safi-Esfahani, Faramarz Islamic Azad Univ Fac Comp Engn Najafabad Branch Najafabad Iran Islamic Azad Univ Big Data Res Ctr Najafabad Branch Najafabad Iran

Cloud computing maps tasks to resources in a scalable fashion. The scheduling is an NP-hard problem;thus, the scheduler chooses one solution from among many. This is the reason why finding the best optimal solution, especially at a high scale of the system, is not possible. Applying metaheuristic algorithms to find a near-to-optimal solution, not the best one, could be the right approach. Dragonfly metaheuristic algorithm explores and exploits a solution space by the inspiration of hunting and emigration behavior of dragonflies in nature. But it suffers from the premature convergence of the algorithm to an undesirable when explores the solution space. In this research, an improved dragonfly algorithm (applying biogeography-based algorithm, Mexican hat wavelet and dragonfly algorithm-BMDA) is presented to resolve the premature convergence by applying a mutation phase that is the combination of the biogeography-based optimization (BBO) migration process and Mexican hat wavelet transform in dragonfly algorithm. Then, it is applied for dynamically scheduling tasks under the BMDDSF framework (BBO-Mexican hat wavelet-dragonfly dynamic scheduling framework) in the cloud computing environment. The purpose is customizing a metaheuristic algorithm to be applied in the resource manager of cloud computing to improve its performance. The BMDA algorithm was firstly evaluated for the mean error in comparison with the baseline algorithms using the CEC2017 benchmark functions. Then, the performance of the BMDDSF framework in cloud computing was tested using NASA parallel workload compared with baseline methods in terms of execution time, response time and reduction of service-level agreement violation. The experiments showed that the presented method outweighed the baseline approaches.

关键词： Task scheduling Cloud computing Dragonfly algorithm biogeography-based optimization algorithm Mexican hat wavelet

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RBF Neural Network Soft-Sensor Modeling of Rotary Kiln Pellet Quality Indices Optimized by biogeography-based optimization algorithm

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JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 2015年第1期48卷 7-15页

作者： Wang, Jie-sheng Shen, Na-na Ren, Xiu-dong Liu, Guan-nan Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114044 Liaoning Peoples R China Univ Sci & Technol Liaoning Natl Financial Secur & Syst Equipment Engn Res Ct Anshan 114044 Liaoning Peoples R China Liaoning Anshan Power Supply Co Anshan 114044 Liaoning Peoples R China

A soft-sensor model is proposed with the aim of predicting the finished pellet quality indicators (chemical composition, physical properties, and metallurgical properties) of the rotary kiln pellet sintering process. The model is based on the radial basis function (RBF) neural network that is optimized by the biogeography-based optimization (BBO) algorithm. Six variables that are associated with the reaction mechanism of the rotary kiln pellet sintering process and that are closely related to the quality indices of the finished pellets-the material thickness of the chain grate, the velocity of chain grate, the temperature of the kiln head, the temperature of the kiln tail, the rotary kiln speed, and the quantity of the fed coal-are selected as inputs to the proposed soft-sensor model, and the finished pellet quality indices form the outputs. Accordingly, a multiple-input-single-output (MISO) RBF neural network (RBFNN) soft-sensor model is established. The structural parameters of the RBFNN model are optimized by the BBO algorithm. The simulation results showed that the model yields better generalization results and has a higher prediction accuracy, and therefore, it is capable of meeting the requirements of real-time control as well as those of an online soft-sensor in the rotary kiln sintering process.

关键词： Rotary Kiln Pellet Sintering Soft-Sensor RBF Neural Network biogeography-based optimization algorithm

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Parameters Identification of Fluxgate Magnetic Core Adopting the biogeography-based optimization algorithm

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SENSORS 2016年第7期16卷 1-13页

作者： Jiang, Wenjuan Shi, Yunbo Zhao, Wenjie Wang, Xiangxin Harbin Univ Sci & Technol Higher Educ Key Lab Measuring & Control Technol & Sch Measurement Control Technol & Commun Engn Harbin 150080 Peoples R China Northeast Dianli Univ Sch Automat Engn Chuanying 132012 Jilin Peoples R China

The main part of the magnetic fluxgate sensor is the magnetic core, the hysteresis characteristic of which affects the performance of the sensor. When the fluxgate sensors are modelled for design purposes, an accurate model of hysteresis characteristic of the cores is necessary to achieve good agreement between modelled and experimental data. The Jiles-Atherton model is simple and can reflect the hysteresis properties of the magnetic material precisely, which makes it widely used in hysteresis modelling and simulation of ferromagnetic materials. However, in practice, it is difficult to determine the parameters accurately owing to the sensitivity of the parameters. In this paper, the biogeography-based optimization (BBO) algorithm is applied to identify the Jiles-Atherton model parameters. To enhance the performances of the BBO algorithm such as global search capability, search accuracy and convergence rate, an improved biogeography-based optimization (IBBO) algorithm is put forward by using Arnold map and mutation strategy of Differential Evolution (DE) algorithm. Simulation results show that IBBO algorithm is superior to Genetic algorithm (GA), Particle Swarm optimization (PSO) algorithm, Differential Evolution algorithm and BBO algorithm in identification accuracy and convergence rate. The IBBO algorithm is applied to identify Jiles-Atherton model parameters of selected permalloy. The simulation hysteresis loop is in high agreement with experimental data. Using permalloy as core of fluxgate probe, the simulation output is consistent with experimental output. The IBBO algorithm can identify the parameters of Jiles-Atherton model accurately, which provides a basis for the precise analysis and design of instruments and equipment with magnetic core.

关键词： Jiles-Atherton model biogeography-based optimization algorithm Arnold map differential evolution fluxgate

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IMPROVED biogeography-based optimization algorithm based ON HYBRID HIGH-ORDER MOBILITY MODEL

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2016年第6期12卷 1961-1976页

作者： Wang, Jiesheng Song, Jiangdi Univ Sci & Technol Liaoning Natl Financial Secur & Syst Equipment Engn Res Ct 185 Qianshan Rd Anshan 114051 Peoples R China Univ Sci & Technol Liaoning Sch Elect & Informat Engn 185 Qianshan Rd Anshan 114051 Peoples R China

biogeography-based optimization algorithm (BBO) realizes the information circulation and sharing by using the species migration among habitats and achieves the global optimization by improving habitat adaptability. based on the information sharing strategy and the population adaptive migration mechanism of BBO algorithm, six new high-order nonlinear hybrid mobility models are proposed based on the cosine-four order mobility model and cosine-sixteen order mobility model. Simulation experiments are carried out to compare the optimization performances of the proposed hybrid high-order mobility models on the typical function optimization problems. The simulation results and analysis show that the proposed improved BBO algorithm has good optimization performance.

关键词： biogeography-based optimization algorithm Mobility model Function optimization

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Local search strategy biogeography-based optimization algorithm for self-tuning of PID parameters

Local search strategy biogeography-based optimization algori...

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第三十二届中国控制会议

作者： WANG Fuli LI Ping CAO Jiangtao LI Chengxiang School of Information and Control Engineering Liaoning Shihua University

ISBN: (纸本)9781479900305

As a new kind of evolutionary algorithm,the biogeography-based optimization algorithm has a good utilizing ability. However,its exploration ability needs to be *** paper presents an improved biogeography-based optimization algorithm for self-tuning of *** proposed method integrates local search strategy and selection operation of differencial evolution(DE) with migration operator in original BBO to improve the efficiency of migration and overcome the premature convergence in BBO *** results on two different cases show that the proposed method can find the optimum parameter of PID controller more quickly than the other compared approaches.

关键词： self-tuning of PID controller biogeography-based optimization algorithm local search strategy

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Set Covering Problem Resolution by biogeography-based optimization algorithm 16th

Set Covering Problem Resolution by Biogeography-Based Optimi...

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16th International Conference on Computational Science and Its Applications (ICCSA)

作者： Crawford, Broderick Soto, Ricardo Riquelme, Luis Olguin, Eduardo Misra, Sanjay Pontificia Univ Catolica Valparaiso Valparaiso Chile Univ Cent Chile Santiago Chile Univ San Sebastian Fac Ingn & Tecnol Santiago Chile Univ Autonoma Chile Santiago Metropolitan Chile Univ Cient Sur Lima Peru Covenant Univ Ota Nigeria

ISBN: (纸本)9783319420851

The research on Artificial Intelligence and Operational Research has provided models and techniques to solve many industrial problems. For instance, many real life problems can be formulated as a Set Covering Problem (SCP). The SCP is a classic NP-hard combinatorial problem consisting in find a set of solutions that cover a range of needs at the lowest possible cost following certain constraints. In this work, we use a recent metaheuristic called biogeography-based optimization algorithm (BBOA) inspired by biogeography, which mimics the migration behavior of animals in nature to solve optimization and engineering problems. In this paper, BBOA for the SCP is proposed. In addition, to improve performance we provide a new feature for the BBOA, which improve stagnation in local optimum. Finally, the experiment results show that BBOA is a excellent method for solving such problems.

关键词： biogeography-based optimization algorithm Set Covering Problem Metaheuristics

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A biogeography-based optimization algorithm with Heuristics for Distributed Multiproduct Scheduling Problem 8

A Biogeography-based Optimization Algorithm with Heuristics ...

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8th International Conference on Robotics, Control and Automation (ICRCA)

作者： Zhang, Yaya Gu, Xingsheng East China Univ Sci & Technol Minist Educ Key Lab Smart Mfg Energy Chem Proc Shanghai Peoples R China

ISBN: (纸本)9798350344738;9798350344721

Multiproduct batch plant scheduling problems arise in various industries, such as fine chemical, pharmaceutical, rubber, food, and daily cosmetics industries. To remain competitive in such a global economy, the modern production mode has shifted from a single factory to a multi-factory production network. Thus, distributed batch scheduling has attracted wide attention. To address the distributed batch scheduling with multiproduct and multi-factory production networks, we consider minimizing the global makespan over all factories and propose a biogeography-based optimization algorithm combined with heuristics to find the optimal production plan and enhance productivity. First, the time key encoding scheme and the First Come First Served rule have been adopted to improve the search efficiency. Then, the power of this heuristic rule has been enhanced by changing the production sequence based on the sequence of other stages instead of only the first stage. Besides, a high-efficient heuristic has been proposed to balance the load of different factories. The proposed algorithm is compared with three algorithms, and simulation results show the effectiveness of the proposed algorithm on distributed multiproduct batch plant scheduling problems.

关键词： biogeography-based optimization algorithm batch plant distributed scheduling heuristics

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Merged biogeography-based optimization algorithm for Color Image Segmentation 17

Merged Biogeography-Based Optimization Algorithm for Color I...

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17th IEEE International Wireless Communications and Mobile Computing Conference (IEEE IWCMC)

作者： Zhang, Lingzhi Xie, Xiaohan Univ Manchester Sch Environm Educ & Dev Manchester Lancs England Shandong Prov Tengzhou 1 High Sch Tengzhou Peoples R China

ISBN: (纸本)9781728186160

Image segmentation is an important step in image processing. Segmentation based on threshold is a common method. Searching the suitable threshold vector is essentially an optimization problem, especially for color image which have higher dimensions and complexity. This paper proposes a merged biogeography-based optimization algorithm (MBBO) for color image segmentation based on threshold. We merge a mutation operation into the migration operator of BBO to enhance the global search ability. Then we merge a chemotaxis operation into the mutation operator of BBO to enhance the local search ability. A greedy selection method is also used to further improve the performance and reduce computation complexity. Experimental results show that MBBO obtains better optimization performance, stronger stability and faster running speed compared with other existing algorithms.

关键词： optimization algorithm biogeography-based optimization algorithm Image Segmentation Kapur Entropy

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Power grid partition with improved biogeography-based optimization algorithm

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2021年 46卷 101267-101267页

作者： Liu, Fangyu Gu, Bruce Qin, Shuwen Zhang, Kaiyan Cui, Lei Xie, Gang Taiyuan Univ Technol Coll Phys & Optoelect Taiyuan 030024 Peoples R China Taiyuan Univ Sci & Technol Shanxi Key Lab Adv Control & Equipment Intelligen Taiyuan 030024 Peoples R China Victoria Univ Ballarat Rd Coll Engn & Sci Informat Technol Discipline Footscray Vic 8001 Australia Inner Mongolia & Forest Power Co Ltd Hohhot 011508 Peoples R China Deakin Univ Sch Informat Technol Geelong Vic 3128 Australia

With the continuous expansion of the scale of the interconnected power grid and the large amount of new energy access, the power grid suffers from a wider range of faults. Unlocking the electromagnetic ring network and performing layered and partitioned operation of the power grid is the future development trend of the power grid. Grid partition operation can effectively enhance the controllability within each sub-partition of the power grid, the decoupling between the partitions and the robustness of cascade failure. Depending on the power grid topology and electrical characteristics, this paper proposes a new power grid partition algorithm based on the improved biogeography-based optimization (BBO) algorithm, which improves the algorithm's calculation speed and partition quality. The algorithm, first, analyzes the physical and operational characteristics of the power network, and determines the zoning principle based on the actual situation;next combined with the definition of relevant parameters of the complex network theory, constructs a topology model that conforms to the characteristics of the power grid;at the same time, considering the small-world characteristics of the power grid topology, the original biogeography is improved the update method of algorithm node information exchange reduces the time complexity of the algorithm and makes it more suitable for modern power grids. Then, according to the value of the improved modularity function and the grid zoning principle, the optimal zoning plan is output. Finally, the IEEE-39 node system and the IEEE-118 node system are designated as calculation examples. The extensive calculation example results demonstrate that the method of community network analysis considering the physical characteristics of the power grid is reasonable to a certain extent. The calculation method is simple and fast, which meets the needs of complex power grid analysis and engineering calculation.

关键词： biogeography-based optimization algorithm Small-world network Power network partition Complex network Community detection

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