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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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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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Game theory-based multi-task scheduling in cloud manufacturing using an extended biogeography-based optimization algorithm

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CONCURRENT ENGINEERING-RESEARCH AND APPLICATIONS 2019年第4期27卷 314-330页

作者： Xiao, Jiuhong Zhang, Wenyu Zhang, Shuai Zhuang, Xiaoyu Zhejiang Univ Finance & Econ Sch Informat Hangzhou 310018 Zhejiang Peoples R China

Cloud manufacturing is an emerging paradigm of global manufacturing networks. Through centralized management and operation of distributed manufacturing services, it can deal with different requirement tasks submitted by multiple customers in parallel. Therefore, the cloud manufacturing multi-task scheduling problem has attracted increasing attention from researchers. This article proposes a new cloud manufacturing multi-task scheduling model based on game theory from the customer perspective. The optimal result for a cloud manufacturing platform is derived from the Nash equilibrium point in the game. As the cloud manufacturing multi-task scheduling problem is known as an NP-hard combinatorial optimization problem, an extended biogeography-based optimization algorithm that embeds three improvements is presented to solve the corresponding model. Compared with the basic biogeography-based optimization algorithm, genetic algorithm, and particle swarm optimization, the experimental simulation results demonstrate that the extended biogeography-based optimization algorithm finds a better schedule for the proposed model. Its benefit is to provide each customer with reliable services that fulfill the demanded manufacturing tasks at reasonable cost and time.

关键词： cloud manufacturing multi-task scheduling game theory biogeography-based optimization algorithm

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Dynamic Optimal Scheduling of Microgrid based on ε constraint multi-objective biogeography-based optimization algorithm

Dynamic Optimal Scheduling of Microgrid Based on ε constrai...

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Automation, Control and Robotics Engineering (CACRE), International Conference on

作者： Bo Li Jue Wang Nan Xia College of Information Science and Engineering Dalian Polytechnic University

ISBN: (数字)9781728198880

ISBN: (纸本)9781728198897

Aiming at the important research topic of optimal scheduling in microgrid field, the model for multi-objective optimal dispatching of microgrid is established with the objective of minimum economic and environmental treatment costs. On this basis, the model is organically integrated with constraint handling technology, multi-objective optimization and biogeography-based optimization algorithm and then a constrained multi-objective evolutionary model for biogeography-based optimization is further established. The corresponding constraint handling mechanism, the determining strategy of habitat suitability index and migration strategy are improved, and the convergence performance and the distribution uniformity of Pareto frontier for multi-objective evolutionary algorithm are effectively enhanced. Applied to the optimal scheduling of typical microgrid systems, the effectiveness of the proposed model and method is verified.

关键词： microgrid optimal scheduling biogeography-based optimization algorithm constrained multi-objective handling adaptive migration strategy

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An Effective biogeography-based optimization algorithm for Flow Shop Scheduling with Intermediate Buffers

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Chinese Journal of Electronics 2018年第6期27卷 1141-1150页

作者： LIU Shufen WANG Pengfei YAO Zhilin College of Computer Science and Technology Jilin University

This paper proposes an Effective biogeography-based optimization(EBBO) algorithm for solving the flow shop scheduling problem with intermediate buffers to minimize the Total flow time(TFT). Discrete job permutations are used to represent individuals in the EBBO so the discrete problem can be solved directly. The NEH heuristic and NEH-WPT heuristic are used for population initialization to guarantee the diversity of the solution. Migration and mutation rates are improved to accelerate the search process. An improved migration operation using a two-points method and mutation operation using inverse rules are developed to prevent illegal solutions. A new local search algorithm is proposed for embedding into the EBBO algorithm to enhance local search *** simulations and comparisons demonstrated the superiority of the proposed EBBO algorithm in solving the flow shop scheduling problem with intermediate buffers with the TFT criterion.

关键词： Flow shop scheduling Intermediate buffer Total flow time(TFT) biogeography-based optimization algorithm Local search algorithm

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Four Categories of Human Teeth based on biogeography-based optimization algorithm and Multilayer Perceptrons

Four Categories of Human Teeth Based on Biogeography-based O...

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2018 2nd International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA 2018)

作者： Mengmeng Yang Arifur Nayeem Liam O'Donnell School of Computer Science and Technology Nanjing Normal University

Teeth is a structure in which many vertebrates exist. For some animals, such as lions, tigers and so on, teeth are chewing tools and weapons to protect themselves. But for human, it also carries the beauty of the face. When the teeth are sick, accurate classification of the teeth seems particularly important. The main purpose of this paper is to classify the teeth accurately using biogeography-based optimization algorithm(BBO) and Multilayer perceptron(MLP). The results showed our method achieved 83.75± 2.95%, 83.50± 5.16%, 84.00± 5.16%, and 84.75± 3.43% accuracy for identifying incisor, canine, premolar, and molar.

关键词： teeth classification biogeography-based optimization algorithm multilayer perceptrons

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biogeography-based optimization algorithm for Solving the Set Covering Problem 5th

Biogeography-Based Optimization Algorithm for Solving the Se...

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5th Computer Science On-line Conference (CSOC)

作者： Crawford, Broderick Soto, Ricardo Riquelme, Luis Olguin, Eduardo Pontificia Univ Catolica Valparaiso Valparaiso Chile Univ San Sebastian Santiago Santiago Metrop Chile Univ Cent Chile Santiago Santiago Metrop Chile Univ Autunoma Chile Temuco Chile Univ Cient Sur Lima Peru

ISBN: (纸本)9783319336251;9783319336237

biogeography-based optimization algorithm (BBOA) is a kind of new global optimization algorithm inspired by biogeography. It mimics the migration behavior of animals in nature to solve optimization and engineering problems. In this paper, BBOA for the Set Covering Problem (SCP) is proposed. SCP is a classic combinatorial problem from NP-hard list problems. It consist to find a set of solutions that cover a range of needs at the lowest possible cost following certain constraints. In addition, we provide a new feature for improve performance of BBOA, improving stagnation in local optimum. With this, the experiment results show that BBOA is very good at solving such problems.

关键词： biogeography-based optimization algorithm Set Covering Problem

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Migration Ratio Model Analysis of biogeography-based optimization algorithm and Performance Comparison

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2016年第3期9卷 544-558页

作者： Wang, Jie-sheng Song, Jiang-di Univ Sci & Technol Liaoning Natl Financial Secur & Syst Equipment Engn Res Ct Anshan Liaoning Provin Peoples R China Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114044 Liaoning Provin Peoples R China

biogeography-based optimization (BBO) algorithm is based on species migration between habitats to complete information circulation and sharing, which achieves the global optimization by improving the adaptability of habitats. In this paper, the basic migration balance model of biogeography theory is elaborated. based on the population adaptive migration mechanism of BBO algorithm, the algorithm procedure is set up. Seven linear or nonlinear migration ratio models (including three new migration ratio models) are described. Simulation experiments are carried out on eight testing functions to verify the proposed migration ratio models. Simulation results show that different migration ratio model has different influence on the optimization performance of BBO algorithm, in which the sine migration ratio model has the best optimization performance. This also represents that the nonlinear migration ratio model close to the natural laws outperforms other simple linear migration ratio models.

关键词： biogeography-based optimization algorithm migration ratio model function optimization performance comparison

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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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