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7th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Hong, Xue Pu, Han North China Elect Power Univ Dept Automat Baoding Peoples R China

ISBN: (纸本)9781479986460

biogeography-based optimization (BBO) algorithm is an better global optimization algorithm. Compared with traditional algorithms, BBO algorithm has advantages of less parameters, simple calculation, fast convergence. The supercritical (ultra-supercritical) unit flue-reheat steam temperature object has characteristics such as large inertia and hysteresis. Traditional PID controller parameter setting method is difficult to obtain good control effect. Aiming at this problem, the article put up a hybrid optimization method based on BBO algorithm. The method has a significant comprehensive optimization result in the test of value disturbance, internal disturbances and external disturbance. The optimized PID controller also has better tracking ability and a better performance to fight inside and outside interference. The simulation results show that it's feasible and effective to control the temperature by using the optimized PID controller on flue reheat steam control system.

关键词： component biogeography-based optimization algorithm reheat steam temperature hybrid optimization

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An improved biogeography-based optimization approach for beam pattern optimizations of linear and circular antenna arrays

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INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 2021年第6期34卷 e2910-e2910页

作者： Liu, Lingling Wang, Aimin Sun, Geng Zheng, Tingting Yu, Chaoyu Jilin Univ Coll Comp Sci & Technol Changchun Jilin Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun Peoples R China

Antenna array makes an increasingly valuable contribution to various fields of communications. The beam pattern synthesis is one of the significant properties of antenna array. In this article, suppressing the maximum sidelobe levels (SLLs) of different antenna arrays is our main objective. Specifically, we first formulate the beam pattern optimization problems of the linear antenna array (LAA) and circular antenna array (CAA), respectively, and propose an improved biogeography-based optimization (IBBO) approach to tackle the issue. IBBO introduces the hybrid migration and hybrid mutation operators by adopting several improved factors to strengthen the effect of conventional biogeography-based optimization (BBO) algorithm. Emulate experiments are built to evaluate the performance of the IBBO for dealing with the formulated antenna array beam pattern optimization problems comprehensively.

关键词： antenna arrays biogeography-based optimization algorithm chaos harmony search Levy flight sidelobe level

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An offloading strategy with soft time windows in mobile edge computing

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COMPUTER COMMUNICATIONS 2020年 164卷 42-49页

作者： Wei, Zhenchun Pan, Jie Lyu, Zengwei Xu, Junyi Shi, Lei Xu, Juan Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei Peoples R China Minist Educ Engn Res Ctr Safety Crit Ind Measurement & Contro Hefei Peoples R China

Task offloading is a core function of mobile edge computing to make up shortage of energy in mobile terminal. Scholars deploy techniques including cloud servers to improve offloading process with hard time window constraints, as there is strict deadline, which is exclusive to the flexibility of users facing task time-out. To increase the flexibility, this paper examines the time-varying features of users' tolerance as the soft time windows perform. Pay of task processing varies because of completion time, which is a combinatorial optimization problem. This paper proposes a Hybrid Genetic algorithm and biogeography-based optimization (HGABBO) offloading algorithm. The experiments show that the proposed algorithm is effective to multitask processes and outperforms the Artificial Fish algorithm and Ant System algorithm.

关键词： Mobile edge computing Soft time window Offloading biogeography-based optimization algorithm

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Proposing a procedure for multi-objective optimization of cascade thermoelectric coolers to achieve maximum cooling capacity and COP

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JOURNAL OF NON-EQUILIBRIUM THERMODYNAMICS 2024年第3期49卷 309-333页

作者： Hadidi, Amin Univ Tabriz Fac Mech Engn Tabriz Iran

A novel approach is proposed for optimal designing two-stage thermoelectric coolers using the biogeography-based optimization algorithm. For the comprehensiveness of the research, both types of two-stage coolers, including electrically serial and separated stages, have been investigated. Using the proposed procedure, first by selecting the most important performance parameters of the cooler, i.e. COP and the cooling capacity as objective functions, the efficiency and the validity of the approach are proved by comparing the results with results of the previous researchers. In all the examined cases, the proposed approach has led to the enhancing of the cooler performance. The results of single-objective optimization showed that using the proposed method, the cooling capacity and COP can be improved up to 17 % and 8.5 % compared to the previous designs, respectively. In the following, multi-objective optimization of thermoelectric coolers is conducted, taking into account both the objectives of the COP and the cooling capacity. Finally, Pareto optimal solutions and the exergetic efficiency of the optimized cooler were calculated for different values of the weight factor of the multi-objective function. The results of multi-objective optimization showed that by increasing the COP weight factor in the objective function, the exergetic efficiency of the refrigeration system enhances.

关键词： biogeography-based optimization algorithm two-stage thermoelectric cooler maximum cooling capacity COP multi-objective optimization exergy efficiency

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A hybrid approach combining an extended BBO algorithm with an intuitionistic fuzzy entropy weight method for QoS-aware manufacturing service supply chain optimization

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NEUROCOMPUTING 2018年 272卷 439-452页

作者： Zhang, Shuai Xu, Song Zhang, Wenyu Yu, Dejian Chen, Kai Zhejiang Univ Finance & Econ Sch Informat Hangzhou 310000 Zhejiang Peoples R China

With the increasing complexity of manufacturing tasks, selecting an optimal manufacturing service supply chain has become an important challenge, especially in fuzzy manufacturing environments. In this study, we first propose a new fuzzy quality of service (QoS)-aware multi-objective mathematical model for evaluating the global QoS value of a manufacturing service supply chain including four basic composite structures. Then, we present a hybrid approach that combines the biogeography-based optimization (BBO) algorithm with the intuitionistic fuzzy entropy weight (IFEW) method, to effectively solve the manufacturing service supply chain optimization (MSSCO) problem. Furthermore, the IFEW method is adapted to obtain a more accurate preference weight for each QoS attribute, by further considering the degrees of influence of different decision makers. In addition, the BBO algorithm is extended to effectively obtain a manufacturing service supply chain (MSSC) with an optimal fuzzy QoS value by improving its standard migration and mutation operators, and introducing a new operator called the invasion operator. Finally, we perform three sets of simulation experiments to illustrate the practicality, effectiveness, and efficiency of our proposed method, based on comparisons with the standard BBO algorithm and two other population-based optimization algorithms, namely the genetic algorithm and differential evolution. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Manufacturing service supply chain Fuzzy QoS-aware IFEW method biogeography-based optimization algorithm

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A SELF-ADAPTIVE biogeography-based algorithm TO SOLVE THE SET COVERING PROBLEM

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RAIRO-OPERATIONS RESEARCH 2019年第3期53卷 1033-1059页

作者： Crawford, Broderick Soto, Ricardo Olivares, Rodrigo Riquelme, Luis Astorga, Gino Johnson, Franklin Cortes, Enrique Castro, Carlos Paredes, Fernando Pontificia Univ Catolica Valparaiso Valparaiso Chile Univ Valparaiso Valparaiso Chile Univ Playa Ancha Valparaiso Chile Univ Tecn Federico Santa Maria Valparaiso Chile Univ Diego Portales Santiago Chile

Using the approximate algorithms, we are faced with the problem of determining the appropriate values of their input parameters, which is always a complex task and is considered an optimization problem. In this context, incorporating online control parameters is a very interesting issue. The aim is to vary the parameters during the run so that the studied algorithm can provide the best convergence rate and, thus, achieve the best performance. In this paper, we compare the performance of a self-adaptive approach for the biogeography-based optimization algorithm using the mutation rate parameter with respect to its original version and other heuristics. This work proposes altering some parameters of the metaheuristic according to its exhibited efficiency. To test this approach, we solve the set covering problem, which is a classical optimization benchmark with many industrial applications such as line balancing production, crew scheduling, service installation, databases, among several others. We illustrate encouraging experimental results, where the proposed approach is capable of reaching various global optimums for a well-known instance set taken from the Beasleys OR-Library, and sometimes, it improves the results obtained by the original version of the algorithm.

关键词： Metaheuristics biogeography-based optimization algorithm set covering problem

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BBO algorithm-based tuning of PID controller for speed control of synchronous machine

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2016年第4期24卷 3274-3285页

作者： Kasilingam, Gowrishankar Pasupuleti, Jagadeesh Univ Tenaga Nas Coll Engn Dept Elect Power Selangor Malaysia

A biogeography-based optimization (BBO) algorithm was used for tuning the parameters of a proportional integral derivative (PID) controller-based power system stabilizer (PSS). The proposed method minimizes the low frequency electromechanical oscillations (0.1-2.5 Hz) and enhances the stability of the power system by optimally tuning the PID parameters. This was achieved by minimizing the objective function of the integral square error for various disturbances. The performance of the BBO algorithm was tested on a single machine infinite bus system for a different range of operating conditions and the results were compared with particle swam optimization, adaptation law, and conventional PSS. The result analysis concluded that the BBO algorithm damps out the low frequency oscillations in the rotor of the synchronous machine effectively when compared to other methods. The algorithms were simulated with MATLAB/Simulink. The results from the simulation showed that the proposed controller yields a fast convergence rate and better dynamic performance.

关键词： Power system stabilizer PID controller particle swarm optimization adaptation law biogeography-based optimization algorithm

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Optimum design of reinforced earth walls using evolutionary optimization algorithms

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NEURAL COMPUTING & APPLICATIONS 2020年第16期32卷 12079-12102页

作者： Kashani, Ali R. Saneirad, Ali Gandomi, Amir H. Univ Memphis Dept Civil Engn Memphis TN 38152 USA Arak Univ Dept Civil Engn Arak Iran Stevens Inst Technol Sch Business Hoboken NJ 07030 USA

This study addresses the optimum cost design of mechanically stabilized earth (MSE) using geosynthetics. The design process of MSEs is mathematically programmed based on an objective function depending on the length of reinforcements and vertical distance of reinforced layers. Design restrictions control the final design to be valid in terms of constraints. The aim is to explore the efficiency of evolutionary-based algorithms in dealing with MSE optimization problem along with automating the minimum cost design of MSE walls. To this end, three evolutionary algorithms, differential evolution (DE), evolution strategy, and biogeography-based optimization algorithm (BBO), are tackled to solve this problem. Comprehensive computational simulations confirm the impact of different effective parameters variation on the final design. Finally, the BBO algorithm performed the best, while DE recorded the most unsatisfactory results.

关键词： Geosynthetic reinforced earth retaining wall Mechanically stabilized earth Evolutionary algorithms Differential evolution Evolutionary strategy biogeography-based optimization algorithm

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WPD and DE/BBO-RBFNN for solution of rolling bearing fault diagnosis

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NEUROCOMPUTING 2018年 312卷 27-33页

作者： Zhang, Qing Gao, Junwei Dong, Honghui Mao, Yunlong Qingdao Univ Coll Automat & Elect Engn Qingdao 266071 Peoples R China Beijing Jiaotong Univ Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China

A new system of fault diagnosis which combines wavelet packet decomposition (WPD), radical basis function neural network (RBFNN) and a hybrid differential evolution with biogeography-based optimization (DE/BBO) algorithm is designed in this paper to improve the efficiency and accuracy of rolling bearing fault diagnosis. biogeography-based optimization (BBO) is a new biogeography inspired algorithm, however it has the disadvantages of early-maturing and instable convergence. Combining BBO with differential evolution (DE) and making small modifications for the mutation strategy of DE, an improved DE/BBO algorithm is formed. Firstly, the fault feature vectors of original rolling bearing fault signals are effectively obtained by wavelet packet decomposition and reconstruction. Secondly, the RBF neural network is optimized by DE/BBO algorithm, the fault types of rolling bearing are trained and diagnosed next. The result of MATLAB simulation shows that in contrast to the traditional RBFNN, the fitness and precision of bearing fault diagnosis are higher and the root-mean-square error (RMSE) is lower because of the introduction of DE/BBO algorithm. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Wavelet packet decomposition Differential evolution biogeography-based optimization algorithm Radical basis function neural network Root-mean-square error Rolling bearing fault diagnosis

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A Fuzzy Cooperative Approach to Resolve the Image Segmentation Problem

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2021年第3期12卷 188-214页

作者： Abdellahoum, Hamza Boukra, Abdelmajid Univ Sci & Technol Houari Boumediene Dept Comp Sci LSI Lab Bab Ezzouar Algeria

The image segmentation problem is one of the most studied problems because it helps in several areas. In this paper, the authors propose new algorithms to resolve two problems, namely cluster detection and centers initialization. The authors opt to use statistical methods to automatically determine the number of clusters and the fuzzy sets theory to start the algorithm with a near optimal configuration. They use the image histogram information to determine the number of clusters and a cooperative approach involving three metaheuristics, genetic algorithm (GA), firefly algorithm ( FA). and biogeography-based optimization algorithm (BBO), to detect the clusters centers in the initialization step. The experimental study shows that, first, the proposed solution determines a near optimal initial clusters centers set leading to good image segmentation compared to well-known methods;second, the number of clusters determined automatically by the proposed approach contributes to improve the image segmentation quality.

关键词： biogeography-based optimization algorithm Clustering Firefly algorithm Fuzzy C-Means Genetic algorithm Metaheuristics optimization Segmentation

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