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A biogeography-based optimization algorithm with modified migration operator for large-scale distributed scheduling with transportation time

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期231卷

作者： Zhang, Yaya Gu, Xingsheng East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China

To expand production scale and enhance enterprise competitiveness, modern supply chains and manufacturing systems have shifted from the single factory production to the multi-factory production network with the characteristics of large scale, diversification of varieties, redundant production factories, flexible production processes, and high-value products. This production mode is called distributed scheduling. The transportation time caused by geographically distributed factories in Distributed Scheduling Problem (DSP) with a seriesparallel network plays an important role in the scheduling scheme. To minimize the global makespan over all factories, a two-segment representation has been taken as an encoding scheme while the decoding procedure applies the First Come First Served (FCFS) heuristic rule. A biogeography-based optimization with Modified Migration Operator (BBO-MMO) is then proposed to address the heterogeneous DSP with transportation time between factories at adjacent processing stages, which improves the migration operator by modifying the immigration rate according to processing time and transportation time. A cosine migration model has been adopted to enhance the ability of BBO-MMO to break through local optimum. BBO-MMO is compared with the Improved Memetic algorithm (IMA), and several groups of examples with various parameters have been generated to test the performance of BBO-MMO. The results show that the proposed BBO-MMO can effectively solve the large-scale heterogeneous DSP with transportation time.

关键词： Heterogeneous distributed scheduling Transportation time biogeography-based optimization algorithm Heuristic rule

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Multi-layer perceptron classification method of medical data based on biogeography-based optimization algorithm with probability distributions

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APPLIED SOFT COMPUTING 2022年 121卷 108766-108766页

作者： Li, Xu-Dong Wang, Jie-Sheng Hao, Wen-Kuo Wang, Min Zhang, Min Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114044 Peoples R China

In the field of medical informatics, the accuracy of medical data classification plays a vital role. Multi-layer Perceptron (MLP), as one of the most widely used neural networks, has been widely used in the medical fields. In recent years, the biogeography-based optimization (BBO) algorithm has been proposed to train MLP, but the original algorithm often encounters local minimums, slow convergence, and sensitivity to initialized values during the optimization process. To this end, this paper adopted the different probability distributions to improve the BBO (PD-BBO) algorithm to train MLP so as to improve medical data classification accuracy. These distributions include Gamma distribution, Beta distribution, Gaussian distribution, Exponential distribution, Poisson distribution, Geometric distribution, Rayleigh distribution and Weber distribution Then these different probability distributions were embed into the migration process of the BBO algorithm to replace the random distribution and the migration probability was defined. Finally, simulation experiments were carried out, and the benchmark function was used to prove the effectiveness of the proposed algorithms. And then it was used to train a multi-layer perceptron, and five medical data sets were selected for classification. After that, the performance of the standard BBO algorithm and five typical meta-heuristic algorithms were compared. The results showed that the PD-BBO algorithms to train MLP was better than the BBO algorithm and the selected meta-heuristic algorithms, and the classification accuracy has been improved to a certain extent. (c) 2022 Elsevier B.V. All rights reserved.

关键词： biogeography-based optimization algorithm Multi-layer perceptron Probability distribution Medical data classification

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A robust approach for optimal design of plate fin heat exchangers using biogeography based optimization (BBO) algorithm

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APPLIED ENERGY 2015年 150卷 196-210页

作者： Hadidi, Amin Islamic Azad Univ Ahar Branch Dept Mech Engn Ahar Iran

Design of plate-fin heat exchangers is a very complex task generally based on trial and error process. Traditional designing methods are very time consuming and do not guarantee the archive of an optimal solution;therefore heuristic based computation methods are used, usually. So, in present paper a new design method proposed for optimization of plate fin heat exchangers using biogeography-based optimization (BBO) algorithm. The BBO algorithm has some advantages in detecting the global minimum compared with other heuristic algorithms. In present research the BBO scheme has been employed for optimal design of the plate fin heat exchanger by minimization of the total annual cost, heat transfer area and total pressure drops of the equipment considering main structural and geometrical parameters of the exchanger as design variables. based on proposed approach, a full computer code was developed and three various case studies are investigated by it to illustrate the effectiveness and accuracy of the proposed method. Comparison of the results with those obtained by previous methods reveals that the BBO algorithm can be successfully employed for optimization of plate fin heat exchangers. Finally, parametric analysis carried out to evaluate the sensitivity of the proposed method to the cost and structural parameters. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Economic optimization Plate fin heat exchanger biogeography-based optimization algorithm Sensitivity analysis Pareto optimal solutions

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A new biogeography-based optimization (BBO) algorithm for the flexible job shop scheduling problem

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2012年第9-12期58卷 1115-1129页

作者： Rahmati, Seyed Habib A. Zandieh, M. Shahid Beheshti Univ Management & Accounting Fac Dept Ind Management Tehran Iran Islamic Azad Univ Ind & Mech Engn Fac Qazvin Branch Qazvin Iran

biogeography-based optimization (BBO) algorithm is a new kind of optimization technique based on biogeography concept. This population-based algorithm uses the idea of the migration strategy of animals or other species for solving optimization problems. In this paper, the BBO algorithm is developed for flexible job shop scheduling problem (FJSP). It means that migration operators of BBO are developed for searching a solution area of FJSP and finding the optimum or near-optimum solution to this problem. In fact, the main aim of this paper was to provide a new way for BBO to solve scheduling problems. To assess the performance of BBO, it is also compared with a genetic algorithm that has the most similarity with the proposed BBO. This similarity causes the impact of different neighborhood structures being minimized and the differences among the algorithms being just due to their search quality. Finally, to evaluate the distinctions of the two algorithms much more elaborately, they are implemented on three different objective functions named makespan, critical machine work load, and total work load of machines. BBO is also compared with some famous algorithms in the literature.

关键词： biogeography-based optimization algorithm Flexible job shop scheduling problem Makespan Machine work load

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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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Multi-objective biogeography-based optimization algorithm for the Control of Robotic Manipulator

Multi-objective Biogeography-based Optimization Algorithm fo...

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2012 2nd International Conference on Advanced Materials and Computer Science(ICAMCS 2012)

作者： Xu Zhi-dan Mo Hong-wei Xu Zhi-jie Zhang Jianqin Automation College Harbin Engineering University Institute of Basic Science Harbin University of Commerce School of Science Beijing University of Civil Engineering and Architecture School of Surveying and Mapping Engineering Beijing University of Civil Engineering and Architecture

A novel multi-objective biogeography-based optimization algorithm(MBBO) is proposed and is applied to tune the parameters of PID controllers for robotic manipulator. In MBBO, a new migration operator with disturbance term is put forward to ensure the convergence of the population and promote the diversity of solutions. Simulation results on PID control for robotic manipulator show that the PID controllers tuned by MBBO have better reference tracking performance in closed loop.

关键词： PID controller Multi-objective optimization biogeography-based optimization algorithm Robotic manipulator control

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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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A fractional order fuzzy PID for load frequency control of four-area interconnected power system using biogeography-based optimization

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2019年第2期29卷 e2735.1-e2735.17页

作者： Mohammadikia, Reza Aliasghary, Mortaza Urmia Univ Technol Fac Elect & Comp Engn POB 57155-419 Orumiyeh Iran

In this study, a fractional order fuzzy proportional-integral-derivative (FOFPID) controller is designed for load frequency control of four-area interconnected power systems. The model of system consists of three reheat thermal turbine units (Area1, Area2, and Area3) and one hydro power plant (Area4). Therefore, the proposed controller (with same and different parameters) is employed for the first three zones, and different controller parameters are considered for Area4 due to existence of hydro turbine. In order to minimize frequency and power tie-line deviations in the system, biogeography-based optimization (BBO) algorithm is utilized to tune controller parameters. Finally, the proposed technique is compared with three different controllers including PID, fuzzy PID (FPID), and fractional order PID. For a fair comparison, the parameters of aforesaid controllers are also tuned by BBO algorithm. The results of different simulation cases indicate that the FOFPID controller has a superior performance and transient response compared with the other approaches against various load disturbances.

关键词： biogeography-based optimization algorithm four-area interconnected power system fractional order controller load frequency control

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Smart homes energy management: Optimal multi-objective appliance scheduling model considering electrical energy storage and renewable energy resources

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Heliyon 2025年第3期11卷 e42417页

作者： Dehghani, Moslem Bornapour, Seyyed Mohammad Electrical Engineering Department Faculty of Engineering Yasouj University Yasouj Iran

As smart homes (SHs) integrate into distribution systems, microgrid scheduling has become increasingly important because of their schedulable loads that reduce peak loads. Accordingly, a multi-objective optimization approach is presented for SH energy management (SHEM) and demand response (DR) programs with 30-min time slots. Time-of-use tariffs are used in the suggested scheme, and the primary goal is to minimize the daily bills and peak-to-average ratio (PAR), simultaneously. This scheme includes flexible and fixed home appliances. Here, the SHEM system consists of photovoltaic and wind turbine systems in combination with an electrical energy storage (EES) system to provide optimum peak load performance at peak times, based on the discharging and charging mechanism. Also, in the proposed mathematical formulation, the bought and selling energy is considered during the day. An improved biogeography-based optimization algorithm (IBBO) is used to solve the multi-objective problem. The first step is to develop the equations for general electrical appliances of particular SH consumers, and then minimize the mentioned two objectives. based on the outcomes under different scenarios such as different sizes of renewable energy resources, various charging/discharging rates, and different selling electricity tariff ratios, PAR and operational costs are reduced, and the electricity is sold to upstream. Moreover, simulations show that the suggested scheme produces the optimal outcomes, in which both objectives are near their optimal levels, as shown in the Pareto Front of the optimal solutions. The maximum standard deviation of total objective function between all cases for IBBO, gray wolf optimizer (GWO), and whale optimization algorithm (WOA) are 6.55, 17.22, and 24.87, respectively, which show the robustness of IBBO in finding the best solution in comparisons of other algorithms. Also, the average solution of IBBO is lower than GWO, and WOA, which shows the performance and s

关键词： biogeography-based optimization algorithm Electricity selling Energy storage systems Home energy management systems Photovoltaic Wind turbine

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optimization of Condenser Vacuum based on Neural Network and SA-BBO

Optimization of Condenser Vacuum based on Neural Network and...

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3rd International Conference on Energy, Environment and Sustainable Development (EESD 2013)

作者： Zhang, Lei Ye, Yalan Si, Fengqi Nanjing Coll Chem Technol Dept Mech Technol Nanjing Jiangsu Peoples R China Jiangsu Maritime Inst Dept Marine Engn Nanjing Peoples R China Southeast Univ Sch Energy & Environm Nanjing Peoples R China

ISBN: (纸本)9783037859728

The optimization of condenser vacuum is significant to improve efficiency and save energy in the power plant. Taking a 600MW unit as the research object, the condenser vacuum optimization model was established synthetically based on neural network, simulated annealing and biogeography optimization hybrid algorithm (SA-BBO). Circulating pumps power, slight increase of turbine power as well as the market value difference between coal and electric were included in the model. The objective function of the model is to maximize the profit of the power plant. The most effective combinations of the condenser vacuum and the circulating water pump were calculated eventually in different operating conditions by using characteristic analysis of variable condenser conditions. In a certain condition, running three circulating pumps for two steam turbines instead of two pumps can make the condenser vacuum reduce 0.49kPa, and increase revenue 110.2 yuan/h.

关键词： Neural network simulated annealing biogeography-based optimization algorithm circulating water pump operating optimization condenser optimum vacuum

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