Статті в журналах: "HYBRID OPTIMIZATION MODEL"

1

Xue, Li-hua, and Yong-hua Li. "Hybrid optimization model of product concepts." Journal of Central South University of Technology 13, no. 1 (February 2006): 105–9. http://dx.doi.org/10.1007/s11771-006-0115-4.

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2

Gao, Xiaoli, Yangfei Yuan, Jie Li, and Weifeng Gao. "A Hybrid Search Model for Constrained Optimization." Discrete Dynamics in Nature and Society 2022 (September 28, 2022): 1–15. http://dx.doi.org/10.1155/2022/1190174.

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This paper proposes a hybrid model based on decomposition for constrained optimization problems. Firstly, a constrained optimization problem is transformed into a biobjective optimization problem. Then, the biobjective optimization problem is divided into a set of subproblems, and different subproblems are assigned to different Fitness functions by the direction vectors. Different from decomposition-based multiobjective optimization algorithms in which each subproblem is optimized by using the information of its neighboring subproblems, the neighbors of each subproblem are deFined based on corresponding direction vector only in the method. By combining three main components, namely, the local search model, the global search model, and the direction vector adjusting strategy, the population can gradually move toward the global optimal solution. Experiments on two sets of test problems and Five real-world engineering design problems have shown that the proposed method performs better than or is competitive with other compared methods.

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3

Miracle, D. Blandina, R. K. Viral, P. M. Tiwari, and Mohit Bansal. "Hybrid Metaheuristic Model for Optimal Economic Load Dispatch in Renewable Hybrid Energy System." International Transactions on Electrical Energy Systems 2023 (April 6, 2023): 1–25. http://dx.doi.org/10.1155/2023/5395658.

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Hybrid generating systems in power networks have emerged as a result of the rapid growth of renewable infrastructure and widespread support for green energy. One of the most significant problems in designing and operating an electric power generation system is the efficient scheduling of all power generation facilities to meet the rising power demand. Economic load dispatch (ELD) is a generic procedure in the electrical power system, and the ELD in power system problems involves scheduling the power generating units to reduce cost and satisfy system constraints. Metaheuristic algorithms are gaining popularity for solving constrained ELD issues because of their larger global solution capacity, flexibility, and derivative-free construction. In this research, the ELD problem of integrated renewable resources is solved using a unique solution model based on hybrid optimization. Furthermore, this work considers multiobjectives such as total wind generation cost, total cost function of thermal units, and penalty cost function. The hybrid optimization model optimizes the power generation of thermal power plants within the maximum and minimum limitations. Additionally, the turbines are selected optimally by the hybrid optimization model to ensure the power generation of wind turbines based on the demands. The proposed hybrid optimization is a combination of particle swarm optimization (PSO) and cat swarm optimization (CSO), and the new algorithm is referred to as the particle oriented cat swarm optimization model (POCSO). Finally, the performance of the proposed work is compared to other conventional models. In particular, the cost function of POCSO is 6.25%, 6%, 11.7%, 36%, 27%, and 46.42% better than the cost function of whale optimization algorithm (WOA), elephant herd optimization (EHO), moth-flame optimization (MFO), dragonfly algorithm (DA), sealion optimization (SLnO), CSO, and PSO methods, respectively. Also, for IEEE-30 bus system, the best value of the proposed work is 7.46%, 5.41%, 16.30%, 14.88%, 17.60%, 13.86%, 15.21%, 17.49%, and 4.27% better than that of the PSO, CSO, SLnO, DA, MFO, EHO, WOA, multiagent glowworm swarm optimization (MAGSO), and Harris hawks optimization-based feed-forward neural network (HHO-FNN) methods, respectively.

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4

Stevanović, Dejan, Mirjana Banković, Milica Pešić-Georgiadis, and Lazar Stojanović. "Hybrid model for uncertainty assessment in open pit optimization." Tehnika 75, no. 2 (2020): 161–71. http://dx.doi.org/10.5937/tehnika2002161s.

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5

Sukheja, Deepak, and Umesh Kumar Singh. "Novel Distributed Query Optimization Model and Hybrid Query Optimization Algorithm." International Journal of Computer Applications 75, no. 17 (August 23, 2013): 22–32. http://dx.doi.org/10.5120/13203-0461.

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6

Regitha, M. R., Dr Paul Varghese, Shailesh Sivan, and Antony Nijo. "Handoff Delay Optimization Using Hybrid Prediction Model." International Journal of Networked and Distributed Computing 6, no. 2 (2018): 99. http://dx.doi.org/10.2991/ijndc.2018.6.2.5.

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7

Cahyandari, R., R. L. Ariany, and Sukono. "Optimization of hybrid model on hajj travel." IOP Conference Series: Materials Science and Engineering 332 (March 2018): 012042. http://dx.doi.org/10.1088/1757-899x/332/1/012042.

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8

Franco-Lara, E., N. Volk, T. Hertel, V. Galvanauskas, and A. Lübbert. "Model-Supported Optimization of Recombinant Protein Production Using Hybrid Models." Chemie Ingenieur Technik 73, no. 6 (June 2001): 654–55. http://dx.doi.org/10.1002/1522-2640(200106)73:6<654::aid-cite6543333>3.0.co;2-8.

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9

He, Jian Feng, and Xiao Xiong Jin. "Multiobjective Optimization of Hybrid Electrical Vehicle Powertrain Mounting System Using Hybrid Genetic Algorithm." Applied Mechanics and Materials 87 (August 2011): 30–37. http://dx.doi.org/10.4028/www.scientific.net/amm.87.30.

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Анотація:

Powertrain mounting system of a Hybrid Electrical Vehicle (HEV) is analyzed and researched, the expression of energy distribution matrix and that of mounting reaction force are derived, and mathematical model of the system is established in Matlab. Correctness of the model established is tested and verified through model establishing for simulation and calculation in ADAMS. Features of Hybrid Genetic Algorithm (HGA) for multiobjective optimization are analyzed and researched, model for calculation of multiobjective optimization using Hybrid Genetic Algorithm is established, targets for optimization of the system are determined, and optimization is executed based on the mounting stiffness parameters. The result that the system is optimized apparently by Hybrid Genetic Algorithm is revealed through contrast of the energy distribution matrix and mounting reaction force of pre and post-optimization.

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10

Li, Wenwei, and Long Zhu. "Multi-objective Optimization Method for Hybrid Energy Storage Capacity of Wind Farm Based on Source-load Interaction." Journal of Physics: Conference Series 2418, no. 1 (February 1, 2023): 012054. http://dx.doi.org/10.1088/1742-6596/2418/1/012054.

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Abstract The traditional method for multi-objective optimization of a wind farm’s hybrid energy storage capacity does not fully consider the impact of source-load interaction on wind power consumption capacity, resulting in a high total system operation cost. Therefore, a multi-objective optimization method for the hybrid energy storage capacity of the wind farm based on source-load interaction is proposed. According to the topological structure of the system, a multi-objective optimization model is constructed. The constraint conditions of the optimization model are studied. By synthesizing the constraint conditions, multiple objective functions are aggregated into a single objective function. The model is solved by using the ordinal optimization theory to get the optimal solution of the objective function. The experimental demonstration adopted the proposed method to improve the wind power system’s energy storage capacity. The results show that the operation cost of the system is lower after the proposed method is adopted to improve the wind farm’s hybrid energy storage capacity. Therefore, it can be proved that the total operation cost of the system can be decreased by integrating the interactive load into the hybrid energy storage optimization’s wind farm capacity.

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11

Bhuvaneshwari, A., R. Hemalatha, and T. SatyaSavithri. "Path Loss Model Optimization Using Stochastic Hybrid Genetic Algorithm." International Journal of Engineering & Technology 7, no. 4.10 (October 2, 2018): 464. http://dx.doi.org/10.14419/ijet.v7i4.10.21041.

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In the context of modeling the propagation of mobile radio signals, optimizing the existing path loss model is largely required to precisely represent the actual propagation medium. In this paper, a hybrid tuning approach is proposed by merging the stochastic Weighted Least Square method and Genetic algorithm. The proposed hybrid optimization is employed to optimize the parameters of Cost 231 Hata propagation model and is validated by cellular field strength measurements at 900 MHz in the sub urban region. The hybrid optimization is compared with optimized results of Weighted Least Square method and Genetic algorithm. The least values of Mean Square error (0.2702), RMSE (0.4798) and percentage Relative error (3.96) justify the tuning precision of the hybrid method. The proposed optimization approach could be used by network service providers to improve the quality of service and in mobile radio network planning of 900 MHz band for 4G LTE services.

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12

Manik, Shilpi. "Control System Model Reduction using Hybrid Optimization Approach." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 3 (June 25, 2020): 4006–11. http://dx.doi.org/10.30534/ijatcse/2020/225932020.

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13

Gu, J., G. Y. Li, and Z. Dong. "Hybrid and adaptive meta-model-based global optimization." Engineering Optimization 44, no. 1 (January 2012): 87–104. http://dx.doi.org/10.1080/0305215x.2011.564768.

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14

Srivastav, Achin, and Sunil Agrawal. "Multi-objective optimization of hybrid backorder inventory model." Expert Systems with Applications 51 (June 2016): 76–84. http://dx.doi.org/10.1016/j.eswa.2015.12.032.

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15

Wang, Fu-Sheng, Jin-Bao Jian, and Chuan-Long Wang. "A model-hybrid approach for unconstrained optimization problems." Numerical Algorithms 66, no. 4 (August 16, 2013): 741–59. http://dx.doi.org/10.1007/s11075-013-9757-0.

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16

Zhou, Heng, Haifeng Zhang, and Chunjie Yang. "Hybrid-Model-Based Intelligent Optimization of Ironmaking Process." IEEE Transactions on Industrial Electronics 67, no. 3 (March 2020): 2469–79. http://dx.doi.org/10.1109/tie.2019.2903770.

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17

Srivastava, Sweta, and Sudip Kumar Sahana. "Nested hybrid evolutionary model for traffic signal optimization." Applied Intelligence 46, no. 1 (July 27, 2016): 113–23. http://dx.doi.org/10.1007/s10489-016-0827-6.

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18

Samma, Hussein, Shahrel Azmin Suandi, and Junita Mohamad-Saleh. "Face sketch recognition using a hybrid optimization model." Neural Computing and Applications 31, no. 10 (April 13, 2018): 6493–508. http://dx.doi.org/10.1007/s00521-018-3475-4.

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19

Doroshenko, А. Yu, P. A. Ivanenko, and O. S. Novak. "Hybrid autotuning model with statistic modelling." PROBLEMS IN PROGRAMMING, no. 4 (December 2016): 027–32. http://dx.doi.org/10.15407/pp2016.04.027.

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Анотація:

The paper presents well known autotuning model modified with statistic modelling in order to narrow a space of search for optimal variation of the program. Proposed method was applied to optimization of hybrid parallel sorting algorithm. Experiment results on multicore system are provided.

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20

Guo, Bai Li. "The Research of Supply Chain Optimization Theory Based on Service-Oriented Manufacturing." Applied Mechanics and Materials 733 (February 2015): 964–67. http://dx.doi.org/10.4028/www.scientific.net/amm.733.964.

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This paper analysis the elements and realization form of hybrid supply chain, and describes the relationship between the service-oriented manufacturing and hybrid supply chain based on the concept, attribute and function of the service-oriented manufacturing model. Article concluded that under the service-oriented manufacturing model of the traditional supply chain integration and optimization, there are two ways: hybrid supply chain model based on endogenous and hybrid supply chain model based on symbiosis. When manufacturing enterprises selected hybrid supply chain model based on endogenous, value evaluation has become a key issue for its optimization; when the selection is hybrid supply chain model based on symbiosis, effective choice of partners will become a key problem for its optimization. Either endogenous or symbiotic hybrid supply chain model will eventually encourage manufacturing companies to build a supply chain optimization model to adapt to their own development, achieve a successful transition to the service-oriented manufacturing.

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21

Xiao, Heng, and Toshiharu Hatanaka. "Model Selecting PSO-FA Hybrid for Complex Function Optimization." International Journal of Swarm Intelligence Research 12, no. 3 (July 2021): 215–32. http://dx.doi.org/10.4018/ijsir.2021070110.

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Swarm intelligence is inspired by natural group behavior. It is one of the promising metaheuristics for black-box function optimization. Then plenty of swarm intelligence algorithms such as particle swarm optimization (PSO) and firefly algorithm (FA) have been developed. Since these swarm intelligence models have some common properties and inherent characteristics, model hybridization is expected to adjust a swarm intelligence model for the target problem instead of parameter tuning that needs some trial and error approach. This paper proposes a PSO-FA hybrid algorithm with a model selection strategy. An event-driven trigger based on the personal best update makes each individual do the model selection that focuses on the personal study process. By testing the proposed hybrid algorithm on some benchmark problems and comparing it with a simple PSO, the standard PSO 2011, FA, HFPSO to show how the proposed hybrid swarm averagely performs well in black-box optimization problems.

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22

Ye, Mei Ying, Hui Jiang, You Sheng Xu, and Xiao Dong Wang. "Bouc-Wen Hysteresis Model Parameter Identification by Means of Hybrid Intelligent Technique." Advanced Materials Research 108-111 (May 2010): 1397–402. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.1397.

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A hybrid intelligent technique is proposed to identify Bouc-Wen hysteresis model parameters. This intelligent technique is based on a hybrid of genetic algorithm (GA) and Levenberg–Marquardt algorithm (LMA). In the hybrid intelligent technique, the GA, a popular evolutionary optimization method, firstly searches the entire problem space to get a set of roughly estimated solutions. The LMA, a well-known numerical method, then performs a local optima search in order to carry out further optimizations. The performance of the hybrid intelligent technique is compared with GA method in terms of parameter accuracy. The simulation experiments of Bouc-Wen hysteresis model with known parameters are illustrated to show that a high quality solution can be achieved by means of the hybrid intelligent technique. The concept of hybrid intelligent technique may benefit the parameter identification in diverse hysteresis model problems.

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23

Wang, Wen Quan, Sheng Huang, Yuan Hang Hou, and Yu Long Hu. "Optimization of Large Vessels Principal Parameters Based on Hybrid Particle Swarm Optimization Algorithm." Applied Mechanics and Materials 253-255 (December 2012): 2172–75. http://dx.doi.org/10.4028/www.scientific.net/amm.253-255.2172.

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The multi-objective optimization design model of large vessels principal parameters was established, according to the characteristics of large ship’s scheme design. The ship stability, rapidity, and seakeeping were selected as the three objectives of the optimization model, and the minimum-deviation method was adopted to establish the unified objective function. The Particle Swarm Optimization and the Artificial Bee Colony algorithm were combined to the hybrid particle swarm algorithm, which then was used to solve the mathematical model. Through the simulation calculation, the results show that the hybrid algorithm has a better optimization performance and it is feasible for hybrid algorithm to apply in the preliminary design of large vessels.

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24

Hao, Xiang Wei, and Yang Liu. "Updating the Finite Element Model of a Bridge Model Using a Hybrid Optimization Method." Key Engineering Materials 456 (December 2010): 37–50. http://dx.doi.org/10.4028/www.scientific.net/kem.456.37.

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Finite element model updating of structures usually ends up with a nonlinear optimization problem. An efficient optimization technique is proposed firstly, which draws together the global searching capability of chaos-based optimization technique and high searching efficiency of trust-region Newton method. This hybrid approach is demonstrated to be more efficient and prone to global minimum than conventional gradient search methods and random search methods by testifying with three test functions. The optimization problem for model updating using modal frequencies and modal shapes is formulated, and a procedure to update the boundary support parameters is presented. A modal test was conducted on a beam structure, and the identified mode frequencies are employed to formulate the optimization problem with the support parameters as the updating parameters. The discrepancy between the mode frequencies of the finite element models before and after updating is greatly reduced, and the updated support condition meet quite well with the insight to the devices that form the supports.

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25

Kangah, Justice Kojo, Justice Kwame Appati, Kwaku F. Darkwah, and Michael Agbo Tettey Soli. "Implementation of an H-PSOGA Optimization Model for Vehicle Routing Problem." International Journal of Applied Metaheuristic Computing 12, no. 3 (July 2021): 148–62. http://dx.doi.org/10.4018/ijamc.2021070106.

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This work presents an ensemble method which combines both the strengths and weakness of particle swarm optimization (PSO) with genetic algorithm (GA) operators like crossover and mutation to solve the vehicle routing problem. Given that particle swarm optimization and genetic algorithm are both population-based heuristic search evolutionary methods as used in many fields, the standard particle swarm optimization stagnates particles more quickly and converges prematurely to suboptimal solutions which are not guaranteed to be local optimum. Although both PSO and GA are approximation methods to an optimization problem, these algorithms have their limitations and benefits. In this study, modifications are made to the original algorithmic structure of PSO by updating it with some selected GA operators to implement a hybrid algorithm. A computational comparison and analysis of the results from the non-hybrid algorithm and the proposed hybrid algorithm on a MATLAB simulation environment tool show that the hybrid algorithm performs quite well as opposed to using only GA or PSO.

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26

Liyi Zhang, Liyi Zhang, Xiaolin Wang Liyi Zhang, Ting Liu Xiaolin Wang, Yong Zhang Ting Liu, and Yongsheng Hu Yong Zhang. "A Novel Brownian Motion-based Hybrid Whale Optimization Algorithm." 網際網路技術學刊 24, no. 3 (May 2023): 795–808. http://dx.doi.org/10.53106/160792642023052403022.

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<p>The Whale Optimization Algorithm (WOA) has the characteristics of simple implementation and few adjustment parameters, which is remarkable in the optimization algorithm. However, there are shortcomings such as premature convergence, slow convergence in the later period, and low search accuracy. For these shortcomings, a novel Brownian motion-based hybrid whale optimization algorithm (HWOA) is proposed. The search strategy in the Harris hawk optimization algorithm (HHO) is adopted to improve the global search ability of the algorithm, and a soft besiege with progressive rapid dives is introduced to solve the problems of premature convergence and slow convergence. Besides, the Brownian motion model is used to replace WOA. The random parameters in the distance formula are calculated to better simulate the prey’s escape during the predation process, and help to jump out of the local optimum. The simulation of 23 benchmark functions shows that compared with the classic and HWOA and metaheuristic, the convergence accuracy and speed have been improved, and the local optimum can be effectively jumped out. At the same time, 10 CEC06-2019 test functions are used to test and analyze it. Compared with WOA, HWOA has better search results, which verifies the superiority of the improved algorithm.</p> <p> </p>

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27

Qi, Feng, Ying Hong Ma, and Xi Yu Liu. "A Hybrid Optimization Method for Neural Tree Network Model." Applied Mechanics and Materials 273 (January 2013): 820–25. http://dx.doi.org/10.4028/www.scientific.net/amm.273.820.

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Neural tree network model has been successfully applied to solving large numbers of complex nonlinear problems in control area. The optimization of neural tree model contains: structure and parameter, the major problem in evolving structure without parameter information was noisy fitness evaluation problem, so an improved genetic programming algorithm is proposed to synthesize the optimization process. Simulation results on two time series prediction problems show that the proposed strategy is a potential method with better performance and effectiveness.

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28

Zhang, Xiao Hong, Jun Hu, Rong Zhang, and Xiao Ling Peng. "Disassembly of Products Based on Hybrid Model Plan." Advanced Materials Research 823 (October 2013): 598–601. http://dx.doi.org/10.4028/www.scientific.net/amr.823.598.

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The hybrid model of products disassembly is built, and a method of generating disassembly sequence is given. The disassembly sequence optimization model based on Genetic Algorithms is built based on the hybrid graph, and gained an optimal disassembly sequence.

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29

Liu, Zong Li, Jie Cao, and Zhan Ting Yuan. "A Hybrid Swarm Optimization Algorithm for Complex Assignment Problem." Applied Mechanics and Materials 26-28 (June 2010): 1151–54. http://dx.doi.org/10.4028/www.scientific.net/amm.26-28.1151.

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Анотація:

The optimization of complex systems, such as production scheduling systems and control systems, often encounters some difficulties, such as large-scale, hard to model, time consuming to evaluate, NP-hard, multi-modal, uncertain and multi-objective, etc. It is always a hot research topic in academic and engineering fields to propose advanced theory and effective algorithms. As a novel evolutionary computing technique, particle swarm optimization (PSO) is characterized by not being limited by the representation of the optimization problems, and by global optimization ability, which has gained wide attentation and research from both academic and industry fields. The task assignment problem in the enterprise with directed graph model is presented. Task assignment problem with buffer zone is solved via a hybrid PSO algorithm. Simulation result shows that the model and the algorithm are effective to the problem.

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30

Niu, Ji Gao, Feng Lai Pei, Su Zhou, and Tong Zhang. "Multi-Objective Optimization Study of Energy Management Strategy for Extended-Range Electric Vehicle." Advanced Materials Research 694-697 (May 2013): 2704–9. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.2704.

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Анотація:

A new method based on genetic-particle swarm hybrid algorithm was presented for parameter optimization of energy management strategy for extended-range electric vehicle (E-REV). Taking a logic threshold control strategy of an E-REV as example, for the aims of minimizing fuel consumption and emissions, a constrained nonlinear programming parameter optimization model was established. Based on this model, genetic algorithm (GA) and particle swarm optimization (PSO) were improved respectively. Further, a genetic-particle swarm hybrid algorithm was put forward and applied to the multi-objective optimization of E-REV energy management strategy. Optimization results show that the hybrid optimization algorithm can avoid falling into local optimum and its search ability is much better than improved adaptive genetic algorithm (IAGA). This hybrid algorithm is also suitable for the control parameters optimization issues of other types of hybrid electric vehicles.

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Giraldo, Sergio A. C., Príamo A. Melo, and Argimiro R. Secchi. "Tuning of Model Predictive Controllers Based on Hybrid Optimization." Processes 10, no. 2 (February 11, 2022): 351. http://dx.doi.org/10.3390/pr10020351.

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A tuning procedure for a model predictive controller (MPC) is presented for multi-input multi-output systems. The approach consists of two steps based on a hybrid method: the goal attainment method and a variable neighborhood search. In the first step, the weights of the MPC objective function are obtained, minimizing the square error between the closed-loop response of the internal controller model and a predefined desired reference trajectory. In the second step, the integer variables of the problem (prediction and control horizons) are obtained, minimizing the square error between the closed-loop response and an optimal trajectory, aiming a controller with low computational cost and good performance. The proposed method was tested in two benchmark processes using different MPC formulations, showing satisfactory results.

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32

Verdonck, N., A. Chasse, P. Pognant-Gros, and A. Sciarretta. "Automated Model Generation for Hybrid Vehicles Optimization and Control." Oil & Gas Science and Technology – Revue de l’Institut Français du Pétrole 65, no. 1 (January 2010): 115–32. http://dx.doi.org/10.2516/ogst/2009064.

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33

Balan, Ionuţ. "A parallel hybrid cooperative model for optimization problems solving." International Journal of Academic Research 4, no. 4 (August 5, 2012): 57–62. http://dx.doi.org/10.7813/2075-4124.2012/4-4/a.8.

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34

Sun, K. T., and H. C. Fu. "A hybrid neural network model for solving optimization problems." IEEE Transactions on Computers 42, no. 2 (1993): 218–27. http://dx.doi.org/10.1109/12.204794.

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Giraldo, Sergio A. C., Príamo A. Melo, and Argimiro R. Secchi. "Tuning of Model Predictive Control Based on Hybrid Optimization." IFAC-PapersOnLine 52, no. 1 (2019): 136–41. http://dx.doi.org/10.1016/j.ifacol.2019.06.050.

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36

Fu, Gang, Yoel Sanchez, and Vladimir Mahalec. "Hybrid model for optimization of crude oil distillation units." AIChE Journal 62, no. 4 (November 21, 2015): 1065–78. http://dx.doi.org/10.1002/aic.15086.

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37

Sun, Bo, Simin Li, Jingdong Xie, and Xin Sun. "IGDT-Based Wind–Storage–EVs Hybrid System Robust Optimization Scheduling Model." Energies 12, no. 20 (October 11, 2019): 3848. http://dx.doi.org/10.3390/en12203848.

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Wind power has features of uncertainty. When wind power producers (WPPs) bid in the day-ahead electricity market, how to deal with the deviation between forecasting output and actual output is one of the important topics in the design of electricity market with WPPs. This paper makes use of a non-probabilistic approach—Information gap decision theory (IGDT)—to model the uncertainty of wind power, and builds a robust optimization scheduling model for wind–storage–electric vehicles(EVs) hybrid system with EV participations, which can make the scheduling plan meet the requirements within the range of wind power fluctuations. The proposed IGDT robust optimization model first transforms the deterministic hybrid system optimization scheduling model into a robust optimization model that can achieve the minimum recovery requirement within the range of wind power output fluctuation, and comprehensively considers each constraint. The results show that the wind–storage–EVs hybrid system has greater operational profits and less impact on the safe and stable operation of power grids when considering the uncertainty of wind power. In addition, the proposed method can provide corresponding robust wind power fluctuation under different expected profits of the decision-maker to the wind–storage–EVs hybrid system.

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38

Ren, Yan, Yuan Zheng, Chong Li, Bing Zhou, and Zhi Hao Mao. "Intelligent Optimization of Hybrid Wind/PV/pumped-Storage Power System." Advanced Materials Research 512-515 (May 2012): 719–22. http://dx.doi.org/10.4028/www.scientific.net/amr.512-515.719.

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Анотація:

The hybrid wind/PV/pumped-storage power system was the hybrid system which combined hybrid wind/PV system and pumped-storage power station. System optimization was very important in the system design process. Particle swarm optimization algorithm was a stochastic global optimization algorithm with good convergence and high accuracy, so it was used to optimize the hybrid system in this paper. First, the system reliability model was established. Second, the particle swarm optimization algorithm was used to optimize the system model in Nanjing. Finally, The results were analyzed and discussed. The optimization results showed that the optimal design method of wind/PV/pumped-storage system based on particle swarm optimization could take into account both the local optimization and the global optimization, which has good convergence high precision. The optimal system was that LPSP (loss of power supply probability) was zero.

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39

Ghosal, Sujit, and Sudipto Chaki. "Estimation and optimization of depth of penetration in hybrid CO2 LASER-MIG welding using ANN-optimization hybrid model." International Journal of Advanced Manufacturing Technology 47, no. 9-12 (August 27, 2009): 1149–57. http://dx.doi.org/10.1007/s00170-009-2234-1.

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40

Lü, Hui, and Dejie Yu. "Stability Optimization of a Disc Brake System with Hybrid Uncertainties for Squeal Reduction." Shock and Vibration 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/3497468.

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Анотація:

A hybrid uncertain model is introduced to deal with the uncertainties existing in a disc brake system in this paper. By the hybrid uncertain model, the uncertain parameters of the brake with enough sampling data are treated as probabilistic variables, while the uncertain parameters with limited data are treated as interval probabilistic variables whose distribution parameters are expressed as interval variables. Based on the hybrid uncertain model, the reliability-based design optimization (RBDO) of a disc brake with hybrid uncertainties is proposed to explore the optimal design for squeal reduction. In the optimization, the surrogate model of the real part of domain unstable eigenvalue of the brake system is established, and the upper bound of its expectation is adopted as the optimization objective. The lower bounds of the functions related to system stability, the mass, and the stiffness of design component are adopted as the optimization constraints. The combinational algorithm of Genetic Algorithm and Monte-Carlo method is employed to perform the optimization. The results of a numerical example demonstrate the effectiveness of the proposed optimization on improving system stability and reducing squeal propensity of a disc brake under hybrid uncertainties.

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41

Geng, Stefan, Andreas Meier, and Thomas Schulte. "Model-Based Optimization of a Plug-In Hybrid Electric Powertrain with Multimode Transmission." World Electric Vehicle Journal 9, no. 1 (June 13, 2018): 12. http://dx.doi.org/10.3390/wevj9010012.

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Plug-in hybrid electric vehicles are developed in order to reduce the fuel consumption and the emission of carbon dioxide. Besides the series, parallel and power split configurations are commonly used for conventional hybrid electric vehicles, and multimode transmissions are used for plug-in hybrid electric vehicles, which are able to switch between different modes like parallel or series operation of the combustion engine and electric motor. Several concepts have already been discussed and presented. These concepts comprise novel structures and multi-speed operation for the combustion engine and the electric motor, respectively. For improving the fuel and energy consumption, model-based optimizations of multimode transmissions are performed. In the first step of the optimization, the optimal number of gears and transmission ratios, as well as the corresponding fuel and energy savings, are estimated. Based on these results, a new multimode transmission concept with two-speed transmissions for the combustion engine and the electric motor has been developed. The knowledge of the concrete concept enables the further optimizations of the transmission ratios and the transmission control. In order to prove the benefit of the new and optimized transmission concept, powertrain simulations have been carried out. The new powertrain concept is compared to a powertrain concept with single-speed transmissions for the internal combustion engine (ICE) and electric motor operation. The new transmission concept enables a significant improvement of the fuel consumption.

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42

Menon, Radhika, Anju Kulkarni, Deepak Singh, and Mithra Venkatesan. "Hybrid multi‐objective optimization algorithm using Taylor series model and Spider Monkey Optimization." International Journal for Numerical Methods in Engineering 122, no. 10 (February 19, 2021): 2478–97. http://dx.doi.org/10.1002/nme.6628.

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43

Zaheer, Kashif Bin, Mohd Ismail Bin Abd Aziz, Amber Nehan Kashif, and Syed Muhammad Murshid Raza. "Two Stage Portfolio Selection and Optimization Model with the Hybrid Particle Swarm Optimization." MATEMATIKA 34, no. 1 (May 28, 2018): 125–41. http://dx.doi.org/10.11113/matematika.v34.n1.1001.

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The selection criteria play an important role in the portfolio optimization using any ratio model. In this paper, the authors have considered the mean return as profit and variance of return as risk on the asset return as selection criteria, as the first stage to optimize the selected portfolio. Furthermore, the sharp ratio (SR) has been considered to be the optimization ratio model. In this regard, the historical data taken from Shanghai Stock Exchange (SSE) has been considered. A metaheuristic technique has been developed, with financial tool box available in MATLAB and the particle swarm optimization (PSO) algorithm. Hence, called as the hybrid particle swarm optimization (HPSO) or can also be called as financial tool box particle swarm optimization (FTB-PSO). In this model, the budgets as constraint, where as two different models i.e. with and without short sale, have been considered. The obtained results have been compared with the existing literature and the proposed technique is found to be optimum and better in terms of profit.

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44

Nalluri, MadhuSudana Rao, Kannan K., Manisha M., and Diptendu Sinha Roy. "Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization." Journal of Healthcare Engineering 2017 (2017): 1–27. http://dx.doi.org/10.1155/2017/5907264.

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With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent disease diagnosis systems have been profoundly coveted. In recent years, numerous individual machine learning-based classifiers have been proposed and tested, and the fact that a single classifier cannot effectively classify and diagnose all diseases has been almost accorded with. This has seen a number of recent research attempts to arrive at a consensus using ensemble classification techniques. In this paper, a hybrid system is proposed to diagnose ailments using optimizing individual classifier parameters for two classifier techniques, namely, support vector machine (SVM) and multilayer perceptron (MLP) technique. We employ three recent evolutionary algorithms to optimize the parameters of the classifiers above, leading to six alternative hybrid disease diagnosis systems, also referred to as hybrid intelligent systems (HISs). Multiple objectives, namely, prediction accuracy, sensitivity, and specificity, have been considered to assess the efficacy of the proposed hybrid systems with existing ones. The proposed model is evaluated on 11 benchmark datasets, and the obtained results demonstrate that our proposed hybrid diagnosis systems perform better in terms of disease prediction accuracy, sensitivity, and specificity. Pertinent statistical tests were carried out to substantiate the efficacy of the obtained results.

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45

Hadi, Ahmed Adnan, and Seyed Vahab AL-Din Makki. "Improved MANET Routing Protocols Performance by Using Hybrid Cat and Particle Swarm Optimization (CPSO)." Webology 19, no. 1 (January 20, 2022): 2182–95. http://dx.doi.org/10.14704/web/v19i1/web19148.

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The technology that used in communicating data and voice among certain mobile network nodes using wireless medium and radio spectrum transmission is called Mobile networking. Generally, Mobile refers to the intent, portable and lightweight devices that may be carried by their movie users. In this paper, we proposed a hybrid version of the swarm optimization model to improve the MANET routing protocols. The proposed optimization sets optimal parameters for the MANET networks. The proposed model combines between Particle Swarm Optimization (PSO) and Cat Swarm Optimization (CSO). The methodology which will be developed in this research can be used for revealing the MANT networks or mobile sensor networks the study involve enhance mechanism(s) that can be used to avoid degraded routing issues to increase the performance. The result the obtain by proposed model satisfy best result compared with both PSO and CSO.

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46

Huang, Minshui, Yongzhi Lei, and Shaoxi Cheng. "Damage identification of bridge structure considering temperature variations based on particle swarm optimization - cuckoo search algorithm." Advances in Structural Engineering 22, no. 15 (July 9, 2019): 3262–76. http://dx.doi.org/10.1177/1369433219861728.

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Structures are always exposed to environmental conditions such as varying temperatures and noises; as a consequence, the dynamic features of structures are changed accordingly. But the model-based methods, used to detect damage using optimization algorithms to get global optimal solution, are highly sensitive to environmental conditions, experimental noises, or numerical errors. While the mechanisms of optimization algorithms are limited by local optimal solution, their convergences are not always assured. In the study, a model-based damage-identification method considering temperature variations, comprised of particle swarm optimization and cuckoo search, is implemented to detect structural damage. First, to eliminate the influence of environmental temperature, temperature change is considered as a parameter of structural material elastic modulus. A function relationship is established between environmental temperature and the material elastic modulus, and an objective function composed of natural frequency, mode shape and modal strain energy with different weight coefficients is constructed. Second, the hybrid optimization algorithm, a combination of particle swarm optimization and cuckoo search, is proposed. Third, to solve the problem of optimization algorithm convergence, the optimization performance of the hybrid optimization algorithm is validated by utilizing four benchmark functions, and it is found that the performance of the hybrid optimization algorithm is the best. In order to test the performance of the three algorithms in damage identification, a numerical simply supported beam is adopted. The results show that the hybrid optimization algorithm can identify the damage location and severity under four different damage cases without considering temperature variations and two cases considering temperature variations. Finally, the hybrid optimization algorithm is introduced to test the damage-identification performance of I-40 Bridge, an actual steel–concrete composite bridge under temperature variations, whose results show that the hybrid optimization algorithm can preferably distinguish between real damages and temperature effects (temperature gradient included); its good robustness and engineering applicability are validated.

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47

Fazeli Hassan Abadi, Moein, and Hassan Rezaei. "A Hybrid Model Of Particle Swarm Optimization And Continuous Ant Colony Optimization For Multimodal Functions Optimization." Journal of Mathematics and Computer Science 15, no. 02 (August 30, 2015): 108–19. http://dx.doi.org/10.22436/jmcs.015.02.02.

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48

Zhang, Y., H. Lin, B. Zhang, and C. Mi. "Performance Modeling and Optimization of a Novel Multi-mode Hybrid Powertrain." Journal of Mechanical Design 128, no. 1 (April 26, 2005): 79–89. http://dx.doi.org/10.1115/1.2114892.

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This paper presents a systematic model for the operation simulation and optimization of a novel multi-mode hybrid powertrain. The hybrid configuration proposed in the paper features a planetary gear train for an electric CVT mode in addition to lay-shaft gears for multiple speed ratios and realizes six operation modes in a simple structure. Detailed component level models were established for the multi-mode hybrid transmission and integrated to the overall vehicle model according to the system configuration using the Simulink/Advisor platform. The vehicle control strategy was then established with the objective to optimize the overall vehicle operation and each hybrid operation mode in terms of fuel economy and emission levels. The performance of the proposed hybrid vehicle system was studied using the developed model under various operation conditions and benchmarked with a current market model with leading performance parameters. The proposed hybrid configuration shows substantial improvements over the benchmark and is validated as a viable hybrid design based on the model simulation.

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49

Kerem, Alper, and Ali Saygin. "Scenario-based wind speed estimation using a new hybrid metaheuristic model: Particle swarm optimization and radial movement optimization." Measurement and Control 52, no. 5-6 (April 24, 2019): 493–508. http://dx.doi.org/10.1177/0020294019842597.

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This paper presents a new hybrid metaheuristic model in order to estimate wind speeds accurately. The study was started by the training process of artificial neural networks with some metaheuristic algorithms such as evolutionary strategy, genetic algorithm, ant colony optimization, probability-based incremental learning, particle swarm optimization, and radial movement optimization in the literature. The success of each model is recorded in graphs. In order to make the closest estimation and to increase the system stability, a new hybrid metaheuristic model was developed using particle swarm optimization and radial movement optimization, and the training process of artificial neural networks was performed with this new model. The data were obtained by real-time measurements from a 63-m-high wind measurement station built at the coordinates of UTM E 263254 and N 4173479, altitude 1313 m. Two different scenarios were created using actual data and applied to all models. It was observed that the error values in the designed new hybrid metaheuristic model were lower than those of the other models.

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50

Shangguan, Lingxiao, Yunfei Yin, Qingtao Zhang, Qun Liu, Wei Xie, and Zejiao Dong. "Icing Time Prediction Model of Pavement Based on an Improved SVR Model with Response Surface Approach." Applied Sciences 12, no. 16 (August 12, 2022): 8109. http://dx.doi.org/10.3390/app12168109.

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Pavement icing imposes a great threat to driving safety and impacts the efficiency of the road transportation system in cold regions. This has attracted research predicting pavement icing time to solve the problems brought about by icing. Different models have been proposed in the past decades to predict pavement icing, within which support vector regression (SVR) is a widely used algorithm for calibrating highly nonlinear relationships. This paper presents a hybrid improved SVR algorithm to predict the time of pavement icing with an enhancement operation by response surface method (RSM) and particle swarm optimization (PSO). RSM is used to increase the number of input data collected onsite. Based on that, the optimal SVR model is established by optimizing the kernel function parameters and penalty coefficient with the particle swarm optimization (PSO) algorithm. The hybrid improved SVR is compared with SVR, PSO-SVR, and RSM-PSO for coefficient of determination (R2), mean absolute error, mean absolute percentage error, and root mean square error to check the effectiveness of PSO and RSM in optimizing SVR. The results show that the combination of two methods in the hybrid improved algorithm has a better optimization capability with R2 of 0.9655 and 0.9318 in a train set and test set, respectively, which outperforms PSO-SVR, RSM-SVR, and SVR. In addition, the R2 of the hybrid improved SVR and PSO-SVR both reach the optimal fitness value approximately at the iteration of 20, which suggests that convergence capacity remains relatively constant with the predictive accuracy being improved.

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