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Application of a Meta-heuristic optimization algorithm Motivated by a Vision Correction Procedure for Civil Engineering Problems

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KSCE JOURNAL OF CIVIL ENGINEERING 2018年第7期22卷 2623-2636页

作者： Lee, Eui Hoon Lee, Ho Min Yoo, Do Guen Kim, Joong Hoon Korea Univ Res Ctr Disaster Prevent Sci & Technol Seoul 02481 South Korea Univ Suwon Dept Civil Engn Suwon 18323 South Korea Korea Univ Sch Civil Environm & Architectural Engn Seoul 02481 South Korea

In this study, a metaheuristic optimization algorithm inspired by a vision correction procedure is applied to civil engineering problems. The Vision Correction algorithm (VCA) has the ability to solve various problems related to mathematical benchmark functions and civil engineering. Vision correction processes have three main steps: myopic/hyperopic correction, brightness adjustment/compression enforcement, and astigmatic correction. This procedure is essential for increasing the usability of glasses and obtaining high-quality vision in humans. Unlike conventional meta-heuristic algorithms, VCA automatically adjusts the global/ local search probability and global search direction based on accumulated optimization results. In VCA, all decision variables have their own search probabilities and require different processes according to whether a global search or local search is required. The proposed algorithm is applied to representative optimization problems, and the results are compared with those of existing algorithms. In civil engineering problems including design problem of water distribution network, VCA shows respectable results compared with those of existing algorithms. In all benchmark problems and civil engineering problems, VCA shows good results and it showed the applicability to other civil engineering problems.

关键词： vision correction optimization algorithm metaheuristic global search local search

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Particle Swarm optimization algorithm With Intelligent Particle Number Control for Optimal Design of Electric Machines

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2018年第2期65卷 1791-1798页

作者： Lee, Jin Hwan Song, Jun-Young Kim, Dae-Woo Kim, Jong-Wook Kim, Yong-Jae Jung, Sang-Yong Sungkyunkwan Univ Dept Elect Elect & Comp Engn Suwon 16419 South Korea Dong A Univ Dept Elect Engn Busan 49315 South Korea Chosun Univ Dept Elect Engn Gwangju 61452 South Korea

In this study, we propose a modified particle swarm optimization (PSO) algorithm, which is an improved version of the conventional PSO algorithm. To improve the performance of the conventional PSO, a novel method is applied to intelligently control the number of particles. The novel method compares the cost value of the global best (gbest) in the current iteration to that of the gbest in the previous iteration. If there is a difference between the two cost values, the proposed algorithm operates in the exploration stage, maintaining the number of particles. However, when the difference in the cost values is smaller than the tolerance values assigned by the user, the proposed algorithm operates in the exploitation stage, reducing the number of particles. In addition, the algorithm eliminates the particle that is nearest to the best particle to ensure its randomness in terms of the Euclidean distance. The proposed algorithm is validated using five numerical test functions, whose number of function calls is reduced to some extent in comparison to conventional PSO. After the algorithm is validated, it is applied to the optimal design of an interior permanent magnet synchronous motor (IPMSM), aiming at minimizing the total harmonic distortion (THD) of the back electromotive force (back EMF). Considering the performance constraint, an optimal design is attained, which reduces back EMF THD and satisfies the back EMF amplitude. Finally, we build and test an experimental model. To validate the performance of the optimal design and optimization algorithm, a no-load test is conducted. Based on the experimental result, the effectiveness of the proposed algorithm on optimal design of an electric machine is validated.

关键词： Electric machine interior permanent magnet synchronous motor (IPMSM) optimal design optimization algorithm particle swarm optimization (PSO)

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An optimization algorithm of Energy Management for HEB Based on Pontryagin's Minimum Principle

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WIRELESS PERSONAL COMMUNICATIONS 2018年第1期103卷 1011-1023页

作者： Wu, Zhen Yin, Guodong Wang, Yeqin Southeast Univ Sch Mech Engn Nanjing Jiangsu Peoples R China Huaiyin Inst Technol Fac Automat Huaian Peoples R China

Fuel economy is pursued by hybrid electric bus (HEB). However, a key challenge for a hybrid bus is to achieve near-optimality while keeping the energy management strategy (EMS) simple. In this paper, an EMS based on the Pontryagin's minimum principle (PMP) is developed and implemented in ADVISOR. The designed EMS can successfully calculate the fuel consumption of the parallel HEB under the specific driving cycle. The simulation result suggests that the fuel economy of the hybrid electric bus with PMP-based control strategy is close to the benchmarking optimal solution calculated through DP, which is much better than the fuel economy of the parallel HEB with the default control strategy in ADVISOR. To sum up, the EMS based on PMP can improve the fuel economy by adjusting the engine operating point to reduce the exhaust emissions. Simultaneously, it can also extend the battery life by maintaining the battery state of charge at an appropriate level.

关键词： Hybrid electric bus Pontryagin's minimum principle Fuel economy SOC optimization algorithm

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Tool path optimization algorithm of spatial cam flank milling based on NURBS surface

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2018年第4期40卷 1-8页

作者： Hu, Dongfang Guo, Jianwei Henan Univ Sci & Technol Sch Mechatron Engn Collaborat Innovat Ctr Machinery Equipment Adv Mf Luoyang 471003 Peoples R China

To solve the problems of approximation error for data point discretization and great fluctuation of cutter path ruled surface, a tool path optimization algorithm of spatial cam flank milling based on NURBS surface is proposed in this paper. On the basis of the expression of the theoretical cutter axis surface, a theory derivation method of isometric surface is put forward based on the expansion line of the spatial cam profile, using cutter axis surface as the working surface of the spatial cam, and the work profile can be obtained from initial design requirements of the spatial cam. According to the geometrical properties of the cutter axis surface, the data sampling grid is generated by defining the adaptive algorithm of curvature for curved surface. To get the valid data points, the discrete mesh generated on self-adaption is planned by the maximum bending degree point-selection method. Taking advantage of these data points, the NURBS ruled surface is constructed to approximate the theoretical cutter axis surface and the NURBS cutter axis ruled surface was being refactored by leastsquare method. Combined with theoretical CNC machining error model, the algorithm validity is proved by numerical calculation of the example and simulation experiment results.

关键词： Spatial cam Flank milling optimization algorithm Least-square method NURBS surface

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Development of a WWTP influent characterization method for an activated sludge model using an optimization algorithm

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MEMBRANE WATER TREATMENT 2018年第3期9卷 155-162页

作者： You, Kwangtae Kim, Jongrack Pak, Gijung Yun, Zuwhan Kim, Hyunook UnU Inc Seoul 08390 South Korea Korea Univ Dept Environm Engn Sejong 30019 South Korea Univ Seoul Dept Energy & Environm Syst Engn Seoul 02504 South Korea

Process modeling with activated sludge models (ASMs) is useful for the design and operational improvement of biological nutrient removal (BNR) processes. Effective utilization of ASMs requires the influent fraction analysis (IFA) of the wastewater treatment plant (WWTP). However, this is difficult due to the time and cost involved in the design and operation steps, thereby declining the simulation reliability. Harmony Search (HS) algorithm was utilized herein to determine the relationships between composite variables and state variables of the model IWA ASM1. Influent fraction analysis was used in estimating fractions of the state variables of the WWTP influent and its application to 9 wastewater treatment processes in South Korea. The results of influent Ss and Xs+X-BH, which are the most sensitive variables for design of activated sludge process, are estimated within the error ranges of 8.9-14.2% and 3.8-6.4%, respectively. Utilizing the chemical oxygen demand (COD) fraction analysis for influent wastewater, it was possible to predict the concentrations of treated organic matter and nitrogen in 9 full scale BNR processes with high accuracy. In addition, the results of daily influent fraction analysis (D-IFA) method were superior to those of the constant influent fraction analysis (C-IFA) method.

关键词： ASMs COD influent fraction analyzer (IFA) Harmony Search (HS) optimization algorithm WWTPs

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optimization Model and algorithm for Multi-Label Learning

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Journal of Applied Mathematics and Physics 2021年第5期9卷 969-975页

作者： Zhengyang Li Fuzhou University Fuzhou China

This paper studies a kind of urban security risk assessment model based on multi-label learning, which is transformed into the solution of linear equations through a series of transformations, and then the solution of linear equations is transformed into an optimization problem. Finally, this paper uses some classical optimization algorithms to solve these optimization problems, the convergence of the algorithm is proved, and the advantages and disadvantages of several optimization methods are compared.

关键词： Operations Research Multi-Label Learning Linear Equations Solving optimization algorithm

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Image segmentation based on equivalent three-dimensional entropy method and artificial fish swarm optimization algorithm

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OPTICAL ENGINEERING 2018年第10期57卷 1-7页

作者： Lei, Xiangxiao Ouyang, Honglin Xu, Lijuan Hunan Univ Coll Elect & Informat Engn Changsha Hunan Peoples R China Changsha Social Work Coll Sch Elect Informat Engn Changsha Hunan Peoples R China

To improve the timeliness of the three-dimensional (3-D) maximum entropy method, an image segmentation method based on equivalent 3-D entropy and artificial fish swarm optimization algorithm is proposed. An equivalent 3-D entropy method without logarithmic operation is developed, and its equivalence is proved theoretically. The optimal threshold is determined based on the artificial fish swarm optimization algorithm so as to avoid exhaustive search and improve algorithm efficiency. The experimental results demonstrate that the proposed method is more time-efficient than the traditional 3-D entropy method and the equivalent 3-D entropy method without affecting segmentation. Compared with the one-dimensional entropy method and the two-dimensional entropy method, it is obviously superior in noise immunity and detail preservation. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： image segmentation maximum entropy three-dimensional histogram optimization algorithm artificial fish-swarm algorithm

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Research and application of an innovative combined model based on a modified optimization algorithm for wind speed forecasting

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MEASUREMENT 2018年 124卷 395-412页

作者： Jiang, Ping Li, Chen Dongbei Univ Finance & Econ Sch Stat Jianshan Rd Dalian 116025 Liaoning Peoples R China

Wind energy is emerging as a promising substitute for conventional energy and plays a pivotal role in the power industry. For wind speed forecasting, many challenges have exposed due to its fluctuation and intermittence. To address these difficulties, different models have been adopted to various wind speed time series in previous studies. However, few methodologies have focused on the importance of model parameter optimization or data pre-processing, resulting in undesirable forecasting performance. In this study, an innovative combined model that combines data pre-processing, modified optimization algorithms, three neural networks and an effective deciding weight method is proposed for short-term wind speed forecasting. To improve the forecasting capacity of the combined model, a modified optimization algorithm is proposed and employed to determine the parameters of the single models. Furthermore, a deciding weight method based on multivariate statistical estimation is applied for weight optimization. Additionally, ten-minute wind speed data from a wind farm in Penglai, China, are selected for multi-step ahead forecasting. The results obtained confirmed an adequate approximation of the actual wind speed series and a significant improvement of the forecasting accuracy of the proposed model.

关键词： Wind speed forecasting optimization algorithm Multi-step ahead forecasting Combined model Weight coefficient optimization

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A Novel Social Insect optimization algorithm for the Optimal Design of an Interior Permanent Magnet Synchronous Machine

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IEEE TRANSACTIONS ON MAGNETICS 2018年第12期54卷 1-6页

作者： Kwon, Hyuk-Sung Ro, Jong-Suk Jung, Hyun-Kyo Seoul Natl Univ Dept Elect & Comp Engn Seoul 08826 South Korea Chung Ang Univ Sch Elect & Elect Engn Seoul 06974 South Korea

optimization of an electric machine is a nonlinear multi-variable problem. For optimization of the nonlinear multi-variable problem, many function evaluations are required, which in turn requires much time. To address this problem, we propose a novel optimization algorithm of which the convergence speed, accuracy, and reliability are superior compared to those of widely used conventional algorithms. The performance of the proposed algorithm is verified through mathematical test functions and applied to a practical optimization scenario of cogging torque minimization for an interior permanent magnet synchronous machine.

关键词： Electric machine interior permanent magnet synchronous machine (IPMSM) multivariable problem nature-inspired optimization algorithms optimization algorithm

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A novel Master-Slave optimization algorithm for generating an optimal release policy in case of reservoir operation

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JOURNAL OF HYDROLOGY 2019年 577卷 123959-000页

作者： Turgut, Mert Sinan Turgut, Oguz Emrah Afan, Haitham Abdulmohsin El-Shafie, Ahmed Ege Univ Fac Engn Mech Engn Dept TR-35040 Izmir Turkey Izmir Bakircay Univ Fac Engn Mech Engn Dept TR-35665 Izmir Turkey Univ Malaya Fac Engn Civil Engn Dept Kuala Lumpur 50603 Malaysia

Dams and reservoirs provide decision-makers and managers with appropriate control on the available water resources, allowing the implementation of various strategies for the most efficient usage of the available water resources. In areas where water supply exhibits significant temporal variation when compared with the demand, the challenge is to bridge the gap and achieve an optimal match between the water supply and demand patterns. Therefore, the release of water from reservoirs should be controlled to ensure that the operation rule for the available water storage in the reservoir is optimized to satisfy the future water demands. This level of optimal control can only be achieved using an efficient optimization algorithm to optimally derive the operation rule for such a complex water system. Herein, two main methods have been considered to tackle this water resource management problem. First, three different optimization algorithms, namely particle swarm optimization, differential evolution, and whale optimization algorithm, have been applied. In addition, two different optimization algorithms, namely crow search algorithm and master-slave algorithm, have been introduced to generate an optimal rule for water release policy. Further, the proposed optimization algorithms have been applied to one of the most critical dam and reservoir water systems, namely the Aswan High Dam (AHD), which controls almost 95% of Egypt's water resources. The current operation of AHD using the existing optimization rules resulted in a mismatch between the water supply and water demand. In other words, the water availability could be higher than the water demand during a certain period, whereas it could be less than the water demand during another period. The results denoted that the master-slave algorithm outperforms the remaining algorithms and generates an optimization rule that minimizes the mismatch between the water supply and water demand.

关键词： Dam and reservoir operation optimization algorithm Aswan High Dam Crow search algorithm Master-slave algorithm

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