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Identification of Multiple Fire Sources in the Utility Tunnel Based on a Constrained Particle Swarm optimization algorithm

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FIRE TECHNOLOGY 2022年第5期58卷 2825-2845页

作者： Liu, Xiaojiang Sun, Bin Xu, Zhao-Dong Liu, Xuanya Xu, Dajun Southeast Univ Joint Lab Smart Disaster Prevent Major Infrastruc China Pakistan Belt & Rd Nanjing 210096 Peoples R China Minist Emergency Management Tianjin Fire Res Inst Tianjin 300381 Peoples R China

Fire disaster is one of the most dangerous disasters in the utility tunnel with plenty of high-voltage and communication cables. Fire source identification is an important part of fire protection in utility tunnel fires. The particle swarm optimization (PSO) algorithm based on limited temperature observations was applied in the multiple fire sources identification problem, and a constrained PSO algorithm is developed for performance improvement. The fire characteristics could be estimated simultaneously, including the fire source location, the maximum temperature value, and the attenuation coefficient. Based on these parameters, the whole temperature distribution of the tunnel could be predicted correspondingly. The feasibility, superiority, and robustness of the proposed algorithm were demonstrated in numerical and experimental scenarios. Results showed that the proposed constrained algorithm could identify the double fire sources with high accuracy, and the identified locations were gathered around the actual ones in comparison with the basic algorithm. The fire source locations and fire states could be estimated under noisy and disturbance situations within an acceptance error. When the measurement noises varied from 0.02 to 0.10, the temperature prediction error of each measurement point changed from [0.1 degrees C, 5.4 degrees C] to [7.3 degrees C, 36.8 degrees C]. Additionally, the closer the distance between fire source and sensors is, and the more sensors allocated, the higher the prediction accuracy is.

关键词： Utility tunnel Fire disaster optimization algorithm Multiple fire sources

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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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Parameter identification of electronic throttle using a hybrid optimization algorithm

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NONLINEAR DYNAMICS 2011年第4期63卷 549-557页

作者： Yuan, Xiaofang Li, Shutao Wang, Yaonan Sun, Wei Wu, Lianghong Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China

Aiming at the problems in parameter identification of an electronic throttle, this paper proposes a novel hybrid optimization algorithm to search the optimal parameter values of the plant. The parameter identification of an electronic throttle is considered as an optimization process with an objective function minimizing the errors between the measurement and identification, and the optimal parameter values of the plant are searched by using a hybrid optimization algorithm. The proposed hybrid optimization algorithm, effective combination of parallel chaos optimization algorithm (PCOA) and simplex search method, preserves both the global optimization capability of PCOA and the accurate search ability of simplex search method. Simulation and experiment results have shown the good performance of the proposed approach.

关键词： Parameter identification optimization algorithm Electronic throttle Chaos optimization algorithm (COA) Simplex search method

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Parameters determination of the modified J-A model with an optimization algorithm

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INTERNATIONAL JOURNAL OF APPLIED ELECTROMAGNETICS AND MECHANICS 2013年第3期41卷 259-266页

作者： Cong, Haoxi Zou, Liang Zhang, Li Li, Qingmin Shandong Univ Sch Elect Engn Jinan 250061 Shandong Peoples R China North China Elect Power Univ Sch Elect & Elect Engn Beijing Peoples R China

J-A model is widely used in hysteresis modeling as well as performance simulation of the magnetic materials. To achieve preferable adequacy, a modified J-A model is given, however, accurate solution of the parameters is very important for the modified J-A model, especially for over-determined nonlinear equations. In response to solving the over-determined nonlinear equations, this paper first turned the problem of the over-determined nonlinear equations into solving the minimum value of a multivariate function by means of the least square method. While the multivariate function is of high nonlinearity (the function is not continuous and the matrix of the partial derivatives is singular), solution methods using derivative calculation were abandoned, and the direct seeking methods with no derivative calculations (simplex algorithm) were involved to solve the problem. In the end the solution was validated with the use of the genetic algorithm and the simulated annealing algorithm.

关键词： Modified J-A model overdetermined nonlinear equations least square method optimization algorithm simulation

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Defect of Archimedes optimization algorithm and its verification

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SOFT COMPUTING 2023年第2期27卷 701-722页

作者： Ding, Guiyan Wang, Wentao Liu, Hao Tu, Liangping Univ Sci & Technol Liaoning Anshan Peoples R China

Archimedes optimization algorithm (AOA) is a new meta-heuristic algorithm which is based on Archimedes principle and mimics the buoyancy force received by an object in water. The AOA is designed according to physical principles and has been the object of many scholars' research because of its simple and reliable performance. In the course of the study, this paper finds that the AOA is flawed. In the iterative update of the algorithm, the buoyancy principle applied to the object is not completely followed. Through the investigation and analysis of this problem, it is found that the algorithm design which follows the buoyancy principle completely is more advantageously and persuasively, and named the corrected algorithm CAOA. The performance of the CAOA and other comparison optimization algorithms is tested in benchmark functions CEC2017 under equal conditions to verify the ideas proposed in this paper. In the solution accuracy with dimensions of 30 and 50, the comprehensive score of the CAOA is 31 and 33 and ranks first in all algorithms. In the statistical analysis, the CAOA compared with other algorithms one by one, and achieved the best results in all test functions. When compared with other algorithms, the CAOA ranked first. It is hoped that the verification of the ideas in this paper will help the AOA to develop better and optimize the development of the algorithm.

关键词： Meta-heuristic Archimedes optimization algorithm optimization algorithm Defect New verification method

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A discrete Teaching-Learning-Based optimization algorithm to solve distribution system reconfiguration in presence of distributed generation

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2016年 82卷 264-273页

作者： Lotfipour, Arash Afrakhte, Hossein Univ Guilan Fac Engn Dept Elect Engn Rasht Iran

Reconfiguration is an important way to increase the power distribution systems efficiency. The problem of reconfiguration is a complicated combinatorial optimization problem with discrete decision variables. To solve such problem, a powerful optimization technique is required. This paper presents a discrete Teaching-Learning-Based optimization (DTLBO) algorithm for solving the distribution system reconfiguration (DSR) problem. The objectives are power loss minimization and voltage profile improvement in presence of distributed generation (DG). The proposed method is applied to 33-bus and 69-bus test systems and a part of the distribution network of Ahvaz, a city in the south of Iran. A comparison between the proposed algorithm and other existing methods shows the effectiveness and capability of the proposed method to reach the global optimum and rapid convergence to the optimal solution. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Discrete Teaching-Learning-Based optimization algorithm Distribution system Distributed generation Reconfiguration

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Deep Reinforcement Learning Based optimization algorithm for Permutation Flow-Shop Scheduling

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2023年第4期7卷 983-994页

作者： Pan, Zixiao Wang, Ling Wang, Jingjing Lu, Jiawen Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Huawei Noahs Ark Lab Shenzhen 518129 Peoples R China

As a new analogy paradigm of human learning process, reinforcement learning (RL) has become an emerging topic in computational intelligence (CI). The synergy between the RL and CI is an emerging way to develop efficient solution algorithms for solving complex combinatorial optimization (CO) problems like machine scheduling problem. In this paper, we proposed an efficient optimization algorithm based on Deep RL for solving permutation flow-shop scheduling problem (PFSP) to minimize the maximum completion time. Firstly, a new deep neural network (PFSPNet) is designed for the PFSP to achieve the end-to-end output without limitation of problem sizes. Secondly, an actor-critic method of RL is used to train the PFSPNet without depending on the collection of high-quality labelled data. Thirdly, an improvement strategy is designed to refine the solution provided by the PFSPNet. Simulation results and statistical comparison show that the proposed optimization algorithm based on deep RL can obtain better results than the existing heuristics in similar computational time for solving the PFSP.

关键词： optimization Job shop scheduling Decoding Reinforcement learning Dynamic scheduling Encoding Computational intelligence Reinforcement learning deep neural network flow-shop scheduling optimization algorithm improvement strategy

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A novel focused crawler based on cell-like membrane computing optimization algorithm

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NEUROCOMPUTING 2014年 123卷 266-280页

作者： Liu, WenJun Du, YaJun Xihua Univ Sch Math & Comp Engn Chengdu 610039 Peoples R China

In many research works, topical priorities of unvisited hyperlinks are computed based on linearly integrating topic-relevant similarities of various texts and corresponding weighted factors. However, these weighted factors are determined based on the personal experience, so that these values may make topical priorities of unvisited hyperlinks serious deviations directly. To solve this problem, this paper proposes a novel focused crawler applying the cell-like membrane computing optimization algorithm (CMCFC). The CMCFC regards all weighted factors corresponding to contribution degrees of similarities of various texts as one object, and utilizes evolution regulars and communication regulars in membranes to achieve the optimal object corresponding to the optimal weighted factors, which make the root measure square error (RMS) of priorities of hyperlinks achieve the minimum. Then, it linearly integrates optimal weighted factors and corresponding topical similarities of various texts, which are computed by using a Vector Space Model (VSM), to compute priorities of unvisited hyperlinks. The CMCFC obtains more accurate unvisited URLs' priorities to guide crawlers to collect higher quality web pages. The experimental results indicate that the proposed method improves the performance of focused crawlers by intelligently determining weighted factors. In conclusion, the mentioned approach is effective and significant for focused crawlers. (c) 2013 Elsevier B.V. All rights reserved.

关键词： Focused crawler Membrane computing optimization algorithm VSM

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AdaptAhead optimization algorithm for Learning Deep CNN Applied to MRI Segmentation

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JOURNAL OF DIGITAL IMAGING 2019年第1期32卷 105-115页

作者： Hoseini, Farnaz Shahbahrami, Asadollah Bayat, Peyman Islamic Azad Univ Dept Comp Engn Rasht Branch Rasht Iran Univ Guilan Dept Comp Engn Fac Engn Rasht Iran

Deep learning is one of the subsets of machine learning that is widely used in artificial intelligence (AI) field such as natural language processing and machine vision. The deep convolution neural network (DCNN) extracts high-level concepts from low-level features and it is appropriate for large volumes of data. In fact, in deep learning, the high-level concepts are defined by low-level features. Previously, in optimization algorithms, the accuracy achieved for network training was less and high-cost function. In this regard, in this study, AdaptAhead optimization algorithm was developed for learning DCNN with robust architecture in relation to the high volume data. The proposed optimization algorithm was validated in multi-modality MR images of BRATS 2015 and BRATS 2016 data sets. Comparison of the proposed optimization algorithm with other commonly used methods represents the improvement of the performance of the proposed optimization algorithm on the relatively large dataset. Using the Dice similarity metric, we report accuracy results on the BRATS 2015 and BRATS 2016 brain tumor segmentation challenge dataset. Results showed that our proposed algorithm is significantly more accurate than other methods as a result of its deep and hierarchical extraction.

关键词： Deep learning Convolutional neural networks MRI segmentation Deep convolutional neural networks optimization algorithm

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AFOX: a new adaptive nature-inspired optimization algorithm

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第12期56卷 15523-15566页

作者： ALRahhal, Hosam Jamous, Razan Univ Regina Fac Engn & Appl Sci Regina SK S4S 0A2 Canada Nahda Univ Fac Engn Bani Suwayf 62764 Egypt

optimization is a common phenomenon that we encounter in our daily routine, which involves selecting the best option from a set of alternatives. A lot of algorithms have been developed, including metaheuristics algorithms, which aim to find solutions close to optimal to solve optimization problems. Many metaheuristic algorithms have been inspired by the behavior of natural phenomena, animals, and biological sciences. This paper proposes a novel nature-based metaheuristic optimization algorithm called Adaptive Fox optimization (AFOX) algorithm, which is inspired by the hunting behavior of foxes. The proposed algorithm enhances the FOX algorithm by balancing the exploration and exploitation phases, speeding up convergence to the global solution, and avoiding local optima. The efficacy of the AFOX algorithm was tested on eight classical benchmark functions, the functions of CEC2018, and the functions of the CEC2019 Benchmarks. Moreover, AFOX was applied to solve real-world optimization problems, such as prediction and engineering design problems, and compared with a wide range of metaheuristic algorithms such as variant versions of FOX, the Dragon-Fly algorithm, particle swarm optimization, Fitness Dependent Optimizer, Grey Wolf optimization, Whale optimization algorithm, Chimp optimization algorithm, Butterfly optimization algorithm, and Genetic algorithm. The results demonstrate the effectiveness of the AFOX algorithm in finding optimal solutions with higher accuracy and faster convergence. Thus, the AFOX algorithm is deemed to be highly efficient in solving real-world optimization problems with accuracy and speed.

关键词： FOX optimization algorithm Metaheuristics Nature-inspired COVID-19 Engineering design problems

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