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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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A hybrid optimization algorithm for multi-agent dynamic planning with guaranteed convergence in probability

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NEUROCOMPUTING 2024年 592卷

作者： Zhang, Ye Zhu, Yutong Li, Haoyu Wang, Jingyu Northwestern Polytech Univ Shenzhen Res & Dev Inst Shenzhen 518057 Peoples R China Northwestern Polytech Univ Sch Astronaut Xian 710072 Peoples R China

The paper aims to solve the problem of multi -agent path planning in complex environment using optimization algorithm. To address the issue of local optimum and premature convergence, a new method is proposed based on the whale optimization algorithm, combining the chaotic initialization, the reverse search and the differential evolution methods. It is theoretically proved that this algorithm is globally convergent in probability. When applied to path planning problems, the proposed optimization algorithm can effectively find a globally optimal and smoother path. Through simulation experiments with multi-UAVs, it is demonstrated that the proposed algorithm has better performance than the state-of-the-art methods in environment with both static and dynamic obstacles, reflecting the global convergence and robustness of the proposed algorithm.

关键词： Trajectory planning optimization algorithm Differential evolution Convergence in probability

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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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An optimization algorithm for individualized biomechanical analysis and simulation of tibia fractures

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JOURNAL OF BIOMECHANICS 2015年第6期48卷 1119-1124页

作者： Roland, M. Tjardes, T. Otchwemah, R. Bouillon, B. Diebels, S. Univ Saarland Chair Appl Mech D-66123 Saarbrucken Germany Univ Witten Herdecke Cologne Merheim Med Ctr Dept Trauma Surg Orthoped Surg & Sportstraumatol D-51109 Cologne Germany

An algorithmic strategy to determine the minimal fusion area of a tibia pseudarthrosis to achieve mechanical stability is presented. For this purpose, a workflow capable for implementation into clinical routine workup of tibia pseudarthrosis was developed using visual computing algorithms for image segmentation, that is a coarsening protocol to reduce computational effort resulting in an individualized volume-mesh based on computed tomography data. An algorithm detecting the minimal amount of fracture union necessary to allow physiological loading without subjecting the implant to stresses and strains that might result in implant failure is developed. The feasibility of the algorithm in terms of computational effort is demonstrated. Numerical finite element simulations show that the minimal fusion area of a tibia pseudarthrosis can be less than 90% of the full circumferential area given a defined maximal von Mises stress in the implant of 80% of the total stress arising in a complete pseudarthrosis of the tibia. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Patient-specific simulations Medical image segmentation Finite element method (FEM) Fractured tibia with implant optimization algorithm

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The application of hybrid cuckoo search-grey wolf optimization algorithm in optimal parameters identification of solid oxide fuel cell

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2022年第9期47卷 6200-6216页

作者： Bai, Qiuchan Li, Hao Huaiyin Inst Technol Fac Automat Huaian 223003 Peoples R China Hebei Agr Univ Coll Mech & Elect Engn Baoding 071001 Hebei Peoples R China

In the present study, dependable and accurate variables of solid oxide fuel cell (SOFC) are identified using an accurate and remarkable convergence pace optimization algorithm, socalled cuckoo search grey wolf optimization (CSGWO). The mentioned hybrid algorithm is acquired from the integration of cuckoo search (CS) and grey wolf optimization (GWO) algorithms. To confirm the CSGWO performance, it is compared with well-known optimization algorithms. The proposed CSGWO hybrid algorithm is applied for a 5-kW physically-based dynamic tubular stack. Along with this, the modeling is performed for different temperatures and pressures. The results of the CSGWO integrated algorithm reveal the supremacy of the considered method compared to other algorithms with the lowest values of MSE and corroborate precision, robustness, and excellent convergence pace compared to different optimization algorithms. The statistical results revealed that the developed CSGWO algorithm has the lowest MSE values by 1.3%, 0.1%, 0.3%, 0.1%, and 0% for the operational pressures of 1, 2, 3, 4, and 5 atm, respectively. All in all, the experimental records confirm that the CSGWO integrated algorithm could be introduced as a favorable substitute to the SOFC models' parameter estimation. (c) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Solid oxide fuel cell Unknown parameters Parameter estimation Hybrid cuckoo search-grey wolf optimization algorithm

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Performance prediction of the PEMFCs based on gate recurrent unit network optimized by improved version of prairie dog optimization algorithm

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2023年第69期48卷 26951-26963页

作者： Liu, Jie Zhang, Shubo Druzhinin, Zumrat Chongqing Creat Vocat Coll Sch Artificial Intelligence Chongqing 402160 Peoples R China Tajik Tech Univ Dushanbe Tajikistan

In this article, the black box dynamic model is presented for forecasting the performance of the PEM (Proton-exchange membrane) fuel cell (FC). An optimized deep artificial neural network has been used to build the experimental nonlinear model of the polymer membrane FC series that functions with hydrogen and oxygen. This research investigates predictability for a gate recurrent unit (GRU) optimized by a modified Prairie Dog Optimizer in PEMFCs. The results obtained have been validated by applying a case study and then a comparison is conducted among the outcomes of the offered technique and 2 other published methods: modified relevance vector machine and Lattice Gated Recurrent Unit (LGRU). The voltage clearly changes significantly, as demonstrated by simulations, even though the FC is handled with a low starting temperature and current. Also, the voltage point distribution has become more concentrated when the current and temperature are high. In both the training and prediction phases, the MAPE is reduced to approximately 0.0043 and 0.0047, respectively, showing that the proposed GRU technique produces superior prediction results when the operational settings approach the optimum operating conditions. According to simulations, the proposed IPDO/GRU with a 0.004 root mean square has the least error, followed by the mRVM and GRU with 0.009 and 0.010 root mean square values. The outcomes show that using the offered procedure does provide the finest verification of the empirical data. & COPY;2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： PEMFC Performance prediction Gate recurrent unit Improved version of prairie dog optimization algorithm

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Complex ontology alignment for autonomous systems via the Compact Co-Evolutionary Brain Storm optimization algorithm

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ISA TRANSACTIONS 2023年 132卷 190-198页

作者： Xue, Xingsi Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol Intelligent Informat Proc Res Ctr Fuzhou 350118 Fujian Peoples R China

Autonomous Systems (ASs) that work in the open, dynamic environment are required to share their data entities and semantics to implement the co-operations. Typically, AS's data schemas and semantics are described via ontology. Since ASs need to maintain their autonomy and conceptual specificity, their ontologies might define one concept with different terms or in different contexts, which yields the heterogeneity issue and hampers their co-operations. An effective solution is to establish a set of data entity's correspondences through the Ontology Alignment (OA). Sine the simple correspondence of one-to-one style lacks expressiveness and cannot completely cover different types of heterogeneity, ASs' co-operations require using the complex correspondence of one-to-many or many-to-may style. Inspired by the success of applying the Brain Storm optimization algorithm (BSO) to solve diverse complex optimization problems, this work proposes a Compact Co-Evolutionary BSO (CCBSO) to face the challenge of aligning AS ontologies. In particular, the AS ontology aligning problem is formally defined, a hybrid confidence measure for distinguishing the simple and complex correspondences is proposed, and a problem-specific CCBSO is presented. The experiment tests CCBSO's performance on different AS ontology aligning tasks, which consist of two simple ontology aligning tasks and one complex ontology aligning track. The experimental results show that CCBSO outperforms the state-of-the-art ontology aligning techniques on all simple and complex ontology aligning tasks.(c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Autonomous system Complex ontology alignment Compact Co-Evolutionary Brain Storm optimization algorithm Hybrid confidence measure

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