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| 1 | A Multi-Layered Gravitational Search Algorithm for Function Optimization and Real-World Problems显示文摘A gravitational search algorithm(GSA)uses gravitational force among individuals to evolve population.Though GSA is an effective population-based algorithm,it exhibits low search performance and premature convergence.To ameliorate these issues,this work proposes a multi-layered GSA called MLGSA.Inspired by the two-layered structure of GSA,four layers consisting of population,iteration-best,personal-best and global-best layers are constructed.Hierarchical interactions among four layers are dynamically implemented in different search stages to greatly improve both exploration and exploitation abilities of population.Performance comparison between MLGSA and nine existing GSA variants on twenty-nine CEC2017 test functions with low,medium and high dimensions demonstrates that MLGSA is the most competitive one.It is also compared with four particle swarm optimization variants to verify its excellent performance.Moreover,the analysis of hierarchical interactions is discussed to illustrate the influence of a complete hierarchy on its performance.The relationship between its population diversity and fitness diversity is analyzed to clarify its search performance.Its computational complexity is given to show its efficiency.Finally,it is applied to twenty-two CEC2011 real-world optimization problems to show its practicality. | Yirui Wang Shangce Gao Mengchu Zhou Yang Yu | 2021 | IEEE/CAA Journal of Automatica Sinica2021，8，1： | 5 |
| 2 | Global Optimum-Based Search Differential Evolution显示文摘In this paper, a global optimum-based search strategy is proposed to alleviate the situation that the differential evolution(DE) usually sticks into a stagnation, especially on complex problems. It aims to reconstruct the balance between exploration and exploitation, and improve the search efficiency and solution quality of DE. The proposed method is activated by recording the number of recently consecutive unsuccessful global optimum updates. It takes the feedback from the global optimum,which makes the search strategy not only refine the current solution quality, but also have a change to find other promising space with better individuals. This search strategy is incorporated with various DE mutation strategies and DE variations. The experimental results indicate that the proposed method has remarkable performance in enhancing search efficiency and improving solution quality. | Yang Yu Shangce Gao Yirui Wang Yuki Todo | 2019 | IEEE/CAA Journal of Automatica Sinica2019，6，2： | 5 |
| 3 | Complex-Valued Neural Networks:A Comprehensive Survey显示文摘Complex-valued neural networks(CVNNs)have shown their excellent efficiency compared to their real counterparts in speech enhancement,image and signal processing.Researchers throughout the years have made many efforts to improve the learning algorithms and activation functions of CVNNs.Since CVNNs have proven to have better performance in handling the naturally complex-valued data and signals,this area of study will grow and expect the arrival of some effective improvements in the future.Therefore,there exists an obvious reason to provide a comprehensive survey paper that systematically collects and categorizes the advancement of CVNNs.In this paper,we discuss and summarize the recent advances based on their learning algorithms,activation functions,which is the most challenging part of building a CVNN,and applications.Besides,we outline the structure and applications of complex-valued convolutional,residual and recurrent neural networks.Finally,we also present some challenges and future research directions to facilitate the exploration of the ability of CVNNs. | ChiYan Lee Hideyuki Hasegawa Shangce Gao | 2022 | IEEE/CAA Journal of Automatica Sinica2022，9，8： | 2 |
| 4 | Solving Multitrip Pickup and Delivery Problem With Time Windows and Manpower Planning Using Multiobjective Algorithms显示文摘The multitrip pickup and delivery problem with time windows and manpower planning(MTPDPTW-MP)determines a set of ambulance routes and finds staff assignment for a hospital. It involves different stakeholders with diverse interests and objectives. This study firstly introduces a multiobjective MTPDPTW-MP(MO-MTPDPTWMP) with three objectives to better describe the real-world scenario. A multiobjective iterated local search algorithm with adaptive neighborhood selection(MOILS-ANS) is proposed to solve the problem. MOILS-ANS can generate a diverse set of alternative solutions for decision makers to meet their requirements. To better explore the search space, problem-specific neighborhood structures and an adaptive neighborhood selection strategy are carefully designed in MOILS-ANS. Experimental results show that the proposed MOILS-ANS significantly outperforms the other two multiobjective algorithms. Besides, the nature of objective functions and the properties of the problem are analyzed. Finally, the proposed MOILS-ANS is compared with the previous single-objective algorithm and the benefits of multiobjective optimization are discussed. | Jiahai Wang Yuyan Sun Zizhen Zhang Shangce Gao | 2020 | IEEE/CAA Journal of Automatica Sinica2020，7，4： | 2 |
| 5 | Effects of “rich-gets-richer” rule on small-world networks显示文摘 | Hongwei Dai Shangce Gao Yu Yang Zheng Tang | 2010 | Neurocomputing2010，，10： | 1 |
| 6 | Improving Dendritic Neuron Model With Dynamic Scale-Free Network-Based Differential Evolution显示文摘Some recent research reports that a dendritic neuron model(DNM)can achieve better performance than traditional artificial neuron networks(ANNs)on classification,prediction,and other problems when its parameters are well-tuned by a learning algorithm.However,the back-propagation algorithm(BP),as a mostly used learning algorithm,intrinsically suffers from defects of slow convergence and easily dropping into local minima.Therefore,more and more research adopts non-BP learning algorithms to train ANNs.In this paper,a dynamic scale-free network-based differential evolution(DSNDE)is developed by considering the demands of convergent speed and the ability to jump out of local minima.The performance of a DSNDE trained DNM is tested on 14 benchmark datasets and a photovoltaic power forecasting problem.Nine meta-heuristic algorithms are applied into comparison,including the champion of the 2017 IEEE Congress on Evolutionary Computation(CEC2017)benchmark competition effective butterfly optimizer with covariance matrix adapted retreat phase(EBOwithCMAR).The experimental results reveal that DSNDE achieves better performance than its peers. | Yang Yu Zhenyu Lei Yirui Wang Tengfei Zhang Chen Peng Shangce Gao | 2022 | IEEE/CAA Journal of Automatica Sinica2022，9，1： | 1 |
| 7 | The origins of cardiac tissue in the amphibian,Xenopus laevis显示文摘 | MohunT OrfordR ShangC | 2003 | Trends Cardiovasc Med2003，13，6： | 1 |
| 8 | Study on the standardsystem of the application of information technology in China'sconstruction industry 显示文摘 | ShangC Wang Y Liu H | 2004 | Automation in construction2004，13，5： | 1 |
| 9 | Facilitatingefficientmarsterrainimageclassificationwithfuzzy-roughfeatureselection显示文摘 | ShangC BarnesD ShenQ | 2011 | InternationalJournalofHybridIntelligentSystems2011，8，1： | 1 |
| 10 | Study on the standardsystem of the application of information technology in China'sconstruction industry 显示文摘 | ShangC Wang Y Liu H | 2004 | Automation in construction2004，13，5： | 1 |
| 11 | A novel clonal selection algorithm and its application to traveling salesman problems显示文摘 | GAO Shangce DAI Hongwei YANG Gang | 2007 | IEICE Transaction on Fundamentals of Electronics Communications and Computer Sciences2007，90，10： | 1 |
| 12 | StudyontheacousticGabsorptionmechanismofanechoictilewithmultipleGscatGteringtheory显示文摘 | ShangC WeiYJ ZhangJZ etal | 2013 | JournalofShipMechanics2013，17，5： | 1 |
| 13 | Improved pattern recognition with complex artificial immune system 显示文摘 | Wang Wei Gao Shangce Tang Zheng | 2009 | Soft Compute2009，，13： | 1 |
| 14 | A novel clonal selection algorithm and its application to traveling salesman problem显示文摘 | SHANGCE GAO HONGWEI DAI GANG YANG | | 0，，10： | 1 |
| 15 | Ant Colony Optimization with Clustering for Solving the Dynamic Location Routing Problem显示文摘 | GAO SHANGCE WANG YIRUI CHENG JIUJUN | 2016 | Applied Mathemamatics and Computation2016，，285： | 1 |
| 16 | Dendritic Deep Learning for Medical Segmentation显示文摘Dear Editor,This letter presents a novel segmentation approach that leverages dendritic neurons to tackle the challenges of medical imaging segmentation.In this study,we enhance the segmentation accuracy based on a SegNet variant including an encoder-decoder structure,an upsampling index,and a deep supervision method.Furthermore,we introduce a dendritic neuron-based convolutional block to enable nonlinear feature mapping,thereby further improving the effectiveness of our approach. | Zhipeng Liu Zhiming Zhang Zhenyu Lei Masaaki Omura Rong-Long Wang Shangce Gao | 2024 | IEEE/CAA Journal of Automatica Sinica2024，11，3： | 0 |
| 17 | Multiple Elite Individual Guided Piecewise Search-Based Differential Evolution显示文摘The differential evolution(DE)algorithm relies mainly on mutation strategy and control parameters'selection.To take full advantage of top elite individuals in terms of fitness and success rates,a new mutation operator is proposed.The control parameters such as scale factor and crossover rate are tuned based on their success rates recorded over past evolutionary stages.The proposed DE variant,MIDE,performs the evolution in a piecewise manner,i.e.,after every predefined evolutionary stages,MIDE adjusts its settings to enrich its diversity skills.The performance of the MIDE is validated on two different sets of benchmarks:CEC 2014 and CEC 2017(special sessions&competitions on real-parameter single objective optimization)using different performance measures.In the end,MIDE is also applied to solve constrained engineering problems.The efficiency and effectiveness of the MIDE are further confirmed by a set of experiments. | Shubham Gupta Shitu Singh Rong Su Shangce Gao Jagdish Chand Bansal | 2023 | IEEE/CAA Journal of Automatica Sinica2023，10，1： | 0 |
| 18 | Dendritic Learning-Incorporated Vision Transformer for Image Recognition显示文摘Dear Editor,This letter proposes to integrate dendritic learnable network architecture with Vision Transformer to improve the accuracy of image recognition.In this study,based on the theory of dendritic neurons in neuroscience,we design a network that is more practical for engineering to classify visual features.Based on this,we propose a dendritic learning-incorporated vision Transformer(DVT),which out-performs other state-of-the-art methods on three image recognition benchmarks. | Zhiming Zhang Zhenyu Lei Masaaki Omura Hideyuki Hasegawa Shangce Gao | 2024 | IEEE/CAA Journal of Automatica Sinica2024，11，2： | 0 |
| 19 | A Chaotic Local Search-Based Particle Swarm Optimizer for Large-Scale Complex Wind Farm Layout Optimization显示文摘Wind energy has been widely applied in power generation to alleviate climate problems.The wind turbine layout of a wind farm is a primary factor of impacting power conversion efficiency due to the wake effect that reduces the power outputs of wind turbines located in downstream.Wind farm layout optimization(WFLO)aims to reduce the wake effect for maximizing the power outputs of the wind farm.Nevertheless,the wake effect among wind turbines increases significantly as the number of wind turbines increases in the wind farm,which severely affect power conversion efficiency.Conventional heuristic algorithms suffer from issues of low solution quality and local optimum for large-scale WFLO under complex wind scenarios.Thus,a chaotic local search-based genetic learning particle swarm optimizer(CGPSO)is proposed to optimize large-scale WFLO problems.CGPSO is tested on four larger-scale wind farms under four complex wind scenarios and compares with eight state-of-the-art algorithms.The experiment results indicate that CGPSO significantly outperforms its competitors in terms of performance,stability,and robustness.To be specific,a success and failure memories-based selection is proposed to choose a chaotic map for chaotic search local.It improves the solution quality.The parameter and search pattern of chaotic local search are also analyzed for WFLO problems. | Zhenyu Lei Shangce Gao Zhiming Zhang Haichuan Yang Haotian Li | 2023 | IEEE/CAA Journal of Automatica Sinica2023，10，5： | 0 |
| 20 | An Information-Based Elite-Guided Evolutionary Algorithm for Multi-Objective Feature Selection显示文摘Dear Editor, This letter is concerned with the evolution strategy for addressing multi-objective feature selection problems in classification. Previous methods suffer from limitations such as being trapped in local optima and lacking stability. To overcome them, we propose a novel eliteguided mechanism based on information theory. Firstly, an elite solution is generated through a dimension reduction strategy and incorporated to the initialization population. | Ziqian Wang Shangce Gao Zhenyu Lei Masaaki Omura | 2024 | IEEE/CAA Journal of Automatica Sinica2024，11，1： | 0 |
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