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A Multi-Layered Gravitational Search Algorithm for Function Optimization and Real-World Problems

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IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 94-109页

作者： Yirui Wang shangce gao Mengchu Zhou Yang Yu the Faculty of Engineering University of ToyamaToyama 930-8555Japan the Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

A gravitational search algorithm(GSA)uses gravitational force among individuals to evolve *** GSA is an effective population-based algorithm,it exhibits low search performance and premature *** ameliorate these issues,this work proposes a multi-layered GSA called *** by the two-layered structure of GSA,four layers consisting of population,iteration-best,personal-best and global-best layers are *** interactions among four layers are dynamically implemented in different search stages to greatly improve both exploration and exploitation abilities of *** 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 *** is also compared with four particle swarm optimization variants to verify its excellent ***,the analysis of hierarchical interactions is discussed to illustrate the influence of a complete hierarchy on its *** relationship between its population diversity and fitness diversity is analyzed to clarify its search *** computational complexity is given to show its ***,it is applied to twenty-two CEC2011 real-world optimization problems to show its practicality.

关键词： Artificial intelligence exploration and exploitation gravitational search algorithm hierarchical interaction hierarchy machine learning population structure

Global Optimum-Based Search Differential Evolution

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IEEE/CAA Journal of Automatica Sinica 2019年第2期6卷 379-394页

作者： Yang Yu shangce gao Yirui Wang Yuki Todo the Faculty of Engineering University of Toyama the Faculty of Electrical and Computer Engineering Kanazawa University

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.

关键词： Differential evolution(DE) global optimum memetic algorithm

Solving Multitrip Pickup and Delivery Problem With Time Windows and Manpower Planning Using Multiobjective Algorithms

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IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 1134-1153页

作者： Jiahai Wang Yuyan Sun Zizhen Zhang shangce gao the Department of Computer Science and also with the Key Laboratory of Machine Intelligence and Advanced ComputingMinistry of Educationand also with the Guangdong Key Laboratory of Big Data Analysis and ProcessingSun Yat-sen UniversityGuangzhou 510275China the Department of Computer Science Sun Yat-sen UniversityGuangzhou 510275China the Faculty of Engineering University of ToyamaToyama-shi 930-8555Japan

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.

关键词： Adaptive neighborhood selection manpower planning multiobjective optimization multitrip pickup and delivery problem with time windows

Dendritic Learning and Miss Region Detection-Based Deep Network for Multi-scale Medical Segmentation

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Journal of Bionic Engineering 2024年第4期21卷 2073-2085页

作者： Lin Zhong Zhipeng Liu Houtian He Zhenyu Lei shangce gao Faculty of Engineering University of ToyamaToyama9300887Japan

Automatic identification and segmentation of lesions in medical images has become a focus area for *** for medical image provides professionals with a clearer and more detailed view by accurately identifying and isolating specific tissues,organs,or lesions from complex medical images,which is crucial for early diagnosis of diseases,treatment planning,and efficacy *** paper introduces a deep network based on dendritic learning and missing region detection(DMNet),a new approach to medical image *** combines a dendritic neuron model(DNM)with an improved SegNet framework to improve segmentation accuracy,especially in challenging tasks such as breast lesion and COVID-19 CT scan *** work provides a new approach to medical image segmentation and confirms its *** have demonstrated that DMNet outperforms classic and latest methods in various performance metrics,proving its effectiveness and stability in medical image segmentation tasks.

关键词： Medical image segmentation Dendritic learning Deep supervision Dynamic focal loss

Multiple Elite Individual Guided Piecewise Search-Based Differential Evolution

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 135-158页

作者： Shubham Gupta Shitu Singh Rong Su shangce gao Jagdish Chand Bansal the School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore the Department of Mathematics South Asian UniversityNew Delhi 110021India IEEE Faculty of Engineering University of ToyamaToyama-shi 930-8555Japan

The differential evolution(DE)algorithm relies mainly on mutation strategy and control parameters'*** take full advantage of top elite individuals in terms of fitness and success rates,a new mutation operator is *** control parameters such as scale factor and crossover rate are tuned based on their success rates recorded over past evolutionary *** 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 *** 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 *** the end,MIDE is also applied to solve constrained engineering *** efficiency and effectiveness of the MIDE are further confirmed by a set of experiments.

关键词： Control parameters differential Evolution metaheuristic algorithms mutation operator

An Adaptive Strategy-incorporated Integer Genetic Algorithm for Wind Farm Layout Optimization

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Journal of Bionic Engineering 2024年第3期21卷 1522-1540页

作者： Tao Zheng Haotian Li Houtian He Zhenyu Lei shangce gao Faculty of Engineering University of ToyamaToyama9300887Japan

Energy issues have always been one of the most significant concerns for scientists *** the ongoing over exploitation and continued outbreaks of wars,traditional energy sources face the threat of *** energy is a readily available and sustainable energy *** farm layout optimization problem,through scientifically arranging wind turbines,significantly enhances the efficiency of harnessing wind ***-heuristic algorithms have been widely employed in wind farm layout *** paper introduces an Adaptive strategy-incorporated Integer Genetic Algorithm,referred to as AIGA,for optimizing wind farm layout *** adaptive strategy dynamically adjusts the placement of wind turbines,leading to a substantial improvement in energy utilization efficiency within the wind *** this study,AIGA is tested in four different wind conditions,alongside four other classical algorithms,to assess their energy conversion efficiency within the wind *** results demonstrate a notable advantage of AIGA.

关键词： Wind farm layout optimization problem Meta-heuristic algorithms Adaptive Integer genetic algorithm

A Chaotic Local Search-Based Particle Swarm Optimizer for Large-Scale Complex Wind Farm Layout Optimization

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IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1168-1180页

作者： Zhenyu Lei shangce gao Zhiming Zhang Haichuan Yang Haotian Li The authors are with the Faculty of Engineering University of ToyamaToyama 930-8555Japan

Wind energy has been widely applied in power generation to alleviate climate *** 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 *** farm layout optimization(WFLO)aims to reduce the wake effect for maximizing the power outputs of the wind ***,the wake effect among wind turbines increases significantly as the number of wind turbines increases in the wind farm,which severely affect power conversion *** heuristic algorithms suffer from issues of low solution quality and local optimum for large-scale WFLO under complex wind ***,a chaotic local search-based genetic learning particle swarm optimizer(CGPSO)is proposed to optimize large-scale WFLO *** is tested on four larger-scale wind farms under four complex wind scenarios and compares with eight state-of-the-art *** experiment results indicate that CGPSO significantly outperforms its competitors in terms of performance,stability,and *** be specific,a success and failure memories-based selection is proposed to choose a chaotic map for chaotic search *** improves the solution *** parameter and search pattern of chaotic local search are also analyzed for WFLO problems.

关键词： Chaotic local search(CLS) evolutionary computation genetic learning particle swarm optimization(PSO) wake effect wind farm layout optimization(WFLO)

Mapping Network-Coordinated Stacked Gated Recurrent Units for Turbulence Prediction

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1331-1341页

作者： Zhiming Zhang shangce gao MengChu Zhou Mengtao Yan Shuyang Cao Faculty of Engineering University of ToyamaToyama 930-8555Japan Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA gaoling School of Artificial Intelligence Renmin University of ChinaBeijing 100872China College of Civil Engineering Tongji UniversityShanghai 200092China IEEE

Accurately predicting fluid forces acting on the sur-face of a structure is crucial in engineering ***,this task becomes particularly challenging in turbulent flow,due to the complex and irregular changes in the flow *** this study,we propose a novel deep learning method,named mapping net-work-coordinated stacked gated recurrent units(MSU),for pre-dicting pressure on a circular cylinder from velocity ***-cally,our coordinated learning strategy is designed to extract the most critical velocity point for prediction,a process that has not been explored *** our experiments,MSU extracts one point from a velocity field containing 121 points and utilizes this point to accurately predict 100 pressure points on the *** method significantly reduces the workload of data measure-ment in practical engineering *** experimental results demonstrate that MSU predictions are highly similar to the real turbulent data in both spatio-temporal and individual ***,the comparison results show that MSU predicts more precise results,even outperforming models that use all velocity field *** with state-of-the-art methods,MSU has an average improvement of more than 45%in various indicators such as root mean square error(RMSE).Through comprehensive and authoritative physical verification,we estab-lished that MSU’s prediction results closely align with pressure field data obtained in real turbulence *** confirmation underscores the considerable potential of MSU for practical applications in real engineering *** code is available at https://***/zhangzm0128/MSU.

关键词： Convolutional neural network deep learning recurrent neural network turbulence prediction wind load predic-tion.

An Information-Based Elite-Guided Evolutionary Algorithm for Multi-Objective Feature Selection

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 264-266页

作者： Ziqian Wang shangce gao Zhenyu Lei Masaaki Omura the Faculty of Engineering University of ToyamaToyama-shi 930-8555Japan IEEE

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.

关键词： stability theory overcome