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The Realization of the Sample Learning Based on differential evolution algorithm

2nd International Conference on Energy, Environment and Sustainable Development (EESD 2012)

作者： Tang, Xiao Wu, Zhijian Guo, Lejiang Wuhan Univ State Key Lab Software Engn Wuhan 430072 Peoples R China

ISBN: (纸本)9783037855522

differential evolution algorithm is a kind of heuristic random search algorithm, and the traditional sample learning is to find a inductive assertion including all positive examples but not all counter-examples in the example space. But this process is endless and cumbersome because of the large number of the samples. The merit of difference evolution algorithm is searching in the community. So this paper using this merit to combine with sample learning then promoting efficiency.

关键词： differential evolution algorithm sample learning example space

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Designing safety-oriented tourist routes for heterogeneous tourist groups using an artificial multi-intelligence system

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JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2023年第7期40卷 589-609页

作者： Pitakaso, Rapeepan Nanthasamroeng, Natthapong Gonwirat, Sarayut Khonjun, Surajet Kaewta, Chutchai Srichok, Thanatkit Dinkoksung, Sairoong Ubon Ratchathani Univ Dept Ind Engn Ubon Ratchathani Thailand Ubon Ratchathani Rajabhat Univ Dept Engn Technol Ubon Ratchathani Thailand Kalasin Univ Dept Comp Engn & Automat Kalasin Thailand Ubon Ratchathani Rajabhat Univ Dept Comp Sci Ubon Ratchathani Thailand Ubon Ratchathani Univ Dept Hotel Management Ubon Ratchathani Thailand

The prior research on tourist route design for heterogeneous groups primarily focused on total tourist preferences and individual fairness. In this study, we present an innovative approach by incorporating safety criteria into the optimal tourist route, an aspect that has been previously overlooked. Ensuring tourist safety during travel and attractions is of utmost importance for creating a secure and enjoyable experience. Our proposed safety-focused multi-objective tourist trip design, coupled with the modified artificial multiple-intelligence system, underwent testing in a case study involving 50 attractions and 44 tourist groups (comprising a total of 140 members). The computational results demonstrate that our approach outperforms existing methods by 16.16% to 36.26%, effectively reducing total traveling and attraction visiting risk by 26.1% compared to conventional techniques proposed in the literature. Moreover, our approach enhances the total preferences and tourist fairness by 5.83% and 25.57%, respectively.

关键词： Artificial multiple intelligence system (AMIS) tourist trip design problem tourist recommendation system differential evolution algorithm genetic algorithm >

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Radial Basis Function Neural Network in Vibration Control of Civil Engineering Structure

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年第1期8卷 347-356页

作者： Lu, Jing Chen, Qinyuan Mohamed, Hamdy Anhui Open Univ Sch Informat & Architectural Engn Hefei 230022 Peoples R China Appl Sci Univ Coll Engn Eker Bahrain

This article uses the radial basis function artificial neural network and the MATLAB toolbox to study the vibration control of civil engineering structures. The article proposes a dynamic structure design method based on a generalized radial basis function neural network. Furtheunore, the RBF neural network theory is used to optimize the structure-related controller parameters in geotechnical engineering. The research results show that RBF neural network can more accurately predict the vibration response of civil engineering. It can effectively solve the time lag problem in vibration control.

关键词： Radial basis function Neural network Lyapunov stability theory Civil engineering Vibration control differential evolution algorithm

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Cooperative DE-Based Localization algorithm for Wireless Communication Network

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INTERNATIONAL JOURNAL OF WIRELESS INFORMATION NETWORKS 2023年第4期30卷 306-315页

作者： Gao, Ying Xia, Bin Zhang, Liye Shandong Univ Technol Sch Comp Sci & Technol Zibo 255000 Shandong Peoples R China Shandong Vocat Coll Ind Coll Architecture & Informat Engn Zibo Peoples R China

Although the conventional differential evolution (DE) localization algorithm provides reasonable localization results, its improvement in localization performance is limited. Considering the distance information between the tags being an effective way to improve localization performance, this paper proposes a novel cooperative DE-based localization algorithm by presenting the localization process and the fitness function, which contains the distance information between the tags. Specifically, our method first utilizes the conventional DE algorithm to provide the initial values of the tags. Then, according to the initial values, the distance information between the tags is fully utilized to improve the localization performance. The simulation results demonstrate our algorithm's effectiveness in improving the localization performance and simultaneously realizing the localization of all tags.

关键词： Cooperative localization differential evolution algorithm Localization accuracy Wireless communication network

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A hybrid layout optimization method based on genetic and differential evolution for Flat-panel(FP) microsatellite

A hybrid layout optimization method based on genetic and dif...

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第43届中国控制会议

作者： Hao Zhang Jun Zhou Guanghui Liu Jianguo Guo Zhenxin Feng School of Astronautics Northwestern Polytechnical University National Local Cooperative Engineering Laboratory of Microsatellite Technology and Application

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Flat-panel(FP) satellites are a novel type of microsatellite configuration, and a hybrid algorithmic layout optimization method is studied in this paper to solve the problem of its large moment of inertia. The method is implemented by two-level algorithms of genetic and differential evolution, in which the genetic algorithm is used to realize the optimal allocation of components within different satellite modules to ensure the mass balance between satellite modules;the differential evolution algorithm further plans the positions of components within the modules and enables the satellite to obtain the optimal moment of inertia and layout scheme. Finally, the effectiveness of the proposed method is verified by the case of layout optimization of remote sensing satellites.

关键词： Microsatellite layout optimization moment of inertia Genetic algorithm differential evolution algorithm

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A Firefighting Resource Dispatch Problem Optimization Using Metaheuristics 24th

A Firefighting Resource Dispatch Problem Optimization Using ...

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24th International Conference on Computational Science and Its Applications (ICCSA)

作者： Matos, Marina A. Goncalves, Rui Rocha, Ana Maria A. C. Costa, Lino A. Alvelos, Filipe Univ Minho ALGORITMI Res Ctr LASI Campus Gualtar P-4710057 Braga Portugal

ISBN: (纸本)9783031652226;9783031652233

Forest fires are a growing concern in our planet, as they have been increasing in frequency and severity over the last few decades. It is therefore essential to manage firefighting in order to prevent and reduce the damage caused to life, the economy and ecosystems. This work deals with the forest firefighting resource dispatch problem using metaheuristics. It is essential to know when and which suppression resources should be sent to extinguish the ignitions. Note that the faster the resources act, the smaller the burned area. An adapted genetic algorithm and differential evolution algorithm are used to solve a firefighting resource dispatch problem aiming to find the optimal allocation of resources to ignitions, and at what instant of time each resource is assigned to each ignition, in order to minimize the total burned area. The two metaheuristics are applied to a set of 60 randomly generated instances, comprising 5, 7 and 10 resources to extinguish 20 or 30 ignitions. A statistical analysis of the results is used to compare the efficiency of the metaheuristics, where the GA shows more competitive results.

关键词： Fire Suppression Dispatch Problem Genetic algorithm differential evolution algorithm

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Scientific and Technological Innovation Rapid Emergency Resource Constraint-Improved Particle Swarm Optimization Project Scheduling Method

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JOURNAL OF ADVANCED MANUFACTURING SYSTEMS 2023年第1期22卷 165-180页

作者： Chen, Manyi Tourism Coll Zhejiang Sch Business Adm Hangzhou 311231 Zhejiang Peoples R China

In the environment of scientific and technological innovation, the human and material resources that can reach the project threshold are often limited. For enterprise projects that require high R&D and production level to produce rapid emergency resources, they often face the problem of difficult effective allocation of emergency resources, resulting in project delay. Therefore, in the process of high-tech projects, it is necessary to deal with the lack of emergency scientific and technological resources. Therefore, based on the particle swarm optimization algorithm which is easy to be realized by scientific and technological innovation enterprises, this study improves its two shortcomings of easy local optimization and low performance, constructs the CD-PSO project scheduling model, verifies the performance of the improvement points, and tests the performance of the model in the actual project case of enterprise rapid emergency resource scheduling. The results show that the optimal solution of CD-PSO model in the three sample sets is higher than that of D-PSO model in the same group, and the average scheduling duration is the shortest, which is 350 days, and the proportion of reaching the optimal duration is 100%. The operation effect is obviously better than other scheduling models. The CD-PSO project scheduling model designed in this study is easier to implement and can calculate the optimal solution of the first project more efficiently. It not only has excellent performance, but also has a strong popularization foundation.

关键词： Emergency resource constraints project scheduling particle swarm optimization global optimal solution RCPSP cloud model differential evolution algorithm

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Master Slave Game Optimization Scheduling of Park Comprehensive Energy System Based on Stepped Demand Response

Master Slave Game Optimization Scheduling of Park Comprehens...

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International Conference on Wireless Power Transfer (ICWPT)

作者： Zhang, Xinhe Wang, Songcen Xu, Yichuan Yu, Xin Xia, Chenyang Xing, Aiwen Inst China Elect Power Res Beijing 100192 Peoples R China State Grid Jiangsu Elect Power Inst Changzhou Power Supply Branch Changzhou 213000 Peoples R China China Univ Min & Technol Xuzhou 221008 Peoples R China

ISBN: (纸本)9789819708796;9789819708772;9789819708765

In order to deeply understand the interactive process of various subjects in the park's integrated energy system, effectively guide the demand-side load optimization and promote energy low-carbon, this paper puts forward a masterslave game optimization scheduling model of the park's integrated energy system based on ladder demand response, with the park operator as the leader and the user aggregator as the follower. First of all, a master-slave game structure with park operators and user aggregators as the main body is constructed, and a stepped demand response incentive mechanism is established to guide the energy use behavior of the user side;Secondly, based on the Stackelberg game theory and taking into account the carbon trading mechanism, with the goal ofmaximizing the comprehensive income of the park operators and the consumer surplus of the user aggregators, the optimal scheduling model of the comprehensive energy system considering the master-slave game of the park operators and the user aggregators is constructed;Finally, the theoretical research is analyzed and demonstrated through a numerical example, and the results show that the game interaction model proposed in this paper can effectively improve the benefits of the principal and subordinate parties, and reduce the carbon emission level of the system.

关键词： Comprehensive energy system of the park Stackelberg game differential evolution algorithm Demand response Optimize scheduling

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Optimizing Energy Scheduling and Power Flow in the Power System Using Coronavirus algorithm 6

Optimizing Energy Scheduling and Power Flow in the Power Sys...

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6th Global Power, Energy and Communication Conference (GPECOM)

作者： ALbaaj, Basim Kaplan, Orhan Gazi Univ Dept Elect Elect Engn Ankara Turkiye

ISBN: (纸本)9798350351088;9798350351095

Addressing financial losses in power systems is essential, leading to the need for effective solutions. This study focuses on optimizing energy scheduling from various generation sources within the power system. To achieve this, an optimal power flow (OPF) problem has been formulated for IEEE 30 buses system considering constraints such as voltage stability, line capacity and power generation and solved in MATLAB using the coronavirus optimization algorithm (COVIDOA) and differential evolution algorithm (DE). The simulation results of static optimal power flow (SOPF) indicate that COVIDOA achieves a cost reduction of $576.459569/h, while DE achieves $578.125673/h. Additionally, COVIDOA and DE led to power loss reductions of 2.5906 MW and 2.6802 MW, respectively. Furthermore, COVIDOA and DE reduce carbon emissions by 0.2069 ton/h and 0.2081 ton/h, respectively, which shows the superior performance of COVIDO algorithm over DE. In dynamic optimal power flow (DOPF) over 24 hours under uncertainty with the presence of renewable energy and energy storage systems, the total cost minimization amounts to $11717.6471, with minimizing all below objectives as total power loss to 67.26100769 MW, total amount of emissions to 5.17373409 tons, and total voltage deviation to 11.14408357 pu when compared with the absence of renewable energy and energy Storage Systems. These results underscore the effectiveness of the proposed algorithm COVIDOA in dynamic optimal power flow (DOPF).

关键词： coronavirus optimization algorithm differential evolution algorithm optimal power flow

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Multi-view BLUP: a promising solution for post-omics data integrative prediction

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Journal of Genetics and Genomics 2025年第6期52卷 839-847页

作者： Wu, Bingjie Xiong, Huijuan Zhuo, Lin Xiao, Yingjie Yan, Jianbing Yang, Wenyu National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural University Hubei Wuhan 430070 China College of Informatics Huazhong Agricultural University Hubei Wuhan 430070 China Hubei Hongshan Laboratory Hubei Wuhan 430070 China

Phenotypic prediction is a promising strategy for accelerating plant breeding. Data from multiple sources (called multi-view data) can provide complementary information to characterize a biological object from various aspects. By integrating multi-view information into phenotypic prediction, a multi-view best linear unbiased prediction (MVBLUP) method is proposed in this paper. To measure the importance of multiple data views, the differential evolution algorithm with an early stopping mechanism is used, by which we obtain a multi-view kinship matrix and then incorporate it into the BLUP model for phenotypic prediction. To further illustrate the characteristics of MVBLUP, we perform the empirical experiments on four multi-view datasets in different crops. Compared to the single-view method, the prediction accuracy of the MVBLUP method has improved by 0.038–0.201 on average. The results demonstrate that the MVBLUP is an effective integrative prediction method for multi-view data. © 2025

关键词： Best linear unbiased prediction differential evolution algorithm Multi-view data Phenotype prediction Similarity function

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