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Shuffled complex evolution coupled with stochastic ranking for reservoir scheduling problems

Water Science and Engineering 2019年第4期12卷 307-318页

作者： Jing-qiao Mao Ming-ming Tian Teng-fei Hu Kang Ji Ling-quan Dai Hui-chao Dai College of Water Conservancy and Hydropower Engineering Hohai UniversityNanjing 210098China Ningbo Hongtai Water Conservancy Information Technology Co. Ltd.Ningbo 315040China College of Hydraulic and Environmental Engineering Three Gorges UniversityYichang 443002China

This paper introduces an optimization method(SCE-SR)that combines shuffled complex evolution(SCE)and stochastic ranking(SR)to solve constrained reservoir scheduling problems,ranking individuals with both objectives and constrains considered.A specialized strategy is used in the evolution process to ensure that the optimal results are feasible *** method is suitable for handling multiple conflicting constraints,and is easy to implement,requiring little parameter *** search properties of the method are ensured through the combination of deterministic and probabilistic *** proposed SCE-SR was tested against hydropower scheduling problems of a single reservoir and a multi-reservoir system,and its performance is compared with that of two classical methods(the dynamic programming and genetic algorithm).The results show that the SCE-SR method is an effective and efficient method for optimizing hydropower generation and locating feasible regions quickly,with sufficient global convergence properties and *** operation schedules obtained satisfy the basic scheduling requirements of reservoirs.

关键词： Reservoir scheduling Optimization method Constraint handling Shuffled complex evolution stochastic ranking

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An Improved C2oDE based on stochastic ranking

An Improved C2oDE based on Stochastic Ranking

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第40届中国控制会议

作者： Chengyong Si Jianqiang Shen Chaofeng Wang Lei Wang Sino-German College University of Shanghai for Science and Technology College of Electronics and Information Engineering Tongji University

How to balance diversity and convergence as well as constraint violation and objective function in Constrained Optimization Problems(COPs) is an important and key issue when designing a new Constrained Optimization Evolutionary Algorithms(COEAs).CoDE set a good example on this point,and obtains promising *** CoDE,the two Constraint Handling Techniques(CHTs) are adopted fixed in two *** verify whether there are some other more efficient combinations,stochastic ranking(SR) is adopted to balance the *** results show a good performance,which also give some inspiration for other research.

关键词： constrained optimization stochastic ranking Deb’s feasibility-based rule ε-constrained method

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A Novel Hybrid Optimization Algorithm for Materialized View Selection from Data Warehouse Environments

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Computer Systems Science & Engineering 2023年第11期47卷 1527-1547页

作者： Popuri Srinivasarao Aravapalli Rama Satish School of Computer Science and Engineering VIT-AP UniversityAmaravatiIndia

Responding to complex analytical queries in the data warehouse(DW)is one of the most challenging tasks that require prompt *** problem of materialized view(MV)selection relies on selecting the most optimal views that can respond to more queries *** work introduces a combined approach in which the constraint handling process is combined with metaheuristics to select the most optimal subset of DW views from *** proposed work initially refines the solution to enable a feasible selection of views using the ensemble constraint handling technique(ECHT).The constraints such as self-adaptive penalty,epsilon(ε)-parameter and stochastic ranking(SR)are considered for constraint *** two constraints helped the proposed model select the finest views that minimize the objective ***,a novel and effective combination of Ebola and coot optimization algorithms named hybrid Ebola with coot optimization(CHECO)is introduced to choose the optimal *** and Coot have recently introduced metaheuristics that identify the global optimal set of views from the given *** combining these two algorithms,the proposed framework resulted in a highly optimized set of views with minimized *** cost functions are described to enable the algorithm to choose the finest solution from the problem ***,extensive evaluations are conducted to prove the performance of the proposed approach compared to existing *** proposed framework resulted in a view maintenance cost of 6,329,354,613,784,query processing cost of 3,522,857,483,566 and execution time of 226 s when analyzed using the TPC-H benchmark dataset.

关键词： Materialization ensemble approach stochastic ranking optimization optimal view selection

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Application of Domain Decomposition Method and Improved Evolutionary Algorithm in Optimization of Spiral Inductors on Silicon

Application of Domain Decomposition Method and Improved Evol...

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2006 the IET International Conference on Wireless Mobile & Multimedia Networks

作者： Dan He Lingling Sun Jun Liu Institute of CAD Hangzhou Dianzi University Hangzhou 310018 P. R. China

＜正＞The rising demand for low-cost radio frequency integrated circuits has generated an increasing interest in planar spiral inductors on silicon .However, several kinds of parasitic effects degrade the quality factor of inductors, so it is essential to choose optimal parameters to gain high Q values under some constraints. Thus nonlinear constrained optimization problem is formed. Calculating inductance with domain decomposition method can avoid tedious calculation for mutual inductance, and handling constrained optimization with stochastic ranking-based evolutionary algorithm can avoid determining optimal penalty coefficient. The paper tries to combine both methods and gains better results in less time. Experiments show that combining both methods can solve the nonlinear constrained optimization problem efficiently and successfully.

关键词： evolutionary algorithm stochastic ranking decomposition method

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