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2009 IEEE Congress on Evolutionary Computation最新文献

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A Genetic Ant Colony Optimization Algorithm for Inter-domain Path Computation problem under the Domain Uniqueness constraint 域唯一性约束下域间路径计算问题的遗传蚁群优化算法
Pub Date : 2022-01-01 DOI: 10.1109/CEC55065.2022.9870339
D. T. Anh, Nguyen Hoang Long, Tran Van Diep, Huynh Thi Thanh Binh
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引用次数: 0
Step-Size Individualization: a Case Study for The Fish School Search Family 步长个性化:鱼群搜索家族的案例研究
Pub Date : 2022-01-01 DOI: 10.1109/CEC55065.2022.9870212
H. A. A. Neto, M. Lacerda, F. B. L. Neto
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引用次数: 0
Many Layer Transfer Learning Genetic Algorithm (MLTLGA): a New Evolutionary Transfer Learning Approach Applied To Pneumonia Classification 多层迁移学习遗传算法:一种应用于肺炎分类的进化迁移学习新方法
Pub Date : 2021-01-01 DOI: 10.1109/CEC45853.2021.9504912
Raphael de Lima Mendes, Alexandre Henrick da Silva Alves, M. S. Gomes, P. L. L. Bertarini, L. R. Amaral
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引用次数: 0
Applying Never-Ending Learning (NEL) Principles to Build a Gene Ontology (GO) Biocurator 应用永无止境的学习(NEL)原理构建基因本体(GO)生物库
Pub Date : 2021-01-01 DOI: 10.1109/CEC45853.2021.9504981
L. R. Amaral, Alexandre Henrick da Silva Alves, Raphael de Lima Mendes, M. S. Gomes, P. L. L. Bertarini, Estevam Hruschka
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引用次数: 0
An Ensemble of Scalarizing Functions and Weight Vectors for Evolutionary Multi-Objective Optimization 基于尺度函数和权向量的进化多目标优化
Pub Date : 2021-01-01 DOI: 10.1109/CEC45853.2021.9504941
Diana Cristina Valencia-Rodríguez, C. Coello
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引用次数: 0
Autonomous Monitoring System for Water Resources based on PSO and Gaussian Process 基于粒子群和高斯过程的水资源自主监测系统
Pub Date : 2021-01-01 DOI: 10.1109/CEC45853.2021.9504936
Micaela Jara Ten Kathen, Isabel Jurado Flores, Daniel Gutiérrez-Reina, Alejandro Tapia Córdoba
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引用次数: 0
A Simulated IMO-DRSA Approach for Cognitive Reduction in Multiobjective Financial Portfolio Interactive Optimization 多目标金融组合交互优化认知约简的模拟IMO-DRSA方法
Pub Date : 2021-01-01 DOI: 10.1109/CEC45853.2021.9504906
Julio Cezar Soares Silva, Adiel Almeida Filho
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引用次数: 0
An Empirical Study on the Use of the S-energy Performance Indicator in Mating Restriction Schemes for Multi-Objective Optimizers s -能量绩效指标在多目标优化器匹配约束方案中的应用实证研究
Pub Date : 2021-01-01 DOI: 10.1109/CEC45853.2021.9504869
Amín V. Bernabé Rodríguez, C. Coello
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引用次数: 0
A preference-based multi-objective demand response mechanism 基于偏好的多目标需求响应机制
Pub Date : 2020-07-01 DOI: 10.1109/CEC48606.2020.9185875
I. R. S. Silva, Jose Eduardo Almeida de Alencar, R. Rabêlo
The demand response (DR) aims to balance the purveyance and demand of electricity to maximize the reliability and efficiency of the energy supply process in the electrical power system (EPS). However, one of the main impediments to the insertion of DR in the residential context is the need of programming the use of various electrical appliances and the scheduling of renewable resources and storage system in the same time interval, that requires a range of specific knowledge and time availability of the consumer to handle the various home appliances. This article presents a preference-based multi-objective optimization model based on real-time electricity price to solve the problem of optimal residential load management. The proposal’s purpose is to minimize both the electricity consumption associated cost and the inconvenience caused to consumers. The proposed model was formalized as a nonlinear programming problem subject to a set of constraints associated with the consumption of electrical energy and operational aspects related to the residential appliance categories. The proposed multi-objective model was solved computationally by the Constrained Many-Objective Non-Dominated Sorted Genetic Algorithm (NSGA-III) to determine the new scheduling of residential appliances, renewable energy resources, and energy storage system utilization for the entire time horizon, considering consumer preferences. The results show that the multi-objective DR model proposed using the NSGA-III technique can minimize the total cost associated with energy consumption as well as reduce the inconvenience of consumers, besides helping consumers to take advantage of DR’s benefits without requiring manual intervention.
需求响应(DR)旨在平衡电力供应和需求,以最大限度地提高电力系统能源供应过程的可靠性和效率。然而,在住宅环境中插入DR的主要障碍之一是需要对各种电器的使用进行编程,并在同一时间间隔内调度可再生资源和存储系统,这需要消费者的一系列特定知识和时间可用性来处理各种家用电器。本文提出了一种基于实时电价的基于偏好的多目标优化模型,用于解决最优居民负荷管理问题。该建议的目的是尽量减少与电力消耗相关的成本和对消费者造成的不便。所提出的模型被形式化为一个非线性规划问题,该问题受到与电力消耗和与住宅电器类别相关的操作方面相关的一组约束。采用约束多目标非支配排序遗传算法(NSGA-III)对所提出的多目标模型进行计算求解,在考虑消费者偏好的情况下,确定整个时间范围内住宅电器、可再生能源和储能系统利用的新调度。结果表明,利用NSGA-III技术提出的多目标DR模型,除了帮助消费者在不需要人工干预的情况下利用DR的好处外,还可以使与能源消耗相关的总成本最小化,减少消费者的不便。
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引用次数: 3
Improving evolution of service configurations for moving target defense 改进移动目标防御服务配置的演化
Pub Date : 2020-01-01 DOI: 10.1109/CEC48606.2020.9185786
Ernesto Serrano Collado, Mario García Valdez, J. J. M. Guervós
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引用次数: 0
期刊
2009 IEEE Congress on Evolutionary Computation
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