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ieee SSCI 2011: symposium Series on computational intelligence - CIDUE 2011: 2011 ieee symposium on computational intelligence in dynamic and uncertain environments

IEEE SSCI 2011: Symposium Series on Computational Intelligen...

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symposium Series on computational intelligence, ieee SSCI 2011 - 2011 ieee symposium on computational intelligence in dynamic and uncertain environments, CIDUE 2011

ISBN: (纸本)9781424499311

The proceedings contain 7 papers. The topics discussed include: genetic algorithms with elitism-based immigrants for dynamic load balanced clustering problem in mobile ad hoc networks;an adaptive strategy for updating the memory in evolutionary algorithms for dynamic optimization;Trusted Learner: an improved algorithm for trusted incremental function approximation;theoretical and empirical analysis of diversity in non-stationary learning;iFAST: an intelligent fire-threat assessment and size-up technology for first responders;Hellinger distance based drift detection for nonstationary environments;and diagnosis of software erosion through fuzzy logic.

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ieee SSCI 2014: 2014 ieee symposium Series on computational intelligence - CIDUE 2014: 2014 ieee symposium on computational intelligence in dynamic and uncertain environments, Proceedings

IEEE SSCI 2014: 2014 IEEE Symposium Series on Computational ...

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2014 ieee symposium on computational intelligence in dynamic and uncertain environments, CIDUE 2014

ISBN: (纸本)9781479945160

The proceedings contain 11 papers. The topics discussed include: analysis of hyper-heuristic performance in different dynamic environments;multi-colony ant algorithms for the dynamic travelling salesman problem;real-world dynamic optimization using an adaptive-mutation compact genetic algorithm;performance evaluation of sensor-based detection schemes on dynamic optimization problems;a framework of scalable dynamic test problems for dynamic multi-objective optimization;short-term wind speed forecasting using support vector machines;ant colony optimization with self-adaptive evaporation rate in dynamic environments;learning features and their transformations from natural videos;neuron clustering for mitigating catastrophic forgetting in feedforward neural networks;evolutionary algorithms for bid-based dynamic economic load dispatch: a large-scale test case;and statistical hypothesis testing for chemical detection in changing environments.

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Proceedings of the 2013 ieee symposium on computational intelligence in dynamic and uncertain environments, CIDUE 2013 - 2013 ieee symposium Series on computational intelligence, SSCI 2013

Proceedings of the 2013 IEEE Symposium on Computational Inte...

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2013 ieee symposium on computational intelligence in dynamic and uncertain environments, CIDUE 2013 - 2013 ieee symposium Series on computational intelligence, SSCI 2013

ISBN: (纸本)9781467358491

The proceedings contain 14 papers. The topics discussed include: similarity-based evolution control for fitness estimation in particle swarm optimization;recurrent neural network ensembles for convergence prediction in surrogate-assisted evolutionary optimization;issues with performance measures for dynamic multi-objective optimization;takeover time in dynamic optimization problems;surrogate enhanced interactive genetic algorithm with weighted Gaussian process;dynamic significant feature extraction for embedded intelligent agent implementations;co-evolutionary learning in the N-choice iterated prisoner's dilemma with PSO algorithm in a spatial environment;an incremental approach for updating approximations of rough fuzzy set under the variation of attribute values;discounted expert weighting for concept drift;and a modular technique for monthly rainfall time series prediction.

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ieee CIDUE 2011 committee symposium on computational intelligence in dynamic and uncertain environments (ieee CIDUE 2011)

IEEE CIDUE 2011 committee symposium on Computational Intelli...

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ieee symposium on computational intelligence in dynamic and uncertain environments (CIDUE)

computational intelligence (CI) methodologies, including evolutionary algorithms, neural networks and fuzzy systems have shown to be sell suited to deal with significant uncertainties that may be encountered in solving real-world problems. The purpose of this symposium is to bring together scientists, engineers, and graduate students to present and discuss recent advances in employing CI for solving scientific and engineering problems in the presence of uncertainties.

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Wireless Power Transfer Meets Semantic Communication for Resource-Constrained IoT Networks: A Joint Transmission Mode Selection and Resource Management Approach

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ieee INTERNET OF THINGS JOURNAL 2025年第1期12卷 556-568页

作者： Sang, Nguyen Huu Hai, Nguyen Duc Anh, Nguyen Duc Duy Luong, Nguyen Cong Nguyen, Van-Dinh Gong, Shimin Niyato, Dusit In Kim, Dong Phenikaa Univ Fac Comp Sci Hanoi 12116 Vietnam Vin Univ Coll Engn & Comp Sci Hanoi 100000 Vietnam Vin Univ Ctr Environm Intelligence Hanoi 100000 Vietnam SunYat Sen Univ Sch Intelligent Syst Engn Guangzhou 510275 Peoples R China Nanyang Technol Univ Coll Comp & Data Sci Singapore 639798 Singapore Sungkyunkwan Univ Dept Elect & Comp Engn Suwon 16419 South Korea

In this work, we consider the integration of energy harvesting (EH) and semantic communication strategies in resource-constrained Internet of Things (IoT) systems. The system empowers IoT devices to harvest energy from a base station, utilizing this harvested energy for the extraction and transmission of semantic information (e.g., scene graphs). To maximize the total transmission of image data or scene graphs to the central station, we formulate a comprehensive problem that jointly optimizes the EH duration, original image selection, transmit power, and channel allocation to IoT devices. The challenges arising from the dynamic environments and uncertain system parameters are effectively tackled by policy-based deep reinforcement learning algorithms, i.e., advantage actor-critic (A2C) and proximal policy optimization (PPO). Simulation results are implemented on the real data set clearly showing the superior performance achieved by our proposed algorithms compared to the baseline schemes. Notably, our approach enables IoT devices to transmit a greater number of original images and scene graphs with increased triplets to the central station, as highlighted in the simulation outcomes. This phenomenon showcases the potential of our strategy to enhance the capabilities of IoT systems in dynamic environments.

关键词： Internet of Things Semantics Energy harvesting Optimization Base stations Resource management Channel allocation deep reinforcement learning (DRL) energy harvesting (EH) power control semantic communication (SemCom)

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Takeover time in dynamic optimization problems

Takeover time in dynamic optimization problems

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2013 ieee symposium on computational intelligence in dynamic and uncertain environments, CIDUE 2013 - 2013 ieee symposium Series on computational intelligence, SSCI 2013

作者： Bravo, Yesnier Luque, Gabriel Alba, Enrique Departamento de Lenguajes y Ciencias de la Computación Universidad de Málaga Málaga Spain

ISBN: (纸本)9781467358491

The analysis of selection pressure is a mathematical tool that has been traditionally used for studying the dynamics of population-based optimization algorithms in stationary environments, but in dynamic optimization problems (DOPs) it is still an open issue. Common metrics such as growth curve and takeover time have no clear meaning when the problem changes over time. In this article, we propose a new definition of takeover time for DOPs. For the sake of clarity, we focus on evolutionary algorithms (EA), but results could be extended to other population-based algorithms. A model for calculating takeover time values is proposed and then its accuracy is later experimentally validated. © 2013 ieee.

关键词： Computation theory

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2013 ieee symposium on computational intelligence in dynamic and uncertain environments, CIDUE 2013 - 2013 ieee symposium Series on computational intelligence, SSCI 2013

作者： Sun, Chaoli Zeng, Jianchao Pan, Jengshyang Jin, Yaochu Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Taiyuan Shanxi 030024 China Shenzhen Graduate School Harbin Institute of Technology Shenzhen Guangdong 518055 China Department of Computing University of Surrey Guildford GU2 7XH United Kingdom

ISBN: (纸本)9781467358491

Evolution control in the surrogate-assisted evolutionary and other meta-heuristic optimization algorithms is essential for their success in efficiently achieving the global optimum. In order to further reduce the number of fitness evaluations, a similarity-based evolution control method is introduced into the fitness estimation strategy for particle swarm optimization (FESPSO) [1]. In the proposed method, the fitness of a particle is either estimated or evaluated, depending on its similarity to the particle whose fitness is known. The performance of the proposed algorithm is examined on eight benchmark problems, and the simulation results show that the proposed algorithm is highly competitive on reducing the number of required fitness evaluations using the computationally expensive fitness function. © 2013 ieee.

关键词： Particle swarm optimization (PSO)

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Issues with performance measures for dynamic multi-objective optimisation

Issues with performance measures for dynamic multi-objective...

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2013 ieee symposium on computational intelligence in dynamic and uncertain environments, CIDUE 2013 - 2013 ieee symposium Series on computational intelligence, SSCI 2013

作者： Helbig, Marde Engelbrecht, Andries P. CSIR: Meraka Institute Brummeria South Africa University of Pretoria Computer Science Pretoria South Africa University of Pretoria Computer Science Department Pretoria South Africa

ISBN: (纸本)9781467358491

In recent years a number of algorithms were proposed to solve dynamic multi-objective optimisation problems. However, a major problem in the field of dynamic multi-objective optimisation is a lack of standard performance measures to quantify the quality of solutions found by an algorithm. In addition, the selection of performance measures may lead to misleading results. This paper highlights issues that may cause misleading results when comparing dynamic multi-objective optimisation algorithms with performance measures that are currently used in the field. © 2013 ieee.

关键词： Multiobjective optimization

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LDCnet: Minimizing the cost of supervision for various types of concept drift

LDCnet: Minimizing the cost of supervision for various types...

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2013 ieee symposium on computational intelligence in dynamic and uncertain environments, CIDUE 2013 - 2013 ieee symposium Series on computational intelligence, SSCI 2013

作者： Sobolewski, Piotr Wozniak, Michal Wroclaw University of Technology 27 Wybrzeze Wyspiańskiego St Wroclaw 50-370 Poland

ISBN: (纸本)9781467358491

Supervision cost is often overlooked when designing decision systems to cope with concept drift. The solution presented in this article utilizes low supervision while achieving similar efficiency to the state-of-the-art methods. Algorithm bases on a net of classification models which cover the whole feature space, preparing the system for every possible concept. The experiments are performed in the simulated environment with four scenarios representing different types of concept drift. © 2013 ieee.

关键词： Artificial intelligence

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An adaptive strategy for updating the memory in evolutionary algorithms for dynamic optimization

An adaptive strategy for updating the memory in evolutionary...

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ieee symposium on computational intelligence in dynamic and uncertain environments

作者： Zhu, Tao Luo, Wenjian Li, Zhifang Nature Inspired Computation and Applications Laboratory School of Computer Science and Technology University of Science and Technology of China Hefei 230027 Anhui China Anhui Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Hefei 230027 China

ISBN: (纸本)9781424499311

The memory scheme is one of the most widely employed techniques in Evolutionary Algorithms for solving dynamic optimization problems. The updating strategy is a key concern for the memory scheme. Unfortunately, the existent memory updating strategies neglect the characteristics of the memory updating behaviors, and sometimes this could lead results against the original intention. In this paper, a novel updating strategy is proposed, which can adaptively update the memory according to the characteristics of the memory updating behaviors. Experiments are carried out in different kinds of dynamic environments, and the experimental results show that the proposed strategy is better than the traditional strategies. © 2011 ieee.

关键词： Evolutionary algorithms

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