A coevolutionary algorithm is an extention of the conventional genetic algorithm that incorporates the strategy of divide and conquer in developing a complex solution in the form of interacting co-adapted subcomponent...
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ISBN:
(纸本)0769528759
A coevolutionary algorithm is an extention of the conventional genetic algorithm that incorporates the strategy of divide and conquer in developing a complex solution in the form of interacting co-adapted subcomponents. In this paper we propose an efficient coevolutionary algorithm dynamically controlling species splitting and merging. Our algorithm conducts efficient local search in the reduced search space by splitting species for independent variables while it conducts global search by merging species for interdependent variables. We have experimented the proposed algorithm with some benchmarking function optimization problems and the inventory control problem, and have shown that the algorithm outperforms the existing coevolutionary algorithms.
Aiming to solve the issues of large time cost, low efficiency, and high computational complexity in the design of cavity filters using traditional approaches, a multi-objective design approach for cavity filters based...
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The proceedings contain 8 papers. The special focus in this conference is on AI-assisted Solutions for COVID-19 and Biomedical Applications in Smart Cities. The topics include: The Impact of Contingency Measures ...
ISBN:
(纸本)9783031382031
The proceedings contain 8 papers. The special focus in this conference is on AI-assisted Solutions for COVID-19 and Biomedical Applications in Smart Cities. The topics include: The Impact of Contingency Measures on the COVID-19 Reproduction Rate;Business Intelligence Platform for COVID-19 Monitoring: A Case Study;First Clustering Analysis of COVID in Portugal;Multichannel Services for Patient Home-Based Care During COVID-19;steps Towards intelligent Diabetic Foot Ulcer Follow-Up Based on Deep learning;recommendation of Medical Exams to Support Clinical Diagnosis Based on Patient’s Symptoms.
To compare three traditional extraction methods (extraction with cold water, ultrasonic extraction and circumfluence extraction with hot water) of gastrodin and gastrodigenin, the best one is circumfluence extraction ...
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ISBN:
(纸本)9780819495594
To compare three traditional extraction methods (extraction with cold water, ultrasonic extraction and circumfluence extraction with hot water) of gastrodin and gastrodigenin, the best one is circumfluence extraction with hot water. And the optimum extraction process was carried out through orthogonal experiment. The best extraction temperature was 60 degrees C and extracted 3 times by 70% alcohol. The time for each extraction was 1 hour, and the quantity of alcohol used 10 times more than the quantity of sample. The contents of samples in different harvest festivals (from August to January) were determined. The best time is in September.
The rapid evolution of intelligent computing and next-generation networks necessitates models that accurately capture the intricate dynamics of time-dependent systems. This paper introduces an innovative adaptation of...
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Dynamic deployment is one of the key topics addressed in wireless sensor networks (WSNs) study, which refers to coverage and detection probability of WSNs. This paper proposes a self-organizing algorithm for enhancing...
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ISBN:
(纸本)9783540741701
Dynamic deployment is one of the key topics addressed in wireless sensor networks (WSNs) study, which refers to coverage and detection probability of WSNs. This paper proposes a self-organizing algorithm for enhancing the coverage and detection probability for WSNs which consist of mobile and stationary nodes, which is so-called virtual force-directed particle swarm optimization (VFPSO). The proposed algorithm combines the virtual force (VF) algorithm with particle swarm optimization (PSO), where VF uses a judicious combination of attractive and repulsive forces to determine virtual motion paths and the rate of movement for sensors and PSO is suitable for solving multi-dimension function optimization in continuous space. In VFPSO, the velocity of each particle is updated according to not only the historical local and global optimal solutions but also the virtual forces of sensor nodes. Simulation results demonstrate that the proposed VFPSO has better performance on regional convergence and global searching than PSO algorithm and can implement dynamic deployment of WSNs more efficiently and rapidly.
In this paper, a suitable particle swarm optimization (PSO) is firstly proposed to obtain the pulse width modulation (PWM) switching time which maintaining a required fundamental voltage and a minimal total harmonic d...
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ISBN:
(纸本)9783540741701
In this paper, a suitable particle swarm optimization (PSO) is firstly proposed to obtain the pulse width modulation (PWM) switching time which maintaining a required fundamental voltage and a minimal total harmonic distortion (THD) for very high power medium voltage induction motor drives fed by a double three-level inverter (DTI). The simulation results show that double three-level inverter based variable frequency drive system needs switches with lower voltage rating, and performs lower voltage harmonics than one single three-level inverter. After optimizing the PWM switching time by using particle swarm optimization, the motor drive voltage quality improves more.
The generating of training datasets for machine learning projects is a topical problem. The cost of dataset formation can be considerably high, yet there is no guarantee of an acceptable quality of prepared data. The ...
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ISBN:
(数字)9783030313623
ISBN:
(纸本)9783030313623;9783030313616
The generating of training datasets for machine learning projects is a topical problem. The cost of dataset formation can be considerably high, yet there is no guarantee of an acceptable quality of prepared data. The important issue of dataset generation is the labeling noise. The main causes of this phenomena are: expert errors, information insufficiency, subjective factors and so on. Labeling noise affects the learning stage of a neuronet and so increases the number of errors during the one's functioning. In the current paper the technique to decrease the labeling noise level is proposed. It is based on the principals of the distributed ledger technology. While there is a possibility to decrease the labeling errors number, the services integration on the basis of distributed ledger allows to improve the efficiency of dataset forming.
An accurate and reliable energy-consumption model is the key to operation optimization and energy-saving diagnosis of thermal power units especially under different operation conditions and boundaries. Conventional ma...
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In many microgrids with a lot of uncontrollable DGs, a well-planned operation of the energy storage station is an important guarantee for the stability and economy of the microgrid. To solve the operation optimization...
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