The large-scale integration of electric vehicles (EVs) and renewable energy generation systems into the power grid poses significant challenges to the security and stability of existing power systems. This paper explo...
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ISBN:
(数字)9798350351330
ISBN:
(纸本)9798350351347
The large-scale integration of electric vehicles (EVs) and renewable energy generation systems into the power grid poses significant challenges to the security and stability of existing power systems. This paper explores the optimal scheduling problem of microgrids incorporating EVs and distributed energy sources, including wind, photovoltaic, and coal-fired units. The study also considers the grid load regulation function of electric vehicles based on Vehicle-to-Grid (V2G) technology. A scheduled strategy based on State of Charge (SOC) detection of electric vehicles is proposed, taking into account the operating costs of generating units, the costs associated with wind and solar energy curtailment, and the charging and discharging costs of electric vehicles. A microgrid optimization scheduling model is established with the objective of minimizing the total operating costs within the scheduling period. An improved bee algorithm, combining the bee algorithm with the artificial bee colony algorithm, is employed to solve the model. Comparisons with results from unscheduled charging demonstrate the effectiveness and superiority of the proposed scheduling model.
In an effort to bolster the healthcare system in Thailand, particularly in remote areas with limited access to pharmacists, this study proposes a novel drug recommendation system based on drug details. This system aim...
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ISBN:
(数字)9798350370096
ISBN:
(纸本)9798350370102
In an effort to bolster the healthcare system in Thailand, particularly in remote areas with limited access to pharmacists, this study proposes a novel drug recommendation system based on drug details. This system aims to address the challenge of medication selection in resource-constrained settings by providing physicians with informed recommendations tailored to patient *** proposed system utilizes drug data sourced from ***, encompassing approximately 34,284 drug entries. To ensure high-quality recommendations, the data undergoes a rigorous preprocessing phase involving null value imputation and feature extraction using techniques like TF-IDF. Following preprocessing, a combination of Drug Recommendation with cosine similarity and the Bee algorithm is employed. Cosine similarity establishes a baseline for drug similarity, while the Bee algorithm optimizes the selection process by considering additional factors beyond just similarity, such as potential side effects and cost-effectiveness.
Phishing attacks are one of the most damaging attacks for internet users. Detecting these attacks is one of the main challenges in the internet security due to their lack of unpredictable nature. Machine learning tech...
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ISBN:
(数字)9798350329797
ISBN:
(纸本)9798350329803
Phishing attacks are one of the most damaging attacks for internet users. Detecting these attacks is one of the main challenges in the internet security due to their lack of unpredictable nature. Machine learning techniques are suitable methods to detect these attacks. The accuracy of these methods is highly dependent on the features of the data. This paper proposes a hybrid feature selection approach based on Bee algorithm and Logistic Regression method, which detects phishing attacks accurately by selecting an appropriate feature subset. The results show that the proposed approach has been able to increase the detection accuracy of phishing attacks by 1.23%, using a random forest algorithm, in comparison to previous works with 6.66% less features.
This paper presents a system of systems approach to implement high-speed searching to solve complex optimization problems. In most optimization techniques, parallel computation is not effective due to the complexity i...
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ISBN:
(纸本)9781424481972;9781424481965
This paper presents a system of systems approach to implement high-speed searching to solve complex optimization problems. In most optimization techniques, parallel computation is not effective due to the complexity in algorithm. Here, the search sp ace is distributed within processing power of high performance computing resources. The methodology takes advantage of the bees algorithm and adapts it for best performance in a cluster grid computing environment. The effectiveness of the approach is verified by solving the problem of efficiency in a power plant by economizing fuel cost, efficiency of transmission losses and environmental hazardous emissions.
In robot emergency rescue scenarios, it is common for communication between robots to be restricted, allowing interaction only within localized communication ranges. However, commonly proposed task allocation algorith...
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ISBN:
(数字)9798350354409
ISBN:
(纸本)9798350354416
In robot emergency rescue scenarios, it is common for communication between robots to be restricted, allowing interaction only within localized communication ranges. However, commonly proposed task allocation algorithms with weak communication models often focus on communication quality while neglecting communication distance. Consequently, this paper introduces a Bernoulli communication model incorporating distance information as the communication model. Subsequently, a two-stage distributed task allocation algorithm is proposed based on this communication model. In the convergence stage, each robot utilizes the K-means algorithm to determine the target task group and employs a distributed bee algorithm to select the target task. In the dispersion stage, robots use an improved distributed genetic algorithm to allocate remaining tasks, exchanging optimal solutions with other robots, thereby achieving conflict-free autonomous task allocation. Finally, real-time simulations are conducted to validate that this algorithm effectively resolves the multi-robot task allocation problem within the localized communication model.
＜正＞People have learnt from biological system behaviours and structures to design and develop a number of different kinds of optimisation algorithms that have been widely used in both theoretical study and practical...
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＜正＞People have learnt from biological system behaviours and structures to design and develop a number of different kinds of optimisation algorithms that have been widely used in both theoretical study and practical applications in engineering and business *** efficient supply chain is very important for companies to survive in global competitive ***
This research paper explores hybrid anomaly detection frameworks combining Particle Swarm Optimization (PSO), Bee algorithm (ABC), and Genetic algorithm (GA) using the CICIDS2017 dataset for Wireless Sensor Networks (...
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ISBN:
(数字)9798331532956
ISBN:
(纸本)9798331532963
This research paper explores hybrid anomaly detection frameworks combining Particle Swarm Optimization (PSO), Bee algorithm (ABC), and Genetic algorithm (GA) using the CICIDS2017 dataset for Wireless Sensor Networks (WSNs). It evaluates their effectiveness across diverse network conditions, focusing on anomaly detection accuracy, false positives, and computational efficiency. The study aims to advance anomaly detection methodologies in WSNs by contributing insights into bio-inspired algorithms' impact on anomaly detection. It targets the research community, industry professionals, and scholars, emphasizing the documentation of methodologies and suggesting future research on hybridization techniques, dynamic adaptation, edge computing integration, and machine learning fusion for enhanced anomaly detection in WSNs, fostering improved network security and adaptability.
This paper presents the optimization of the performances of a controlled zero ripple input current high boost dc-dc converter with coupled inductors. In particular, an optimization of PID and LQR controllers design is...
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ISBN:
(纸本)9781467388634
This paper presents the optimization of the performances of a controlled zero ripple input current high boost dc-dc converter with coupled inductors. In particular, an optimization of PID and LQR controllers design is presented, using three heuristic methods: Genetic, Particle Swarm and bees algorithms. A comparison between the PID and LQR controllers, with and without optimization, is presented together with the analysis of effectiveness of the three heuristic algorithms to design the control strategies.
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