Optimization of SM4 Encryption Algorithm for Power Metering Data Transmission

This study focuses on enhancing the security of the SM4 encryption algorithm for power metering data transmission by employing hybrid algorithms to optimize its substitution box (S-box). A multi-objective fitness function is constructed to evaluate the S-box structure, aiming to identify design solutions that satisfy differential probability, linear probability, and non-linearity balance. To achieve global optimization and local search for the S-box, a hybrid algorithm model that combines genetic algorithm and simulated annealing is introduced. This approach yields significant improvements in optimization effects and increased non-linearity. Experimental results demonstrate that the optimized S-box significantly reduces differential probability and linear probability while increasing non-linearity to 112. Furthermore, a comparison of the ciphertext entropy demonstrates enhanced encryption security with the optimized S-box. This research provides an effective method for improving the performance of the SM4 encryption algorithm

Optimization of SM4 Encryption Algorithm for Power Metering Data Transmission

This study focuses on enhancing the security of the SM4 encryption algorithm for power metering data transmission by employing hybrid algorithms to optimize its substitution box (S-box). A multi-objective fitness function is constructed to evaluate the S-box structure, aiming to identify design solutions that satisfy differential probability, linear probability, and non-linearity balance. To achieve global optimization and local search for the S-box, a hybrid algorithm model that combines genetic algorithm and simulated annealing is introduced. This approach yields significant improvements in optimization effects and increased non-linearity. Experimental results demonstrate that the optimized S-box significantly reduces differential probability and linear probability while increasing non-linearity to 112. Furthermore, a comparison of the ciphertext entropy demonstrates enhanced encryption security with the optimized S-box. This research provides an effective method for improving the performance of the SM4 encryption algorithm

Bioclimatic Design of Sustainable Campuses using Advanced Optimisation Methods

Cities occupy 0.5% of the earth surface, but they consume 75% of worldwide energy, and they are responsible of 50% to 80% of CO2 emissions. Cities are directly responsible for the climate change. However, they are the key for providing solutions to this problem. More specifically, a city comprises a very large number of microclimates, according to their urban and environmental design. A sustainable and liveable urban planning could well improve the urban environmental conditions by mitigating the energy fluxes of the city. The objective of this thesis is to address the energy fluxes within the urban environment, in time and space. The study focuses on the improvement of the energy demand of buildings as well as the outdoor human comfort. To do so, we try to establish a new bridge between the biometeorology and the architecture and to find a simplified approach to bring this research into practice. More specifically, the human comfort is well addressed in the research domain but, due to its complexity, it is quite difficult to use it in the real practice. In order to overcome this problem, we introduce three new modules in the software CitySim Pro. CitySim is an urban energy modelling tool which is able to quantify, dynamically, the energy demand from a building scale to the city scale. A first module, developed in this doctoral thesis, focuses on the quantification of the outdoor human comfort by the Index of Thermal Stress (ITS) and the COMFA* budget. The second module aims to understand the radiative environment by the calculation of the Mean Radiant Temperature (MRT). The third module focuses on the cooling potential of the vegetation and evaluates the shadings and the evapotranspiration provided by greenings. Based on the modules, Comfort Maps are designed, representing an important instrument to bring the research into practice: these maps are proposed as an effective way to share information between architects and municipalities, providing indications on the urban microclimatic conditions. Finally, the developed modules are used to optimize, using the hybrid CMA-ES/HDE evolutionary algorithm, the energy demand and the outdoor human comfort of two campuses: EPFL campus in Lausanne (Switzerland), and the Swiss International School (SISD) campus in Dubai (United Arab Emirates). On site monitoring, realized in the SISD campus, underlined the impact of the built environment, as well as the shadowing strategies, by punctual monitoring in five locations of the campus. The results show that i) we should not limit an architectural design to a single building, but it is important to think and design at the district/ city scale. There is ii) a strong relationship between the energy demand of buildings and the outdoor human comfort, consequently both of them should be jointly addressed by architects and urban planners, focusing on the building and the "space between buildings" design. Finally, iii) a sound urban design should derive from the bioclimatology, transforming the climatic adversities into design opportunities. Finally, a list of practical recommendations is defined, providing a support for a sound architectural design in time, and space

An Effective Hybrid Butterfly Optimization Algorithm with Artificial Bee Colony for Numerical Optimization

In this paper, a new hybrid optimization algorithm which combines the standard Butterfly Optimization Algorithm (BOA) with Artificial Bee Colony (ABC) algorithm is proposed. The proposed algorithm used the advantages of both the algorithms in order to balance the trade-off between exploration and exploitation. Experiments have been conducted on the proposed algorithm using ten benchmark problems having a broad range of dimensions and diverse complexities. The simulation results demonstrate that the convergence speed and accuracy of the proposed algorithm in finding optimal solutions is significantly better than BOA and ABC

A Hybrid Optimization Algorithm for Efficient Virtual Machine Migration and Task Scheduling Using a Cloud-Based Adaptive Multi-Agent Deep Deterministic Policy Gradient Technique

This To achieve optimal system performance in the quickly developing field of cloud computing, efficient resource management—which includes accurate job scheduling and optimized Virtual Machine (VM) migration—is essential. The Adaptive Multi-Agent System with Deep Deterministic Policy Gradient (AMS-DDPG) Algorithm is used in this study to propose a cutting-edge hybrid optimization algorithm for effective virtual machine migration and task scheduling. An sophisticated combination of the War Strategy Optimization (WSO) and Rat Swarm Optimizer (RSO) algorithms, the Iterative Concept of War and Rat Swarm (ICWRS) algorithm is the foundation of this technique. Notably, ICWRS optimizes the system with an amazing 93% accuracy, especially for load balancing, job scheduling, and virtual machine migration. The VM migration and task scheduling flexibility and efficiency are greatly improved by the AMS-DDPG technology, which uses a powerful combination of deterministic policy gradient and deep reinforcement learning. By assuring the best possible resource allocation, the Adaptive Multi-Agent System method enhances decision-making even more. Performance in cloud-based virtualized systems is significantly enhanced by our hybrid method, which combines deep learning and multi-agent coordination. Extensive tests that include a detailed comparison with conventional techniques verify the effectiveness of the suggested strategy. As a consequence, our hybrid optimization approach is successful. The findings show significant improvements in system efficiency, shorter job completion times, and optimum resource utilization. Cloud-based systems have unrealized potential for synergistic optimization, as shown by the integration of ICWRS inside the AMS-DDPG framework. Enabling a high-performing and sustainable cloud computing infrastructure that can adapt to the changing needs of modern computing paradigms is made possible by this strategic resource allocation, which is attained via careful computational utilization

DEK-Forecaster: A Novel Deep Learning Model Integrated with EMD-KNN for Traffic Prediction

Internet traffic volume estimation has a significant impact on the business policies of the ISP (Internet Service Provider) industry and business successions. Forecasting the internet traffic demand helps to shed light on the future traffic trend, which is often helpful for ISPs decision-making in network planning activities and investments. Besides, the capability to understand future trend contributes to managing regular and long-term operations. This study aims to predict the network traffic volume demand using deep sequence methods that incorporate Empirical Mode Decomposition (EMD) based noise reduction, Empirical rule based outlier detection, and $K$-Nearest Neighbour (KNN) based outlier mitigation. In contrast to the former studies, the proposed model does not rely on a particular EMD decomposed component called Intrinsic Mode Function (IMF) for signal denoising. In our proposed traffic prediction model, we used an average of all IMFs components for signal denoising. Moreover, the abnormal data points are replaced by $K$ nearest data points average, and the value for $K$ has been optimized based on the KNN regressor prediction error measured in Root Mean Squared Error (RMSE). Finally, we selected the best time-lagged feature subset for our prediction model based on AutoRegressive Integrated Moving Average (ARIMA) and Akaike Information Criterion (AIC) value. Our experiments are conducted on real-world internet traffic datasets from industry, and the proposed method is compared with various traditional deep sequence baseline models. Our results show that the proposed EMD-KNN integrated prediction models outperform comparative models.Comment: 13 pages, 9 figure

Long term energy demand forecasting based on hybrid, optimization: Comparative study

The objective of this research is to develop a long term energy demand forecasting model that used hybrid optimization.To accomplish this goal, a hybrid algorithm that combined a genetic algorithm and a local search algorithm method has been developed to overcome premature convergence.Model performances of hybrid algorithm were compared with former single algorithm model in estimating parameter values of an objective function to measure the goodness-of-fit between the observed data and simulated results.Averages error between two models was adopt to select the proper model for future projection of energy demand

A New Approach in Dynamic Traveling Salesman Problem: A Hybrid of Ant Colony Optimization and Descending

ABSTRAC

Optimization of a PV-Wind Hybrid Power Supply Structure with Electrochemical Storage Intended for Supplying a Load with Known Characteristics

An important aspect of the off-grid utilization of hybrid generation systems is the integration of energy storage facilities into their structures, which allows for improved power supply reliability. However, this results in a significant increase in the cost of such systems. Therefore, it is justified to use optimization resulting in the minimization of the selected economic indicator taking into account the most important technical constraints. For this reason, this work proposes an algorithm to optimize the structure of a hybrid off-grid power distribution system (with electrochemical energy storage), designed to supply a load with known daily energy demand. The authors recommend genetic algorithm utilization as well as a modified criterion for evaluating the quality of solutions based on the Levelized Cost of Energy (LCOE) index. Several technical and economic analyses were presented, including unit costs, power distribution of the wind and solar sections, nominal battery capacity, SSSI index (System Self-Sufficiency Index), etc. The model of the system includes durability of the elements which have a significant impact on the periodic battery replacement. The tests were carried out for two types of loads and two types of electrochemical batteries (NMC-Lithium Nickel Manganese Cobalt Oxide; and PbO2-Lead-Acid Battery), taking into account the forecast of an increased lifetime of NMC type batteries and decreasing their price within five years. The proposed synthesis method of photovoltaic-wind (PV-wind) hybrid off-line systems leads to limiting the energy capacity of electrochemical storages. Based on the analyses, the authors proposed recommended methods to improve (reduce) the value of the criterion index (LCOE) for PV-wind off-grid systems while maintaining the assumed level of power supply reliability.Fil: Kasprzyk, Leszek. Poznań University of Technology; PoloniaFil: Tomczewski, Andrzej. Poznań University of Technology; PoloniaFil: Pietracho, Robert. Poznań University of Technology; PoloniaFil: Nadolny, Zbigniew. Poznań University of Technology; PoloniaFil: Mielcarek, Agata. Poznań University of Technology; PoloniaFil: Tomczewski, Krzysztof. Opole University of Technology; PoloniaFil: Trzmiel, Grzegorz. Poznań University of Technology; PoloniaFil: Alemany, Juan Manuel. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Departamento de Electricidad y Electrónica. Instituto de Protecciones de Sistemas Eléctricos de Potencia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentin

Multipass Turning Operation Process Optimization Using Hybrid Genetic Simulated Annealing Algorithm

For years, there has been increasing attention placed on the metal removal processes such as turning and milling operations; researchers from different areas focused on cutting conditions optimization. Cutting conditions optimization is a crucial step in Computer Aided Process Planning (CAPP); it aims to select optimal cutting parameters (such as cutting speed, feed rate, depth of cut, and number of passes) since these parameters affect production cost as well as production deadline. This paper deals with multipass turning operation optimization using a proposed Hybrid Genetic Simulated Annealing Algorithm (HSAGA). The SA-based local search is properly embedded into a GA search mechanism in order to move the GA away from being closed within local optima. The unit production cost is considered in this work as objective function to minimize under different practical and operational constraints. Taguchi method is then used to calibrate the parameters of proposed optimization approach. Finally, different results obtained by various optimization algorithms are compared to the obtained solution and the proposed hybrid evolutionary technique optimization has proved its effectiveness over other algorithms