Multi-objective Particle Swarm Algorithm Research Articles

A Web Data Mining Algorithm Based on Manifold Distance for Mixed Data in Cloud Service Architecture

Due to the complex distribution of web data and frequent updates under the cloud service architecture, the existing methods for global consistency of data ignore the global consistency of distance measurement and the inability to obtain neighborhood information of data. To overcome these problems, we transform the multi-information goal and multi-user demand (constraint conditions) in web data mining into a constrained multi-objective optimization model and solve it by a constrained particle swarm multi-objective optimization algorithm. While we measure the distance between data by manifold distance. In order to make it easier for the constrained multi-objective particle swarm algorithm to solve different types of problems to find an effective solution set closer to the real Pareto front, a new manifold learning algorithm based on the constrained multi-objective particle swarm algorithm is built and used to solve this problem. Experiments results demonstrate that this can improve the service efficiency of cloud computing.

Optimization of Renewable Energy Hydrogen Production Systems Using Volatility Improved Multi-Objective Particle Swarm Algorithm

Optimizing the energy structure to effectively enhance the integration level of renewable energy is an important pathway for achieving dual carbon goals. This study utilizes an improved multi-objective particle swarm optimization algorithm based on load fluctuation rates to optimize the architecture and unit capacity of hydrogen production systems. It investigates the optimal configuration methods for the architectural model of new energy hydrogen production systems in Xining City, Qinghai Province, as well as the internal storage battery, ALK hydrogen production equipment, and PEM hydrogen production equipment, aiming at various scenarios of power sources such as wind, solar, wind–solar complementary, and wind–solar–storage complementary, as well as intermittent hydrogen production scenarios such as hydrogen stations, hydrogen metallurgy, and continuous hydrogen production scenarios such as hydrogen methanol production. The results indicate that the fluctuation of hydrogen load scenarios has a significant impact on the installed capacity and initial investment of the system. Compared with the single-channel photovoltaic hydrogen production scheme, the dual-channel hydrogen production scheme still reduces equipment capacity by 6.04% and initial investment by 6.16% in the chemical hydrogen scenario with the least load fluctuation.

Electrical Machine Winding Performance Optimization by Multi-Objective Particle Swarm Algorithm

The present work aims to optimize the magnetomotive force and the end-winding leakage inductance from a discrete distribution of conductors in electrical machines through multi-objective particle swarm heuristics. From the development of an application capable of generating the conductor distribution for different machine configurations (single or poly-phase, single or double layer, integral or fractional slots, full or shortened pitch, with the presence of empty slots, etc.) the curves of magnetomotive force and the end-winding leakage inductance associated with the winding are computed. Taking as an optimal winding the one that presents, simultaneously, less harmonic distortion of the magnetomotive force and less leakage inductance, optimization by multi-objective particle swarm was used to obtain the optimal electrical machine configuration and the results are presented.

Multi-objective arterial coordination control method based on induction control and vehicle speed guidance

The fixed green wave speed and staged statistical flow used in arterial signal coordination are not adaptable to the fluctuations in vehicle travel speed and traffic flow on roads, resulting in a mismatch between the signal scheme and the optimal green wave speed and traffic flow demand. This discrepancy negatively impacts the efficiency of intersection traffic. In traditional signal control systems, the cycle and green light timing are typically set independently. However, such a setting method poses problems in practical operation. In this paper, we combine vehicle arrival and vehicle location information, and consider the interaction of speed guidance and dynamic signal optimization to construct a model. This study is developed along the following steps: in the vehicle-road coordination environment, based on the MAXBAND model, a global coordination scheme is obtained, incorporating the speed guidance method; then, based on the vehicle saturation of the inlet lane of the arterial intersection, a multi-objective optimization model for arterial signal coordination under vehicle speed guidance is established based on global coordination with the maximum green wave bandwidth and the minimum delay of arterial vehicles, the minimum number of arterial stops and the minimum delay in the minor direction road as the optimization objectives. Based on global coordination, adopting an integrated control mechanism of cycle and green light timing allows for dynamic adjustments according to real-time traffic conditions. The improved multi-objective particle swarm algorithm is chosen to solve the model, and the simulation environment is built based on the COM interface of VISSIM software and C# platform. Three adjacent intersections of Ganjiang Middle Road in Nanchang are selected as case studies, and the methods in this paper are compared with the current timing scheme, the MAXBAND method and the optimization scheme under speed guidance only, respectively. The results show that the model proposed in this paper achieves significant optimization effects on the indicators of arterial delay, arterial stopping times and the delay of minor roads.

Electric vehicle charging optimization strategy based on the mopso algorithm

Aiming at the problems of electric vehicle disorderly charging on grid load stability and charging cost, this study considers the grid load pressure and presents a multi-faceted optimized model for electric vehicle charging. The model is addressed by utilizing a particle swarm optimization algorithm designed for multiple objectives. The outcomes demonstrate that the charging framework built upon the multi-objective particle swarm algorithm has a fast convergence speed and can avoid the limitation of the local optimal solutions. Under the premise of reducing by managing the grid load fluctuation, the model effectively curbs the expense of vehicle charging while also minimizing peak-to-valley disparities in grid load.

A Modified Particle Swarm Algorithm for the Multi-Objective Optimization of Wind/Photovoltaic/Diesel/Storage Microgrids

Microgrids have been widely used due to their advantages, such as flexibility and cleanliness. This study adopts the hierarchical control method for microgrids containing multiple energy sources, i.e., photovoltaic (PV), wind, diesel, and storage, and carries out multi-objective optimization in the tertiary control, i.e., optimizing the economic cost, environmental cost, and the degree of energy utilization of microgrids. As the traditional multi-objective particle swarm algorithm is prone to local convergence, this study introduces variable inertia weight and learning factors to obtain a modified particle swarm algorithm, which is more advantageous in multi-objective optimization. Compared to the traditional particle swarm algorithm, the modified particle swarm algorithm increased the photovoltaic absorbed rate from 0.7724 to 0.8683 and the wind energy absorbed rate from 0.6064 to 0.7158 in one day, which resulted in an increase in energy utilization by 14.89%, and a reduction in financial environmental costs from RMB 135,870 to RMB 132,230. The simulation of the optimization effect of the conventional particle swarm algorithm and the modified particle swarm algorithm on the microgrid were carried out, respectively, in MATLAB, which verifies the advantage of the modified particle swarm algorithm on the optimization of microgrids. Then, the optimization results, i.e., the data of the power scheduling process of the four power sources, were made into a table and imported into the microgrid model in Simulink. The simulation results indicated that the microgrid was able to output stable voltage, current, and frequency. Finally, the changes in microgrids affected by the external environment were further investigated from the aspects of the market environment and natural environment. Moreover, we verified the presence of a contradiction between the optimization of the microgrid economy and environmental protection. Thus, microgrids need to adjust their optimization focus according to the natural conditions in which they are located.

A Study of Strategies and Methods for the Application of Sports Nutrition in Fitness Training

Abstract Dietary habits are particularly important in fitness training, and a scientific fitness diet needs to be formulated according to the fitness user’s situation, fitness goals and exercise volume. In this paper, a multimodal personalized sports nutrition recommendation model incorporating users’ visual preferences is designed to address the important impact of visual features on the task of sports nutrition recommendation. The user’s visual preference is modeled using the Query-Key-Value attention mechanism, which extracts valuable visual information from their historical data and adds it to textual features. In the sports nutrition program generation part, guided by sports nutrition theory and based on the MMFV model, the sports nutrition program generation method was designed. Then, the multi-objective optimization problem of the sports nutrition scheme is planned and modeled, and a calorie-checking mechanism is added to the multi-objective particle swarm algorithm for the problem of calorie intake not meeting the fitness goal. According to the sports nutrition program recommendation algorithm to improve the three meals diets of the gym participants, through the analysis of relevant data to verify the scientificity of the recommended program. The average intake of protein, vitamin A, vitamin B1 and vitamin C of the fitness participants after the modification increased by 7.4%, 17.77%, 22.33% and 8.46%, respectively, compared with that before the modification. Scapular sebaceous thickness and abdominal sebaceous thickness were reduced by 2.2 mm and 1.47 mm, respectively, compared with the pre-modification period.TC, TG and LDL metabolism were in the normal range, which was significantly different from the pre-modification period (P<0.05).

The design and operation optimization for conductive rubber heated snow melting bridge deck pavement

In order to solve the problem of ice and snow disasters on roads, this study proposed a new technology for active deicing of bridge decks using conductive rubber heating system (CRHS) as the heat source. The existing measures to improve the operation efficiency of snowmelt pavement often only optimize 1–2 influencing factors, and lack of optimization of multiple factors such as paving design, operation and environment. Therefore, in order to improve the operation efficiency, this study carried out multi-objective optimization on the design and operation of conductive rubber heated snow melting bridge deck pavement. The bridge deck pavement scheme of conductive rubber heating system was proposed, and its two-dimensional numerical model of heat transfer was established. The laying spacing of conductive rubber (s), heating power (p), ambient temperature (t) and wind speed (v) were used as input parameters for system operation optimization. Predictive models for total heating time (THT) and the loss of energy rate (LER) were developed using response surface methodology (RSM). The specific effects of input parameters on THT and LER and their interactions were analyzed by thermal simulation. Aiming at less THT and lost energy (LE), the optimal input parameters of conductive rubber heating system were obtained by the multi-objective particle swarm algorithm. The results indicated that shorter THT and lower LE could be achieved when the laying spacing (s) was 0.25 m, the ambient temperature (t) was −3 °C, the wind speed (v) was 2 m/s, and the input power was 550 W/m2. And adjusting s was more effective than adjusting p to obtain low THT and LER. The results and recommendations can effectively guide the operation of conductive rubber heating system. The multi-objective optimization research idea of improving the operation efficiency of snowmelt pavement in this paper can provide reference for similar deicing pavement applications.

Research on wind-solar-storage system optimal scheduling based on multi-objective particle swarm algorithm

For the current problems that economic benefit and energy saving can not be considered in the existing optimal scheduling schemes of wind-solar-diesel-storage system, this paper proposes an optimal scheduling scheme of this system based on multi-objective particle swarm optimization. The mathematical models of active power output of wind turbines, solar turbines and energy storage units of wind-solar-diesel-storage system are established respectively. According to the historical data of wind power generation and photovoltaic power generation, the power prediction data of corresponding prediction models are adopted respectively On this basis, the objective function is constructed by minimizing the operating cost of the system and maximizing the wind-solar consumption ratio, and the system active power, energy storage battery capacity and diesel generator power are taken as constraints Using multi-objective particle swarm optimization algorithm to solve the output of each unit in the system. Simulation results illustrate the optimization scheme can well achieve the target of taking into account both economic benefits and energy saving in the optimal scheduling of wind-solar-diesel-storage system.

Research on Bionic Hierarchical Optimization of Wing Based on PLSR and PSO

The layout of the wing's internal structure not only dramatically influences the wing's strength stiffness but also directly affects the aerodynamic characteristics of the aircraft. Based on the original wing structure, a more flexible spatial design layout to achieve improved overall structural load-bearing performance, and a reasonable structural lightweight design are the research priorities to be considered for the development of future aircraft. Therefore, this paper attempted to design and analyze a lightweight airfoil that meets the performance requirements. Combining the strategy of hierarchical optimization design with the advantages of engineering bionics, the diatom Arachnoidiscus bionic structure, topological optimization, partial least squares regression (PLSR), and multi-objective particle swarm algorithm (PSO) are applied to optimize the placement and size of wing’s internal components. The simulation results show that the weight of the optimized wing structure is reduced by 6% while satisfying the requirements of maximum stress and maximum deformation.

Construction of a coupled evaluation index system of rural habitat environment and rural tourism in China based on multi-objective optimization algorithm

Abstract This paper presents a coupled evaluation of rural habitat environment as well as rural tourism in China based on a multi-objective optimization algorithm. Firstly, the ANN model is used as the three objective evaluation function values of CVBEC, LCC and LCCO2 as the dependent variables, and the NSGA-II algorithm is introduced as the optimization algorithm for the multi-objective optimization of rural architectural design. Secondly, based on the ANN-NSGA-II algorithm, an improvement strategy is proposed for the rural building environment algorithm, and the multi-objective discrete particle swarm algorithm with a dominant ranking number is applied to rural tourism route planning. Then the coupled evaluation system of rural habitat environment and rural tourism is constructed by first and second-level indicators. Finally, the coordination degree and spatial autocorrelation analysis of the multi-system coupling relationship are conducted for the coupling evaluation system. The study shows that the coordinated development degree P value increased from 0.02 in 2017 to 0.83 in 2022, Moran’s I index of living environment and ecological environment system is greater than 0, and the Z value is greater than 1.63.

Probabilistic prediction of wind speed using an integrated deep belief network optimized by a hybrid multi-objective particle swarm algorithm

An improvement in wind speed prediction is highly necessary for estimating the accuracy as well as stability of wind power. In this work, we proposed probabilistic forecasts of wind speed for predicting the short-term wind speed intervals. The optimal model has been designed by considering three different modules such as data decomposition, prediction, and optimization. Variational-Mode-Decomposition (VMD) is utilized for decomposing the primary time series data into a suitable number of modes followed by the Deep Belief Network (DBN) for probabilistic wind sped prediction. Here the Gauss-Bernoulli restricted Boltzmann machine (GBRBM) and Bernoulli-Bernoulli RBM (BBRBM) are combined in the DBN where the GBRBM is utilized as the initial RBM to convert the continuity feature of the source data into a binomial distribution feature. Multi-kernel-random-vector-functional-link-network (MKRVFLN) is employed here as supervised learning in DBN to avoid long execution time and get the model into local optima. In the model optimization, a hybrid multi-objective Sine-Cosine particle-swarm-optimization (MOSCPSO)is used to optimize the DBN parameters for obtaining high accuracy and strong stability output simultaneously. It determines the wind speed at 95%, 90%, 85%, and 80% prediction interval nominal confidence (PINC). To validate the proposed model and comparing with other benchmark prediction techniques, the data are taken from the wind farm located at Sotavento, Spain, at different time horizons (30 min–1 h) in different seasons. The results obtained demonstrate that the proposed technique outperforms the further existing model on the basis of prediction accuracy and stability.

A Two-Stage Optimal Preventive Control Model Incorporating Transient Stability Constraints in the Presence of Multi-Resource Uncertainties

The volatility and uncertainty introduced by increasingly integrated renewable energy pose challenges to the reliable and stable operation of the power system. To mitigate the operation risks, a two-stage optimal preventive control model that incorporates transient stability constraints and considers uncertainties from multiple resources is proposed. First, the uncertainties of different re-sources are modeled, with which the non-sequential Monte Carlo sampling method is used to correspondingly generate the scenarios. Thereafter, a two-stage control model that balances operational safety and economy and realizes preventive control and emergency control is built. The operation schedule from the preventive control stage aims to minimize the transient stability probability and operation costs. If any faults destabilize the system, the emergency control stage will be activated immediately to help the system recover stability with minimal control costs. To expedite the solving of the two-stage model, a multi-objective particle swarm algorithm based on entropy-TOPSIS is proposed. Finally, the effectiveness of the proposed model and solving algorithm are validated with the modified IEEE118 node system.

Parameter Tuning for WECC Generic Photovoltaic System Models Using Latin Hypercube Sampling and Pareto Optimality

With attention increasing towards worldwide ecological conservation, the energy policies of various countries are moving towards renewable energy systems, primarily with solar and wind as the primary power sources. The Western Electricity Coordinating Council (WECC) has developed a generic model as a simulation framework for a typical solar wind generation system to analyze the response of the actual equipment. In this paper, we propose a parameter tuning process for a generic model controller that combines Latin hypercube sampling and Pareto optimization to enable a generic model to effectively match the output of an actual inverter device. Different operating scenarios were considered to match the dynamic characteristics of the inverter device using the multi-objective particle swarm algorithm (MOPSO) along with Pareto optimization. The effectiveness of the proposed parameter tuning process was verified on a user-defined test system and, later, the IEEE 39 bus system with a large solar power facility.

Multi-criteria assessment and triple-objective optimization of a bio-anode microfluidic microbial fuel cell

Microfluidic microbial fuel cell has lower costs and greater potential than typical microbial fuel cell due to the elimination of proton exchange membrane. However, the development has mostly relied on experiments, and there has been little research on numerical simulations. Based on experimental validation, a reliable and universal model for microfluidic microbial fuel cell without quantifying the biomass concentration is proposed. Subsequently, the primary work is to study the output performance and energy efficiency of the microfluidic microbial fuel cell under different operating conditions and to comprehensively optimize the cell performance by employing the multi-objective particle swarm algorithm. Compared the optimal case with the base case, the increase ratios of maximum current density, power density, fuel utilization and exergy efficiency are 40.96%, 20.87%, 61.58% and 32.19%, respectively. On the basis of improving energy efficiency, the maximum power density and current density can reach 1.193 W/m2 and 3.51 A/m2.

Multiobjective Demand Double-Layer Energy Consumption Optimization Strategy for Microgrid Based on Improved HPSOFA

In order to optimize the economy and environmental protection of microgrid, this paper establishes a demand response model based on comprehensive satisfaction, combines the advantages of the classical multiobjective particle swarm algorithm and multiobjective firefly algorithm, and proposes a hybrid particle swarm optimization and firefly algorithm (HPSOFA) to solve the joint economic and environmental dispatch problem of microgrid and improve the wind and light consumption capacity. The proposed improved algorithm introduces a random perturbation term and adaptive learning coefficients, and the algorithm is selected by the dominance relationship of individuals, which improves the diversity of populations and increases the possibility of the algorithm to solve the global optimum. The proposed algorithm is used to solve the test functions of different dimensions, and the results show that the Pareto front of HPSOFA has better distribution and accuracy, which verifies the effectiveness of the proposed algorithm. Simulation analysis is carried out using power data from a region of Liaoning Province, China, and experiments conclude that the operating cost and environmental cost of HPSOFA are significantly lower than other algorithms.

Optimal Scheduling of DC Microgrids based on MOPSO Algorithm

The DC microgrid system contains a variety of distributed power sources, but the distributed power sources are uncertain and intermittent. In order to improve the economy of DC microgrid operation, mainly to reduce the microgrid operation cost and environmental management cost, and at the same time the largest proportion of scenery consumption, the optimization algorithm is effective for microgrid for optimal scheduling. A multi-objective optimal scheduling model for microgrids is established. A microgrid model with wind, photovoltaic, micro gas turbine and energy storage is constructed and solved using a multi-objective particle swarm algorithm. The multi-objective particle swarm algorithm is invoked for a typical 24h daily isolated micro-grid system in a certain region. The simulation results show that the optimal dispatching scheme can effectively improve the economy of the microgrid, reduce its operating costs as well as the ability to consume renewable energy scenery power.

A combined control method of traction and ballast for an electric tractor in ploughing based on load transfer

Agricultural tractors are significant agricultural power machines that must be equipped with different implements according to tillage requirements and operate in a complex field environment. Aiming at the problem that the traditional traction control system could not balance the agronomic requirements of tillage and high-efficiency operation, this study proposed a combined control method of traction and ballast based on load transfer. This method was used for electric tractors with a battery position adjustment (BPA) structure. The tractor-implement system model was built to calculate the dynamic loads of front and rear axles accurately. Then, a multi-objective optimization function for traction performance was constructed, and the multi-objective particle swarm algorithm was employed to solve the optimal battery pack target position. The tractor controller real-time measured the draught force and battery position data. Based on the combined control strategy, the battery pack position and the tillage depth were adjusted to accomplish the cooperative control of the active ballast system and the electro-hydraulic hitch system. Finally, a field test platform for the electric tractor was developed. Ploughing tests under the traction control method and combined control method were performed. The test results showed that the tillage depth uniformity and traction performance under the combined control method were superior to the traction control method. In particular, compared with the traction control method, the tillage depth fluctuation range in the combined control method was reduced by 30.6%, the wheel slip was decreased by 15.1%, the traction efficiency was enhanced by 3.7% and the total motor energy loss was decreased by 4.9%. This study provides a technical reference for improving the economy of energy consumption and the uniformity of tillage depth of tractors in ploughing.

Carbon emission and economic development trade-offs for optimizing land-use allocation in the Yangtze River Delta, China

Reconciling carbon emissions and GDP development is a challenge for most Chinese cities in the sustainable development process. Taking the Yangtze River Delta of China as an example, this study calculated carbon-emission inventories for five typical years and assigned anthropogenic carbon emissions to land-use types. A multi-objective particle swarm algorithm was used to consider three functions of land-use allocation: maximizing GDP, minimizing carbon emissions, and maximizing suitability. Thereafter, the technique for order of preference by similarity to ideal solution(TOPSIS) method was combined to find the optimal solution for carbon emission and economic development trade-offs. It is observed that Jiangsu has the largest carbon emissions in the Yangtze River Delta region; energy consumption is the main source of the carbon emissions, and industrial land is the most carbon intensive region. The land-use allocation obtained by multi-objective optimization with the TOPSIS evaluation method increases GDP and reduces carbon emissions, which is worthy of reference for land planners. Land use optimization from the perspective of balancing carbon emissions and economic development can provide a useful reference for the rational use of land resources and socio-economic sustainable development.

Joint bandwidth allocation and task offloading in multi-access edge computing

In recent years, multi-access edge computing (MEC) has become a hot topic. With its distributed characteristics, MEC provides more possibilities for delay-sensitive tasks. In this paper, we study a task offloading problem to shorten task delay. The problem consists of two aspects, bandwidth allocation and task offloading decision-making. Based on alliance game, we formulate bandwidth allocation to minimize the dissatisfaction of alliances. Game participants are all users. We take into account the dissatisfaction of each alliance and find the least dissatisfaction of the alliance. Then, we formulate the task offloading decision-making to minimize task delay. Task delay consists of communication delay and execution delay. Computing and storage capacity are treated as limiting conditions for decision-making. To solve the offloading problem, we convert the dissatisfaction of alliance into a vector, and obtain the Pareto optimal through multi-objective particle swarm algorithm. Then, we use Branch and Bound method to construct the propagation tree to facilitate decision-making. To evaluate the edge servers in the tree, we build an evaluation matrix and transform the matrix to a set of evaluation index which is used on task offloading decision-making. A large number of experimental results show that our algorithm is better than compared algorithm.