Multi-objective Intelligent Optimization Research Articles

Performance response analysis and optimization for integrated renewable energy systems using biomass and heat pumps: a multi-objective approach

The focus of this study is to optimize the exploration of biomass-driven multi-energy systems, which include combined heat, power, and gas generation. The objective is to enhance the thermal, environmental, and economic performance indicators of the system. The optimization objectives encompass the quantities of internal combustion engines and air source heat pumps, as well as the dimensions of tanks utilized for anaerobic fermentation. A mathematical model was developed to optimize multiple objectives for combined heat, power, and gas generation systems by employing multi-objective intelligent optimization algorithms. The validation and analysis were conducted using rural residences in Lanzhou, Gansu Province, China, as a case study. The sensitivity analysis of biomass gasification combined heat and power systems was conducted from both technical and cost perspectives, examining the dynamic impact characteristics on the outcomes of multi-objective optimization. The findings indicate that the annual energy-saving rate of the optimized combined generation system decreased from 3.62% to -6.78%, while the growth in carbon emissions reduction rate increased from 76.05 to 81.38%, and the annual cost-saving rate grew from 0.97 to 14.96%. The power generation efficiency of the cogeneration station and hydraulic retention time were found to have a significant impact on the multi-objective optimization results of the combined generation system among the technical parameters. The unit cost of anaerobic fermentation tanks had a more significant impact on the multi-objective optimization results in terms of cost parameters, compared to the price of biogas residue.

Hybrid wind speed optimisation forecasting system based on linear and nonlinear deep neural network structure and data preprocessing fusion

Wind speed time series forecasting has been widely used in wind power generation. However, the nonlinear and non-stationary characteristics of wind speed make accurate wind speed forecasting a difficult task. In recent years, the rapid development of artificial intelligence and machine learning technology provides a new solution to the problem of wind speed forecasting. Combining the advance of artificial intelligence and data analysis strategy, this paper proposes a wind speed forecasting system based on multi-model fusion and integrated learning. Considering the differences in data observation and training principles of various algorithms and exploiting the advantages of each model, a wind speed forecasting system with multiple machine learning algorithms embedded in integrated learning is constructed, which includes three modules: data preprocessing, optimization and forecasting. The data preprocessing module can conduct quantitative analysis through input data decomposition and feature extraction, and the combination of multi-objective intelligent optimization algorithm and combined forecasting method can effectively forecast the wind speed time series. The validity of the algorithm is verified using the data of Shandong wind farm in China. The forecasting results show that compared with the traditional single model forecasting, the proposed integrated wind speed forecasting system based on the fusion of multiple models has higher forecasting accuracy.

Multi-Objective Intelligent Optimization Design Method of Microstrip Antenna Based on Back Propagation Neural Network

Multi-Objective Intelligent Optimization Design Method of Microstrip Antenna Based on Back Propagation Neural Network

Optimal operation and control of hybrid power systems with stochastic renewables and FACTS devices: An intelligent multi-objective optimization approach

This paper delves into the increasingly complex domain of Optimal Power Flow (OPF) within modern power systems, enhanced by the integration of unpredictable renewable energy sources. The research originally integrates stochastic photovoltaic and wind energy sources, along with a suite of Flexible AC Transmission System (FACTS) components – including thyristor-controlled series compensators, static VAR compensators, and thyristor-controlled phase shifters. The primary objective is to solve the OPF problem by reducing generation costs while accommodating the variable nature of renewable energy sources and load demands. This study prioritizes the examination of both constant and fluctuating load requirements. The inherent variability of PV and wind energy, along with load demand, is captured through the modelling of probability density functions. This approach enables a more detailed optimization process, incorporating not just the cost of thermal energy generation but also the scheduling costs of renewable sources and associated penalty costs. Moreover, the study examines the strategic placement and sizing of FACTS components, an aspect essential in minimizing the overall cost of power production. Employing both single- and multi-objective optimization algorithms, the research addresses the OPF problem in a modified IEEE-30 bus system through various case studies. The application of the recently developed flow direction algorithm, including its multi-objective variant with an ε-based constraint-handling mechanism to OPF problem is the primary contributions of this work. The results, benchmarked against several advanced metaheuristic algorithms, reveal the proposed algorithm's superior performance. This comprehensive study not only underscores the potential of integrating renewable energy sources into the grid but also highlights the efficacy of intelligent optimization strategies in managing the complexities of modern power systems.

Intelligent multi-objective optimization of 3D printing low-carbon concrete for multi-scenario requirements

This study presents, for the first time, a universal theoretical framework based on machine learning (ML) algorithms for the multi-objective optimization (MOO) design of 3D printing low-carbon concrete to meet the sustainability requirements of the construction industry. The study focuses on the simultaneous reduction of carbon emissions and costs during the design process. Specifically, the applicable scenarios are primarily classified into two categories: meet aesthetics requirements (printing layer height error-HE and printing layer width error-WE approaching 0) and strengths requirements (compressive strength-CS approaching 60 MPa and interlayer bonding strength-IBS as high as possible). Through a comparison with the traditional design method, 3D printing concrete based on the intelligent design method achieves a 44% reduction in costs and a 19% reduction in carbon emissions while meeting aesthetics requirements. Under strengths requirements, there is a 57% reduction in costs and a 22% reduction in carbon emissions. This demonstrates the indispensable and crucial role of the MOO method employed in this study in achieving the lowest costs and carbon emissions. Additionally, the results of experimental verification demonstrate that the models developed in this study have successfully achieved a close alignment between design values and experimental values within the allowable error range (The errors are 3.46%–9.83%). This not only holds the potential to drive the widespread application of 3D printing technology in the construction industry but also presents new possibilities for cleaner production, contributing to the achievement of sustainable development goals.

Multisource information fusion for real-time optimization of shield construction parameters

This paper introduces a hybrid intelligent framework that combines Bayesian optimization (BO), a random forest (RF) model, and the nondominated sorting genetic algorithm-III (NSGA-III) for the optimization and control of tunnel shield construction parameters. The BO-RF method establishes a nonlinear mapping function between the input variables and three targets, surface settlement, cutter wear, and advance speed, serving as the fitness function for NSGA-III. Model interpretability analysis is conducted using Shapley Additive ExPlanations (SHAP). A multiobjective intelligent optimization model is formulated with NSGA-III, targeting surface settlement, cutter wear, and advance speed. A case study validates the applicability and effectiveness of this approach, leading to the following conclusions: (1) The BO-RF algorithm yields highly accurate prediction results, with R2 values ranging from 0.930 to 0.938, RMSE ranging from 0.138 to 0.172, and MAE ranging from 0.112 to 0.138 for the three targets. (2) The optimization results for surface settlement, cutter wear, and advance speed are outstanding, with an average improvement of 12.56 %. The simultaneous adjustment of the three shield construction parameters leads to the best optimization results, with an average improvement of 19.67 %. (3) The energy consumption of the shield drive system decreases by an average of 10.70 %, and the optimization improvement for the first three objectives decreases by an average of 1.82 %, 1.46 %, and 2.23 %, respectively. By introducing the integrated BO-RF-NSGA-III algorithm, this study contributes to the field of tunnel engineering optimization management.

A novel multivariate combined power load forecasting system based on feature selection and multi-objective intelligent optimization

The ongoing transition in the electricity market, coupled with the rising penetration of renewable energy sources, has spurred demand for enhanced load forecasting accuracy. Traditional single predictors exhibit limited robustness and diversity, rendering them unsuitable for most forecasting situations. Moreover, weather conditions, holidays, and social events significantly influence load demand, making forecasting techniques that account for these factors increasingly relevant. To bridge the gaps and further enhance predictive performance, in this paper, a novel adaptive multivariate combined forecasting system is proposed, incorporating redundant feature elimination, adaptive model selection, multi-objective intelligent optimization with non-negative constraints, and reliable interval prediction theory. The proposed system employs non-linear feature selection to identify effective load features and develops an advanced Multi-Objective Optimization Strategy (MOS) for combining multiple optimal predictors under various scenarios. The weight coefficients of sub-predictors are theoretically demonstrated to be Pareto optimal, and the MOS exhibits superior distribution and convergence. Simulation results reveal that the developed system substantially outperforms benchmark models, achieving reliable uncertainty quantification, which facilitates the rational allocation of spinning reserves and enhances dispatching efficiency.

Enhancing Solar Energy Generation: A Comprehensive Machine Learning-Based PV Prediction and Fault Analysis System for Real-Time Tracking and Forecasting

This paper addresses a notable gap in the field of photovoltaic system forecasting by introducing the Machine Learning-based PV Prediction and Fault Analysis System (ML-PVPFAS). This framework is designed to optimize the decomposition of variational systems automatically, using a multi-objective intelligent optimization method to establish its weight. The paper evaluates a range of Machine Learning (ML) and Ensemble Learning (EL) techniques for diagnosing faults in PV arrays, with a primary focus on identifying and categorizing complex faults that could affect these arrays. These faults encompass multiple anomalies and defects with similar current-voltage curves, which have not been previously examined. The analysis of prediction scores reveals that the ML-PVPFAS approach outperforms other methods, with the lowest Mean Absolute Percentage Error (MAPE) at 4.93, Root Mean Squared Error (RMSE) at 4.64, high Accuracy at 90.31%, Precision at 93.60%, a strong Pearson Correlation Coefficient of 0.90, and a fast Computation Time of 77.64 milliseconds. The results suggest that ML-PVPFAS is a dependable and practical algorithm for predicting the power output of PV solar systems, making it a valuable contribution to the field of predictive modeling.

Hybrid ultra-short-term PV power forecasting system for deterministic forecasting and uncertainty analysis

The rapid development of the photovoltaic industry provides a new source of power for the continued operation of the over-consumed energy world. While providing new opportunities for global energy systems, it also poses challenges for power grids. Therefore, it is a priority to fully grasp the characteristics of photovoltaic changes and accurately forecast and analyze them. To enrich the existing research, a novel hybrid prediction system considering meteorological factors is constructed. First, a feature selection module is introduced to select features and assign weights to exogenous meteorological factors, which breaks through the limitations of single-data dimension prediction. Second, shallow and deep learning models are flexibly applied and multi-objective intelligent optimization strategies are introduced to construct deterministic combinatorial prediction models. The module can effectively increase the diversity of prediction models while fully weighing the accuracy and stability of prediction to meet the needs of different information users. Finally, an interval prediction model is constructed to further enrich the PV power prediction system from the perspective of uncertainty analysis. The empirical study is carried out with 5-minute interval data at three sites, and the results show that the hybrid system obtains superior out-of-sample forecasting performance with technical feasibility and general applicability.

Application of hybrid intelligent algorithm for multi-objective optimization of high performance concrete in complex alpine environment highway

Concrete mix proportion affects concrete performance and cost. Therefore, it arouses an increase of interest in the research on performance improvements in concrete through the optimization of the raw material mix proportion, especially in the high cold, high-salinity and other complex environment. In this paper, an intelligent multi-objective optimization framework is constructed based on Bayesian optimization Random Forest (BO-RF) algorithm and fast non-dominated sorting genetic algorithm (NSGA-III). The key factors affecting concrete performance are taken as the input indexes, the freezing resistance, impermeability, carbonization depth and cost are taken as the evaluation indexes of concrete. Based on 400 groups of freeze–thaw cycle of salt invasion experiments in cold environment, the orthogonal experiment method is used to obtain the index system of the original data sets, then, the RF prediction algorithm is adopted to establish the key factors and performance index of a nonlinear mapping relationship and the Bayesian optimization random forest is used to establish a high-precision fitness function. Ultimately, the BO-RF-NSGA-III model is applied for multiobjective optimization. The results show that (1) Based on the proposed framework, the rapid prediction and optimization of concrete performance can be realized, which can well meet the needs of the complex alpine environment (2) Accurate model prediction can be made through BO-RF. The R2 values obtained at the time of prediction range from 0.944 to 0.998, the mean absolute error (MAE) range from 0.001 to 0.09. (3) Based on BO-RF-NSGA-III and grey correlation method, the comprehensive optimal mix proportion of concrete is obtained.

Intelligent multiobjective optimization for high-performance concrete mix proportion design: A hybrid machine learning approach

The concrete mix proportion design process is complex but important, especially in cold, ocean, underground and other complex engineering environments. In this study, a hybrid intelligent optimization method based on the random forest (RF), recursive feature elimination (RFE), Bayesian optimization (BO), least squares support vector machine (LSSVM) and nondominated sorting genetic algorithm (NGSA)-III was proposed to optimize the concrete mix proportion and rapidly and accurately predict the frost resistance, chloride ion penetration resistance and concrete strength (CS). Adopting a key project in Jilin Province as an example, the RF-RFE-BO-LSSVM-NSGA-III algorithm achieved a significant optimization effect in terms of the chloride ion permeability coefficient (CIPC), relative dynamic elastic modulus (RDEM) and 28-day CS. After optimization, the chloride ion penetration resistance, frost resistance and CS increased by 34.6%, 4.1% and 3.7%, respectively, over the average levels of the sample data. This study can provide basis for concrete mix proportion design in complex environment.

Improving the Performance of Hydrological Model Parameter Uncertainty Analysis Using a Constrained Multi-Objective Intelligent Optimization Algorithm

In the field of hydrological model parameter uncertainty analysis, sampling methods such as Differential Evolution based on Monte Carlo Markov Chain (DE-MC) and Shuffled Complex Evolution Metropolis (SCEM-UA) algorithms have been widely applied. However, there are two drawbacks which may introduce bad effects into the uncertainty analysis. The first disadvantage is that few optimization algorithms consider the physical meaning and reasonable range of the model parameters. The traditional sampling algorithms may generate non-physical parameter values and poorly simulated hydrographs when carrying out the uncertainty analysis. The second disadvantage is that the widely used sampling algorithms commonly involve only a single objective. Such sampling procedures implicitly introduce too strong an “exploitation” property into the sampling process, consequently destroying the diversity property of the sampled population, i.e., the “exploration” property is bad. Here, “exploitation” refers to using good already-existing solutions and making refinements to them, so that their fitness will improve further; meanwhile, “exploration” denotes that the algorithm searches for new solutions in new regions. With the aim of improving the performance of uncertainty analysis algorithms, in this research, a constrained multi-objective intelligent optimization algorithm is proposed that preserves the physical meaning of the model parameter using the penalty function method and maintains the population diversity using a Non-dominated Sorted Genetic Algorithm-II (NSGA-II) multi-objective optimization procedure. The representativeness of the parameter population is estimated on the basis of the mean and standard deviation of the Nash–Sutcliffe coefficient, and the diversity is evaluated on the basis of the mean Euclidean distance. The Chengcun watershed is selected as the study area, and uncertainty analysis is carried out. The numerical simulations indicate that the performance of the proposed algorithm is significantly improved, preserving the physical meaning and reasonable range of the model parameters while significantly improving the diversity and reliability of the sampled parameter population.

Optimizing environmental flow based on a new optimization model in balancing objectives among river ecology, water supply and power generation in a high-latitude river

Environmental flow plays an important role in maintaining the health of river ecosystems and aquatic habitats. Although ecological regulation of environmental flow has attracted the attention of scientists, managing the world's reservoir-regulated rivers to better meet the needs of human being and ecosystems is a complex social challenge. To address the above issues, we constructed a model for optimizing reservoir operation based on a balance in achieving multi objectives among environmental flow, water supply and power generation (EWP). The model was solved using an intelligent multi-objective optimization algorithm (ARNSGA-III). The developed model was demonstrated in a large reservoir, Laolongkou Reservoir in the Tumen River. The results showed that the reservoir altered environmental flows mainly in terms of flow magnitude, peak, times, duration and frequency, which result in a sharp decrease in spawning fish, and degradation and replacement of vegetation along the channels. In addition, the mutual feedback relationship between the objectives of environmental flows, water supply and power generation is not static, but varies over time and space. The constructed model based on Indicators of Hydrologic Alteration (IHAs) can effectively guarantee the environmental flow at daily scale. In detail, the river ecological benefit increased by 64% in wet year, 68% in normal year, 68% in dry year after optimizing regulation of reservoir, respectively. This study will provide a scientific reference for the optimizing of the management in other rivers affected by dams.

Intelligent multiobjective optimization design for NZEBs in China: Four climatic regions

Near-zero-energy-consumption buildings (NZEBs) are of great significance for sustainable development, and their design and research have attracted increasing academic attention. To drive and realize the energy saving design and achieve carbon emission and thermal comfort optimization of NZEBs, in this paper, an intelligent optimization method integrating the BIM-DB and PSO-RF-NSGA-III method is established. Multiobjective optimization problems involving NZEBs in four typical climate regions in China are explored. With a typical office building as an example, first, simulation calculations regarding the energy consumption, carbon emissions and indoor thermal comfort in the four climatic regions are performed based on orthogonal tests and BIM-DB. Second, the nonlinear mapping relationships between building design parameters and prediction targets are constructed with the PSO-RF model, which is trained with sample data. The obtained nonlinear mapping relations are used to establish the objective function of NSGA-III, and the multiobjective Pareto-optimal solution set is obtained with the developed PSO-RF-NSGA-III algorithm. Finally, the only optimal solution is determined by using the ideal point method, and reference near-zero-energy-consumption office building parameters are calculated for different climate regions. The conclusions are as follows. (1) The PSO-RF algorithm can efficiently predict building energy consumption, carbon emissions and thermal comfort. In the four regions, the goodness of fit of the three targets is greater than 0.94. (2) Multiobjective optimization can be performed with the proposed RF-NSGA-III intelligent optimization method. After optimizing multiple groups of optimization schemes and adopting energy saving measures, the energy consumption levels in the four climate regions are reduced by 39.72 %, 32.22 %, 26.94 % and 35.37 %, and the other goals are optimized. (3) Index calculations indicate that the optimized building design parameters meet the specified standards for NZEBs, and the main influencing factors and corresponding measures vary from region to region.

Integrated Intelligent Method Based on Fuzzy Logic for Optimizing Laser Microfabrication Processing of GnPs-Improved Alumina Nanocomposites

Studies on using multifunctional graphene nanostructures to enhance the microfabrication processing of monolithic alumina are still rare and too limited to meet the requirements of green manufacturing criteria. Therefore, this study aims to increase the ablation depth and material removal rate and minimize the roughness of the fabricated microchannel of alumina-based nanocomposites. To achieve this, high-density alumina nanocomposites with different graphene nanoplatelet (GnP) contents (0.5 wt.%, 1 wt.%, 1.5 wt.%, and 2.5 wt.%) were fabricated. Afterward, statistical analysis based on the full factorial design was performed to study the influence of the graphene reinforcement ratio, scanning speed, and frequency on material removal rate (MRR), surface roughness, and ablation depth during low-power laser micromachining. After that, an integrated intelligent multi-objective optimization approach based on the adaptive neuro-fuzzy inference system (ANIFS) and multi-objective particle swarm optimization approach was developed to monitor and find the optimal GnP ratio and microlaser parameters. The results reveal that the GnP reinforcement ratio significantly affects the laser micromachining performance of Al2O3 nanocomposites. This study also revealed that the developed ANFIS models could obtain an accurate estimation model for monitoring the surface roughness, MRR, and ablation depth with fewer errors than 52.07%, 100.15%, and 76% for surface roughness, MRR, and ablation depth, respectively, in comparison with the mathematical models. The integrated intelligent optimization approach indicated that a GnP reinforcement ratio of 2.16, scanning speed of 342 mm/s, and frequency of 20 kHz led to the fabrication of microchannels with high quality and accuracy of Al2O3 nanocomposites. In contrast, the unreinforced alumina could not be machined using the same optimized parameters with low-power laser technology. Henceforth, an integrated intelligence method is a powerful tool for monitoring and optimizing the micromachining processes of ceramic nanocomposites, as demonstrated by the obtained results.

Multi-Objective Parametric Optimization Design for Mirrors Combined with Non-Dominated Sorting Genetic Algorithm

The process of intelligent multi-objective parametric optimization design for mirrors is discussed in detail in this paper, with the error of the mirror surface shape and the total mass being examined as the optimization objectives. The establishment of complex objective functions for solving the optimization problem of the mirror surface shape error was realized, and manual modification of the model was avoided. Moreover, combining this with a non-dominated sorting genetic algorithm (NSGA) helped the Pareto front move towards an ideal optimal set of solutions. To verify the effectiveness of the proposed method, an aluminum alloy mirror with an aperture of 140 mm was taken as an example. The Pareto optimal solution set of the mass and surface shape error under 1 g gravity was obtained for finding the required solution and satisfying the optimization goal. In addition, this method is applicable to other complex structural design problems.

Sequential Subspace Optimization Design of a Dual Three-Phase Permanent Magnet Synchronous Hub Motor Based on NSGA III

The multiobjective optimization design of dual three-phase permanent magnet synchronous hub motors (PMSHMs) is challenging due to the high dimension and huge computation cost of finite-element analysis (FEA). A new multiobjective optimization strategy is proposed for dual three-phase PMSHMs in this article. All design parameters are divided into two subspaces according to the Pearson sensitivity analysis results to improve optimization efficiency. A new training method is adopted to improve the accuracy of the approximate model. By improving a multiobjective intelligent optimization algorithm, nondominated sorting genetic algorithm (NSGA) III, a new algorithm is proposed, which will greatly shorten optimization time. It is found that the proposed optimization method can significantly improve the performance, such as smaller torque ripple and higher maximum torque for the investigated PMSHM, while the computation resources are reduced. A prototype based on the optimization results is manufactured, and experiments are conducted on the platform to verify the accuracy of the optimization results and the FEA. The effectiveness of optimization and the accuracy of the simulation are verified by the experimental results.

Radiation shielding optimization design research based on bare-bones particle swarm optimization algorithm

In order to further meet the requirements of weight, volume, and dose minimization for new nuclear energy devices, the bare-bones multi-objective particle swarm optimization algorithm is used to automatically and iteratively optimize the design parameters of radiation shielding system material, thickness, and structure. The radiation shielding optimization program based on the bare-bones particle swarm optimization algorithm is developed and coupled into the reactor radiation shielding multi-objective intelligent optimization platform, and the code is verified by using the Savannah benchmark model. The material type and thickness of Savannah model were optimized by using the BBMOPSO algorithm to call the dose calculation code, the integrated optimized data showed that the weight decreased by 78.77%, the volume decreased by 23.10% and the dose rate decreased by 72.41% compared with the initial solution. The results show that the method can get the best radiation shielding solution that meets a lot of different goals. This shows that the method is both effective and feasible, and it makes up for the lack of manual optimization.

Multi-Objective Optimization Design Method for Whole-Aeroengine Coupling Vibration

With the increasing thrust-to-weight ratio of modern aeroengines and the widespread adoption of thin-walled casing, the impact of the coupling vibration effect between the rotor and stator on the critical speed, rotor–stator mode shape coordination, and response characteristics of the whole engine have become increasingly prominent. However, the vibrational design of the whole-engine coupling has thus far relied on human experience, revealing a need for the study of its intelligent optimization. In this study, to quantitatively analyze the vibration of the whole engine, three vibration evaluation indexes were defined, which were risk coefficients for the critical speed, rotor strain energy, and cross-section rotor–stator rubbing. Using these indexes as the optimization targets, a multi-objective intelligent optimization design method for aeroengine support stiffness was proposed. The design optimization of the support stiffness of a turbofan engine with a large bypass ratio was performed. The results showed that the vibration index of the whole machine can be reduced to different degrees and that the whole-engine coupling vibration is optimized.

The multi-parameter optimization of injections on double-layer diesel engines based on genetic algorithm

In this paper, a coupling model of the NSGA-II genetic algorithm and KIVA-3 V program is developed to perform a multi-objective intelligent optimization study of the injection parameters in a diesel engine with double-layer diverging combustion (DLDC) chamber. Four optimization parameters, involving the first injection timing, the first fuel split ratio, the injection timing interval, and the exhaust gas recirculation (EGR) ratio, are optimized after comparing the pollutant emissions and fuel consumption characteristics. The computation results reveal that both soot and fuel consumption function as the optimization objectives, proving a trade-off relationship with the NOx emission. The analysis of the response surface demonstrates that the application of the multiple-injection strategies is beneficial for enhancing the performance of diesel engines. A large first fuel split ratio and a low EGR rate can minimize both ISFC (indicated specific fuel consumption) and soot emission concurrently. A high EGR rate can significantly reduce NOx emission. In addition, the distribution of the final Pareto font shows that 66.7 % percent of the first fuel split ratio is above 0.56, and only two solutions (8.3 %) have values below 0.35. Furthermore, three representative points of the first fuel split ratio are selected for combustion analysis, which are 0.05, 0.35, and 0.88. As the first fuel split ratio rises, the high-temperature areas in the cylinder are gradually enlarged, and the NOx emission is increased while the soot emission and ISFC are further reduced.