Multi-objective Optimization Method Research Articles

A Fast Multi-Objective Optimization Method for Control Parameters of High-Speed Maglev Vehicle-Bridge System

A fast multi-objective optimization method (FMOOM) is proposed by optimizing control parameters to improve the dynamic performance of a high-speed maglev vehicle–bridge system. This approach involves generating the corresponding dynamic response to the sampled control parameters using a theoretical model of a high-speed maglev vehicle–bridge system, followed by establishing an adaptive surrogate model for the relationship between the control parameters and the dynamic response extrema. In the second step, we combine the adaptive surrogate model and the multi-objective gradient-based optimizer (MOGBO) algorithm to obtain the Pareto solution set satisfying different performance indexes. Additionally, the control parameters are optimized using the fuzzy comprehensive evaluation method. In the numerical simulation, we investigate five maglev trains and ten-span simply supported beam bridges and the theoretical model is verified by comparing the calculations with the measured results. The optimization effect of FMOOM is analyzed under different working conditions. The results show that the adaptive surrogate model has good prediction accuracy based on the radial basis function. Furthermore, the Pareto solution distribution of different schemes using FMOOM is reasonable, and the optimization results are as expected. Compared with the reference scheme, the dynamic response of the maglev vehicle–bridge system is smaller after being subjected to FMOOM optimization, and the six performance indexes are dramatically improved.

Smart grid stability prediction model using two-way attention based hybrid deep learning and MPSO

In smart grid management, precise stability prediction is a complicated task that adds to the effective allocation of resources with grid stability. Specifically, demand-side management is considered an essential element of the overall Smart Grids system. Hence, predicting future energy demands is crucial to regulating consumption by aligning utility offerings with consumer demand. This research presents a hybrid deep learning model (Convolutional Neural Network [CNN] with Bi-LSTM) with a two-way attention method and a multi-objective particle swarm optimization method (MPSO) for short-term load prediction from a smart grid. The proposed hybrid model utilizes a two-way attention method at its encoding and decoding stages, in which an encoding attention layer helps to recognize all the essential features from an input vector, and a decoding attention layer helps to resolve the fixed context vector problem by offering better memory capacity. A CNN and Bi-LSTM are used to capture the essential features from the dataset. We also utilize a t-Nearest Neighbours algorithm to pre-process the initial dataset. An MPSO method combines the features of CNN and Bi-LSTM methods, resulting in better prediction accuracy. As far as we know, it is the first work to suggest a dynamic short-term load prediction model that considers different significant features and enables precise predicting outcomes. The performance of the proposed model and existing well-known deep learning models such as Recurrent Neural Network, Gated Recurrent Unit, Long Short-Term Memory (LSTM), Time Series Transformer, CNN-LSTM and various performance measuring parameters MAE, MSE, MAPE and RMSE are calculated on online UCI dataset (Electrical Grid Stability Simulated Dataset). The proposed hybrid model achieved a better prediction result, which proves the efficiency of the proposed model.

Multi-objective Optimization and Decision-Making Method for Laser Polishing Process Parameters of D2 Die Steel Based on NSGA-II and TOPSIS-EWM

Multi-objective Optimization and Decision-Making Method for Laser Polishing Process Parameters of D2 Die Steel Based on NSGA-II and TOPSIS-EWM

Tradeoff analysis of the energy-harvesting vehicle suspension system employing inerter element

This paper explores the vibration isolation performance and vibration energy recovery performance of an energy-harvesting vehicle suspension system employing inerter element. The study takes into account the structural changes in the suspension caused by introducing the inerter as a new type of vibration isolation element. According to the parallel-series combination of an inerter and a damper, two different structures of energy-harvesting suspension dynamic models are constructed. The mechanism of the suspension parameter uptake on vehicle ride comfort and energy-harvesting characteristics is analyzed. A multi-objective optimal design method for the energy-harvesting vehicle suspension system employing inerter element is proposed, which considers both vehicle ride comfort and energy-harvesting characteristics. A trade-off was found between suspension isolation performance and energy-harvesting efficiency. The results indicate that various structures of energy-harvesting vehicle suspension systems employing inerter element exhibit different vibration isolation performances. Compared with the conventional energy-harvesting suspension, the series structure reduces body acceleration by 15.9 %, and the root mean square (RMS) of energy-harvesting power of the suspension is 22.2 W. The parallel structure reduces the RMS of body acceleration by 14.3 % and the RMS of energy-harvesting efficiency is 56.3 %, with the RMS energy-harvesting power of 84.9 W. The parallel structure demonstrates superior overall performance.

Multi-objective optimization for energy-efficient building design considering urban heat island effects

Building energy performance (BEP) associated with climate change and urban heat island effects (UHI) play an important role in urban sustainable development. To predict and optimize BEP under various socioeconomic scenarios, a new framework combining the physical simulation modeling integrated explainable machine learning and multi-objective optimization is proposed in this study. A Grasshopper-based simulation model incorporates BO-LGBM (Bayesian optimization-LightGBM) is developed to construct a solid prediction system, which tends to tune the hyperparameters accurately and explain more details with the aid of SHapley Additive explanation (SHAP). Two major aspects, including the building energy use intensity and indoor thermal comfort, are modeled by considering the different Shared Socioeconomic Pathways (SSPs) climate change scenarios in the near and far future. A multi-objective optimization method is employed to find an optimal solution for energy-efficient building design under constraints or uncertainties. Key findings include a 54% improvement in the Pareto front for building energy optimization and a significant impact of SSP585 scenarios on future energy consumption. The main novelty lies in the incorporation of machine learning into a physical model to achieve energy-efficient building design in urban contexts by considering UHI effects and climate change, offering actionable strategies for BEP assessment and promoting sustainable city planning.

Integrated multi-objective optimization of rough and finish cutting parameters in plane milling for sustainable machining considering efficiency, energy, and quality

Plane milling is one of the most prevalent methods in metalworking, recognized for its potential to uncover energy-saving opportunities by optimizing cutting parameters. However, existing research on cutting parameters optimization primarily adopts a phased independent optimization approach, with some studies focusing on rough milling parameters and others on finish milling parameters under predetermined allowances. These research methods lack integrated optimization for both rough and finish milling parameters, leading to suboptimal outcomes. To address this issue, this paper proposes an integrated multi-objective optimization method for both rough and finish cutting parameters in plane milling. The aim is to achieve sustainable machining by considering efficiency, energy consumption, and quality. Specifically, the research accomplishes three key objectives: (1) developing detailed models for the efficiency, energy, and quality of plane milling; (2) presenting an integrated multi-objective optimization model and approach for determining rough and finish cutting parameters in plane milling; (3) conducting a specific case study to validate the effectiveness and feasibility of the proposed model and approach. Experimental findings demonstrated that optimizing cutting parameters in the plane milling process through an integrated approach can reduce processing time by 19.37%, decrease energy consumption by 16.88%, and significantly improve the surface roughness of the workpiece by 27.07% compared to traditional empirical methods. Furthermore, compared to independent optimization schemes, processing time is reduced by 2.99%, energy consumption is lowered by 4.72%, and surface roughness is improved by 13.16%.

Multiobjective optimization of effect-directed planar chromatography as a promising tool for fast selection of polypotent natural products

A new method for efficiently selecting polypotent natural products is proposed in this study. The method involves using effect-directed HPTLC data and multiobjective optimization algorithms to extract chromatographic signals from HPTLC bioassay images. Three different multiobjective optimization methods, namely Derringer's desirability approach, Technique for order of preference by similarity to ideal solution (TOPSIS), and Sum of ranking differences (SRD), were applied to the chromatographic signals. In combination with jackknife cross-validation, Derringer's approach and TOPSIS demonstrated high similarity in finding the best (most polypotent), next to the best, next to the worst, and worst (least polypotent) extracts, while the SRD resulted in slightly different outcomes.Furthermore, a new method for identifying the chromatographic features that characterize the most polypotent extracts was proposed. This method is based on partial least square regression (PLS) and can be used in combination with HPTLC-chemical fingerprints to predict the desirability of new extracts. The resulting PLS models demonstrated high statistical performance with determination coefficients ranging from R2 = 0.885 in the case of Derringer's desirability, to 0.986 for TOPSIS. However, the PLS modeling of SRD values was not successful.

Prediction-based multi-objective optimization method for 3D printing resource consumption

A prediction-based multi-objective optimization (PBMO) method is proposed in this paper to forecast and reduce 3D printing (3DP) resources on demand, including time, energy, and material. In the authors’ previous research work, a hybrid code-based and data-driven modeling (HCDM) scheme was proposed to customize the predictive models based on process parameters, material deposition paths, and machine behaviors. This study further utilizes the models as multi-objectives to be minimized, aiming at the appropriate solution of process parameters that consume the least resources. Non-dominated sorting genetic algorithm II (NSGA-II), one of the commonly used metaheuristic algorithms, is adopted to construct the PBMO framework, where the HCDM process is embedded in the fitness evaluation step. The corresponding computing program is compiled and then validated on two material extrusion (MEX) machines. Based on the optimization results, hypervolume, as a Lebesgue measure, is used to evaluate the superiorities of all near-optimal solutions, thereby recommending the best-performing solutions for real 3DP. Apart from the 3DP process, the proposed optimization method is adaptable to other mainstream computer numerical control (CNC) manufacturing processes and will guide process design to promote resource conservation for cleaner production.

Design and Experimental of the Soil Removal Device for Root-Soil Complex of Gentian Imitating the Percussion of Woodpeckers.

A soil removal device for the root-soil complex of Gentian imitating the percussion function of a woodpecker was designed to improve the soil removal efficiency of harvesting devices for rhizome-type traditional Chinese herbal medicines. Based on the physical parameters of roots and the root-soil complex of Gentian, the structure parameters of the striking arm and the actual profile of the cam are determined according to the physical parameters when the woodpecker knocks on the tree. The key parameters that affect the working performance of the soil removal device and their suitable value ranges have been identified through the impact test and analysis of the root-soil complex of Gentian. The mass of the striking hammer, the swing angle of the striking arm, and the rotation speed of the cam were taken as the experimental factors and the soil removal rate and the energy consumption per hammer percussion were taken as the experimental indicators. The ternary quadratic orthogonal regression combination experiment was carried out using Design-Expert. The regression model of the influence factors and evaluation indicators was established through the analysis of variance. The interaction effects of the influence factors on the indicators were analyzed using the response surface method. Using multiobjective optimization method, the optimal parameter combination was obtained as that of the mass of the striking hammer of 0.9 kg, the swing angle of the striking arm of 47°, and the rotation speed of the cam of 100 r/min, then the soil removal rate was the maximum and the energy consumption of single-hammer knocking was the minimum, with the values of 89.12% and 31.21 J, respectively. This study can provide a reference for the design and optimization of soil removal devices for rhizome-type traditional Chinese herbal medicines.

Droop Frequency Limit Control and Its Parameter Optimization in VSC-HVDC Interconnected Power Grids

With the gradual emergence of trends such as the asynchronous interconnection of power grids and the increasing penetration of renewable energy, the issues of ultra-low-frequency oscillations and low-frequency stability in power grids have become more prominent, posing serious challenges to the safety and stability of systems. The voltage-source converter-based HVDC (VSC-HVDC) interconnection is an effective solution to the frequency stability problems faced by regional power grids. VSC-HVDC can participate in system frequency stability control through a frequency limit controller (FLC). This paper first analyses the basic principles of how VSC-HVDC participates in system frequency stability control. Then, in response to the frequency stability control requirements of the sending and receiving power systems, a droop FLC strategy is designed. Furthermore, a multi-objective optimization method for the parameters of the droop FLC is proposed. Finally, a large-scale electromagnetic transient simulation model of the VSC-HVDC interconnected power system is constructed to verify the effectiveness of the proposed droop FLC method.

Multi-objective optimization of comprehensive performance enhancement for proton exchange membrane fuel cell based on machine learning

The comprehensive performance of proton exchange membrane fuel cells depends on operating conditions. This paper innovatively uses the Pearson correlation coefficient to screen the optimization objectives (uniformity index of oxygen, standard deviation of temperature, net power density), and obtains the optimal operating conditions of the proton exchange membrane fuel cell through a multi-objective optimization method. The optimized dataset comes from the simulation results of the three-dimensional numerical model, and the regression model is established through the response surface method. Moreover, the non-dominated sorting genetic algorithm-II is used for processing to obtain the Pareto front solution set, and the optimal operating conditions are obtained from it through the Technique for order preference by similarity to an ideal solution. The analysis of variance result shows that the influence of cathode operating conditions on the comprehensive performance is greater than that of anode, especially the influence of cathode stoichiometry ratio is the most significant. The optimal solution obtained 1.0981 %, 10.5845 %, and 1.0376 % enhancement compared to the optimal values in the simulation results. The differences between the three optimization objectives are only 0.8190 %, 1.0315 %, and 0.8789 % as verified by numerical simulation, thus the machine learning results are reliable and accurate.

Multi-objective optimization of a wet electromagnet-driven water hydraulic high-speed on/off valve for underwater manipulator in deep sea

Water hydraulic high-speed on/off valves (WHSVs) have the potential to replace the traditional oil hydraulic valves to be used in underwater hydraulic manipulators (UHMs) in the deep-sea environment due to their environmental friendliness, strong anti-pollution ability, and good sealing property. In this paper, we develop and optimize a novel wet electromagnet-driven WHSV to enhance its suitability for UHMs. Firstly, an accurate equivalent magnetic model of the WHSV is derived, considering the leakage flux and nonlinear characteristics. This magnetic model is combined with other physical field models to accurately describe the dynamic behavior of the WHSV and provide a comprehensive simulation framework. Then, the multi-objective optimization and decision-making methods are combined to optimize and determine the structural parameters of the WHSV, achieving a balance between rapid response and light mass. Finally, a WHSV prototype is manufactured and tested based on the optimized structural parameters. Experimental results indicate that the WHSV achieves opening and closing times of 1.63 ms and 1.22 ms, respectively, with the electromagnetic components weighing 75.5 g. These results show a maximum deviation of 7.4 % from the optimization results (1.52 ms, 1.31 ms, 71.6 g). Moreover, the deep-sea simulation test verifies the prototype functionality under a high-pressure condition (110 MPa).

Conjointly active and passive modelings with deep neural networks as fully automated optimizations for upper-mid band 6G communications

Today wireless systems include the fifth and sixth generations (5G and 6G) technologies and are growing day by day that result in exponentially increasing data traffic. For providing a reliable and high performance radio frequency (RF) designs especially for 6G networks, amplifiers and antenna as active and passive components play important roles. In the 5G/6G communication systems, the propagation loss is considerably large and its compensation requires high output power generated from the amplifiers for guaranteeing the satisfied quality of transmitted signal. From another point of view, the installed antennas must be able to optimally manage the radiated signals and handle/compensate nonlinear performances of the RF circuitry. Hence, advanced modeling and multi-objective optimization algorithms are required for designing and optimizing high performance amplifiers and antennas in terms of output power, gain, efficiency, linearity, and bandwidth. Concurrently optimizing active and passive components is not straightforward and typically it requires additional efforts by the RF designers. To tackle this drawback, a two-step methodology is proposed: (1) configuring the initial structure of active and passive devices, and (2) sizing the configured devices. In this work, various methods are introduced for structuring the topology of circuits and then artificial intelligence, including machine learning and neural networks, is preferred among other surrogate modelling for sizing the designs. These neural networks are satisfied due to the accurate modeling responses and are able to provide an automated optimization process leads to employ multi-objective optimization methods. In this work, an automated optimization process for comprehensive design of high-performance amplifiers with antennas through bottom-up optimization (BUO) method and long short-term memory (LSTM)-based deep neural networks (DNNs) is proposed. At the output layer of DNNs, the multi-objective multi-verse optimizer (MOMVO) method is employed for optimizing various specifications of active device (i.e., amplifier), and passive device (i.e., antenna), concurrently. In the presented method, all the electromagnetic (EM) design rules are implemented which results in reducing simulation time in the harmonic balance simulation environment that also provides ready to fabricate layouts. The novelty consists of the all-inclusive style that (1) reduces the manual breaks, aka time-to-market, and (2) delivers ready-to-fabricate layouts of the device that exhibits global optimum performances, automatically. The validation of the proposed method is verified by designing and optimizing high power amplifier (HPA) with antenna in the frequency band from 9.0 GHz to 9.6 GHz, suitable for upper-mid band 6G communications.

Application of the TOPSIS Method for Multi-Objective Optimization of a Two-Stage Helical Gearbox

Application of the TOPSIS Method for Multi-Objective Optimization of a Two-Stage Helical Gearbox

Special issue on exact and approximation methods for mixed-integer multi-objective optimization

Special issue on exact and approximation methods for mixed-integer multi-objective optimization

A multi-objective brain storm optimization for integrated distributed flexible job shop and distribution problems

Production and distribution are critical components of the furniture supply chain, and achieving optimal performance through their integration has become a vital focus for both the academic and business communities. Moreover, as economic globalization progresses, distributed manufacturing has become a pioneering production technique. Via leveraging a distributed flexible manufacturing system, mass flexible production at lower costs can be achieved. To this end, this study presents an integrated distributed flexible job shop and distribution problem to minimize makespan and total tardiness. In our research, a set of custom furniture orders from different customers are processed among flexible job shops and then delivered by vehicles to customers as the due date. To distinctly show the presented problem, a mixed integer mathematical programming model is created, and a multi-objective brain storm optimization method is introduced considering the problem's features. In comparison to the other three advanced methods, the superiority of the algorithm created is showcased. The findings of the experiments demonstrate that the constructed model and the introduced algorithm have remarkable competitiveness in addressing the problem being examined.

Multi-objective Bonobo optimisers of industrial low-density polyethylene reactor

Abstract A multi-objective optimization (MOO) technique to produce a low-density polyethylene (LDPE) is applied to address these two problems: increasing conversion and reducing operating cost (as the first optimization problem, P1) and increasing productivity and reducing operating cost (as the second optimization problem, P2). ASPEN Plus software was utilized for the model-based optimization by executing the MOO algorithm using the tubular reactor model. The multi-objective optimization of multi-objective Bonobo optimisers (MOBO-I, MOBO-II and MOBO-III) are utilised to solve the optimization problem. The performance matrices, including hypervolume, pure diversity, and distance, are used to decide on the best MOO method. An inequality constraint was introduced on the temperature of the reactor to prevent run-away. According to the findings of the study, the MOBO-II for Problems 1 and 2 was the most effective MOO strategy. The reason is that the solution set found represents the most accurate, diversified, and acceptable distribution points alongside the Pareto Front (PF) in terms of homogeneity. The minimum operating cost, the maximum conversion and productivity obtained by MOBO-II are Mil. RM/year 114.3, 31.45 %, Mil. RM/year 545.3, respectively.

Research on multi-objective optimization method in the design of high-power nanocrystalline alloy high-frequency transformer

The optimization design of high-frequency transformer (HFT) is a multi-objective optimization problem owing to the mutual constraints between design parameters such as power density, efficiency and temperature rise, and traditional design methods often face difficulties in balancing and selecting multiple design parameters. Therefore, this paper derives the structural parameters of HFT, calculates the magnetic core losses and winding losses considering temperature effects, and establishes a 7-node thermal network model. Selecting power density and efficiency as optimization objectives, the HFT is optimized based on multi-objective particle swarm optimization algorithm and non-dominated genetic algorithm, and compared with traditional free parameter scanning method to analyze the advantages of intelligent optimization algorithm. Further, the influence of temperature rise calculation methods on the optimization design results of HFT is compared and analyzed. Finally, a nanocrystalline HFT with 17 MW/m3 power density and 99 % efficiency and an optimization scheme of HFT with 26 MW/m3 power density and 99.2 % efficiency are designed based on the proposed optimization design processes. The effectiveness of the calculation model and optimization method is verified through experimental measurement, and the essential principle of improving the HFT performance through optimization methods is explored through finite element simulation, providing theoretical reference and data support for the optimization design of nanocrystalline HFT.

Optimizing hybrid energy storage: A multi-objective approach for hydrogen-natural gas systems with carbon-emission management

This article addresses the challenges of load curve variability in integrated energy systems (IES) and emphasizes carbon neutralization in response to global environmental concerns. It introduces a game-theoretic approach to understand the collaborative dynamics within an integrated energy-hydrogen system (HGESS). The study develops a collaborative game model for the IES-HGESS partnership, focusing on carbon neutralization considerations. Using game theory, it investigates the determinants shaping this partnership and extends to create a hybrid model for day-ahead and real-time markets. To foster enduring cooperation, the study introduces a cooperation profit coefficient and devises an income distribution strategy for HGESS-IES interactions. A load management program is incorporated to reduce costs and enhance profitability. Addressing complexity and constraints, the article introduces an advanced multi-objective particle swarm optimization method. Tested on a sample system under various conditions, the results demonstrate that the alliance between IES and HGESS can significantly enhance net profits, achieving mutual benefit and a win-win outcome while advancing sustainability in energy management.

On the convergence analysis of a proximal gradient method for multiobjective optimization

On the convergence analysis of a proximal gradient method for multiobjective optimization