Riboswitch Design Using MODENA.

In silico design of artificial riboswitches is a challenging and intriguing task. Since experimental approaches such as in vitro selection are time-consuming processes, computational tools that guide riboswitch design are desirable to accelerate the design process. In this chapter, we describe the usage of the MODENA web server to design ON riboswitches on the basis of a multi-objective genetic algorithm and RNA secondary structure prediction.

Finite element analysis and surrogate-optimized design of a nature-inspired auxetic stent.

Prior studies have revealed that the structural design of stents is critical to reducing some of the alarming post-operative complications associated with stent-related intervention. However, the technical search for stents that guarantee robustness against stent-induced post-intervention complications remains an open problem. Along this objective, this study investigates a re-entrant auxetic stent's structural response and performance optimizations. In pursuit of the goal, a nonlinear finite element analysis (FEA) is employed to uncover metrics characterizing the auxetic stent's mechanical behavior. Subsequently, the non-dominated sorting genetic algorithm (NSGA-II) is implemented to simultaneously minimize the stent's von Mises stress and the elastic radial recoil (ERR). Results from the FEA revealed a tight connection between the stent's response and the features of the base auxetic building block (the rib length, strut width, and the re-entrant angle). It is observed that the auxetic stent exhibits a much lower ERR. Besides, larger values of its rib length and re-entrant angle are noticed to favor smaller von Mises stress. The Pareto-optimal front from the NSGA-II-based optimization scheme revealed a sharp trade-off in the simultaneous minimization of the von Mises stress and the ERR. Moreover, an optimal combination of the auxetic unit cell's geometric parameters is found to yield a much lower maximum von Mises stress of ≈403 MPa and ERR of ≈0.4%.

Improving effort-aware just-in-time defect prediction with weighted code churn and multi-objective slime mold algorithm.

Effort-aware just-in-time software defect prediction (JIT-SDP) aims to effectively utilize the limited resources of software quality assurance (SQA) to detect more software defects. This improves the efficiency of SQA work and the quality of the software. However, there is disagreement regarding the representation of the key feature variable, SQA effort, in the field of effort-aware JIT-SDP. Additionally, the most recent metaheuristic optimization algorithms (MOAs) have not yet been effectively integrated with multi-objective effort-aware JIT-SDP tasks. These deficiencies, in both feature representation and model optimization (MO), result in a significant disparity between the performance of effort-aware JIT-SDP techniques and the expectations of the industry. In this study, we present a novel method called weighted code churn (CC) and improved multi-objective slime mold algorithm (SMA) (WCMS) for effort-aware JIT-SDP. It comprises two stages: feature improvement (FI) and MO. In the FI phase, we normalize the two feature variables: number of modified files (NF) and distribution of modified code across each file (Entropy). We then use an exponential function to quantify the level of difficulty of the change. The equation is as follows: DD = NFEntropy, where DD is an acronym for the degree of difficulty, NF denotes the base number, and Entropy denotes the index. We define change effort as the product of the difficulty degree in implementing the change and CC, with weighted CC representing the change effort. During the MO stage, we improve the SMA by incorporating multi-objective handling capabilities and devising mechanisms for multi-objective synchronization and conflict resolution. We develop a multi-objective optimization algorithm for hyperparameter optimization (HPO) of the JIT-SDP model in WCMS. To evaluate the performance of our method, we conducted experiments using six well-known open-source projects and employed two effort-aware performance evaluation metrics. We evaluated our method based on three scenarios: cross-validation, time-wise cross-validation, and across-project prediction. The experimental results indicate that the proposed method outperforms the benchmark method. Furthermore, the proposed method demonstrates greater scalability and generalization capabilities.

A multi-objective African vultures optimization algorithm with binary hierarchical structure and tree topology for big data optimization.

INTRODUCTION: Big data optimization (Big-Opt) problems present unique challenges in effectively managing and optimizing the analytical properties inherent in large-scale datasets. The complexity and size of these problems render traditional data processing methods insufficient. OBJECTIVES: In this study, we propose a new multi-objective optimization algorithm called the multi-objective African vulture optimization algorithm with binary hierarchical structure and tree topology (MO_Tree_BHSAVOA) to solve Big-Opt problem. METHODS: In MO_Tree_BHSAVOA, a binary hierarchical structure (BHS) is incorporated to effectively balance exploration and exploitation capabilities within the algorithm; shift density estimation is introduced as a mechanism for providing selection pressure for population evolution; and a tree topology is employed to reinforce the algorithm's ability to escape local optima and preserve optimal non-dominated solutions. The performance of the proposed algorithm is evaluated using CEC 2020 multi-modal multi-objective benchmark functions and CEC 2021 real-world constrained multi-objective optimization problems and is applied to Big-Opt problems. RESULTS: The performance is analyzed by comparing the results obtained with other multi-objective optimization algorithms and using Friedman's statistical test. The results show that the proposed MO_Tree_BHSAVOA not only provides very competitive results, but also outperforms other algorithms. CONCLUSION: These findings validate the effectiveness and potential applicability of MO_Tree_BHSAVOA in addressing the optimization challenges associated with big data.

Multi-objective resistance-capacitance optimization algorithm: An effective multi-objective algorithm for engineering design problems.

Focusing on practical engineering applications, this study introduces the Multi-Objective Resistance-Capacitance Optimization Algorithm (MORCOA), a new approach for multi-objective optimization problems. MORCOA uses the transient response behaviour of resistance-capacitance circuits to navigate complex optimization landscapes and identify global optima when faced with many competing objectives. The core approach of MORCOA combines a dynamic elimination-based crowding distance mechanism with non-dominated sorting to generate an ideal and evenly distributed Pareto front. The algorithm's effectiveness is evaluated through a structured, three-phase analysis. Initially, MORCOA is applied to five benchmark problems from the ZDT test suite, with performance assessed using various metrics and compared against state-of-the-art multi-objective optimization techniques. The study then expands to include seven problems from the DTLZ benchmark collection, further validating MORCOA's effectiveness. The final phase involves applying MORCOA to six real-world constrained engineering design problems. Notably, the optimization of a honeycomb heat sink, which is crucial in thermal management systems, is a significant part of this study. This phase uses a range of performance measures to assess MORCOA's practical application and efficiency in engineering design. The results highlight MORCOA's robustness and efficiency in both real-world engineering applications and benchmark problems, demonstrating its superior capabilities compared to existing algorithms. The effective use of MORCOA in real-world engineering design problems indicates its potential as an adaptable and powerful tool for complex multi-objective optimization tasks.

GRNMOPT: Inference of gene regulatory networks based on a multi-objective optimization approach.

BACKGROUND AND OBJECTIVE: The reconstruction of gene regulatory networks (GRNs) stands as a vital approach in deciphering complex biological processes. The application of nonlinear ordinary differential equations (ODEs) models has demonstrated considerable efficacy in predicting GRNs. Notably, the decay rate and time delay are pivotal in authentic gene regulation, yet their systematic determination in ODEs models remains underexplored. The development of a comprehensive optimization framework for the effective estimation of these key parameters is essential for accurate GRN inference. METHOD: This study introduces GRNMOPT, an innovative methodology for inferring GRNs from time-series and steady-state data. GRNMOPT employs a combined use of decay rate and time delay in constructing ODEs models to authentically represent gene regulatory processes. It incorporates a multi-objective optimization approach, optimizing decay rate and time delay concurrently to derive Pareto optimal sets for these factors, thereby maximizing accuracy metrics such as AUROC (Area Under the Receiver Operating Characteristic curve) and AUPR (Area Under the Precision-Recall curve). Additionally, the use of XGBoost for calculating feature importance aids in identifying potential regulatory gene links. RESULTS: Comprehensive experimental evaluations on two simulated datasets from DREAM4 and three real gene expression datasets (Yeast, In vivo Reverse-engineering and Modeling Assessment [IRMA], and Escherichia coli [E. coli]) reveal that GRNMOPT performs commendably across varying network scales. Furthermore, cross-validation experiments substantiate the robustness of GRNMOPT. CONCLUSION: We propose a novel approach called GRNMOPT to infer GRNs based on a multi-objective optimization framework, which effectively improves inference accuracy and provides a powerful tool for GRNs inference.

Multi-Objective Robust Design Optimization for Crashworthiness Enhancement of Hybrid 2D Triaxially Braided Composite Tube Using Evolutionary Algorithms.

An innovative optimal design framework is developed aiming at enhancing the crashworthiness while ensuring the lightweight design of a hybrid two-dimensional triaxial braided composite (2DTBC) tube, drawing insights from the mesostructure of the composite material. To achieve these goals, we first compile the essential mechanical properties of the 2DTBC using a concentric cylinder model (CCM) and an analytical laminate model. Subsequently, a kriging surrogate model to elucidate the intricate relationship between design variables and macroscopic crashworthiness is developed and validated. Finally, employing multi-objective evolutionary optimization, we identify Pareto optimal solutions, highlighting that reducing the total fiber volume and increasing the glass fiber content in the total fiber volume are crucial for optimal crashworthiness and the lightweight design of the hybrid 2DTBC tube. By integrating advanced predictive modeling techniques with multi-objective evolutionary optimization, the proposed approach not only sheds light on the fundamental principles governing the crashworthiness of hybrid 2DTBC but also provides valuable insights for the design of robust and lightweight composite structures.

Process Parameters Optimization and Numerical Simulation of AlCoCrFeNi High-Entropy Alloy Coating via Laser Cladding.

The use of laser cladding technology to prepare coatings of AlCoCrFeNi high-entropy alloy holds enormous potential for application. However, the cladding quality will have a considerable effect on the properties of the coatings. In this study, considering the complex coupling relationship between cladding quality and the process parameters, an orthogonal experimental design was employed, with laser power, scanning speed, and powder feed rate as correlation factor variables, and microhardness, dilution rate, and aspect ratio as characteristic variables. The experimental data underwent gray correlation analysis to determine the effect of various process parameters on the quality of cladding. Then, the NSGA-II algorithm was used to establish a multi-objective optimization model of process parameters. Finally, the ANSYS Workbench simulation model was employed to conduct numerical simulations on a group of optimized process parameters and analyze the change rule of the temperature field. The results demonstrate that the laser cladding coating of AlCoCrFeNi high-entropy alloy with the single pass is of high quality within the determined orthogonal experimental parameters. The powder feed rate exerts the most significant influence on microhardness, while laser power has the greatest impact on dilution rate, and scanning speed predominantly affects aspect ratio. The designed third-order polynomial nonlinear regression model exhibits a high fitting accuracy, and the NSGA-II algorithm can be used for multi-objective optimization to obtain the Pareto front solution set. The numerical simulation results demonstrate that the temperature field of AlCoCrFeNi high-entropy alloy laser cladding exhibits a "comet tail" phenomenon, where the highest temperature of the molten pool is close to 3000 °C. The temperature variations in the molten pool align with the features of laser cladding technology. This study lays the groundwork for the widespread application of laser cladding AlCoCrFeNi high-entropy alloy in surface engineering, additive manufacturing, and remanufacturing. Researchers and engineering practitioners can utilize the findings from this research to judiciously manage processing parameters based on the results of gray correlation analysis. Furthermore, the outcomes of multi-objective optimization can assist in the selection of appropriate process parameters aligned with specific application requirements. Additionally, the methodological approach adopted in this study offers valuable insights applicable to the exploration of various materials and diverse additive manufacturing techniques.

Multi-Objective Optimization and Mechanical Properties Analysis of Steel-PVA Hybrid Fiber-Reinforced Cementitious Composites.

When steel fiber and PVA fiber produced in China and PVA fiber made in Japan are prepared according to the appropriate proportions, the mechanical properties of hybrid fiber-reinforced cementitious composites (HFRCC) are better, which is beneficial to cost control and has wide application prospects. The effects of the volume content of steel fibers and the volume substitution rate of PVA fibers on the tensile strength, compressive strength, and flexural strength of HFRCC were analyzed using the factor optimization method and principal component analysis (PCA). Through the principal component analysis of HFRCC, a mathematical model for comprehensive performance evaluation was established, and a multi-objective optimization was carried out. The results show that compared with the matrix, the tensile strength, compressive strength, and flexural strength of concrete increase significantly when the volume content of steel fibers is 0.2-0.4% and the volume substitution rate of domestically produced PVA fibers in China or PVA fibers produced in Japan is 50-100%. The maximum cost reduction is 88.25%, and the strength index of HFRCC can reach the optimum; the weights of each factor on the performance of HFRCC were obtained through mathematical statistics. Combined with a variable correlation analysis, these indicators should be noted when optimizing the performance of HFRCC. The research results can provide a basis for the preparation of HFRCC.

Optimization Design of Submerged-Entry-Nozzle Structure Using NSGA-II Genetic Algorithm in Ultra-Large Beam-Blank Continuous-Casting Molds.

To achieve uniform cooling and effective homogenization control in ultra-large beam-blank molds necessitates the optimization of submerged-entry-nozzle (SEN) structures. This study employed computational fluid dynamic (CFD) modeling to investigate the impact of two-port and three-port SEN configurations on fluid flow characteristics, free-surface velocities, temperature fields, and solidification behaviors. Subsequently, integrating numerical simulations with the non-dominated sorting genetic algorithm II (NSGA-II) and metallurgical quality-control expertise facilitated the multi-objective optimization of a three-port SEN structure suitable for beam-blank molds. The optimized parameters enabled the three-port SEN to deliver molten steel to the meniscus at an appropriate velocity while mitigating harmful effects such as SEN port backflow, excessive surface temperature differences, and shell thickness reduction due to fluid flow. The results indicated that the three-port SEN enhanced the molten-steel flow pattern and mitigated localized shell thinning compared with the two-port SEN. Additionally, the optimized design (op2) of the three-port SEN exhibited reduced boundary layer separation and superior fluid dynamics performance over the initial three-port SEN configuration.

Numerical study on the forward and inverse problems of the mobile pump truck frame.

Aiming at the requirements of strong mobility and high flexibility of rescue and relief mobile pump trucks, this paper designs a new type of mobile pump truck frame based on existing mobile vehicle frame models. The materials used for the frame are 40Cr and Q235, and the finite element method is utilized to carry out static mechanical analysis and dynamic characteristic analysis. Simultaneously utilizing topology optimization and multi-objective genetic algorithm to optimize the design of the frame structure. The results show that the optimized pump truck frame can meet the strength design requirements of four typical working conditions: full load bending, full load torsion, emergency turning and emergency braking, while avoiding resonance phenomena caused by road surface and diesel engine vibration. Compared with the original frame model, the weight of the optimized frame is reduced by 87.88 kg, with a weight reduction rate of 10.89%, realizing the lightweight design requirements.

Multi-objective optimization design of low-power-driven, large-flux, and fast-response three-stage valve.

Intrinsically safe solenoids drive solenoid valves in coal mining equipment. The low power consumption of these solenoids limits the response time of the solenoid valves. Additionally, the low viscosity and high susceptibility to dust contamination of the emulsion fluid often lead to leakage and sticking of hydraulic valves. This study proposes a low-power-driven, large-flux, fast-response three-stage valve structure with an internal displacement feedback device to address these issues. The critical parameters of the valve were optimized using a novel multi-objective optimization algorithm. A prototype was manufactured based on the obtained parameters and subjected to simulation and experimental verification. The results demonstrate that the valve has an opening time of 21 ms, a closing time of 12 ms, and a maximum flow rate of approximately 225 L/min. The driving power of this structure is less than 1.2 W. By utilizing this valve for hydraulic cylinder control, a positioning accuracy of ± 0.15 mm was achieved. The comparative test results show that the proposed structural control error fluctuation is smaller than that of the 3/4 proportional valve.

Water quality constrained adjustment planning for regional breeding management with nonlinear programming model under uncertainty in Wenchang City, China.

Basin water pollution caused by livestock, poultry and fish breeding is still a serious problem for remote villages, however, reliable regional breeding management programming have the potentials to improve pollution status. This paper focuses on the optimal model design and water quality analysis of the livestock, poultry and fish breeding system for Wenchang City, China. Methods of multi-objective programming (MOP), interval parameter programming (IPP), fuzzy-stochastic parameter programming (FSPP), and chance constrained programming (CCP) were incorporated into the developed model to tackle multi uncertainties described by interval values, probability distributions, fuzzy membership function. Based on the estimation of local breeding potential and current situation of surface water section, a multi-objective mixed fuzzy-stochastic nonlinear programming optimization model is presented with one-dimensional water quality model. In order to evaluate the environmental carrying capacity of livestock, poultry and fishery manure, predict its development trend and investigate the implementation effect of different emission reduction policies, this paper designs quantization system of the urban water environmental carrying capacity for the model. The results indicated that the water environment pollutant absorption capacity and carrying capacity of Wenchang city have approached the limit especially the towns in the northeast of City which limited the overall development space of the City. The modeling results are valuable for supporting the adjustment of the existing livestock, poultry and fish breeding schemes within a complicated system benefit and surface water quality situation under uncertainty.

Performance-constrained multi-objective optimization of antennas for miniaturization design.

The miniaturization of antennas is crucial as it improves the integration of wireless communication system. In order to achieve miniaturization of antennas, a performance-constrained multi-objective optimization method (PCMOM) considering the size and return loss is proposed. In the PCMOM method, an optimization strategy based on a multi-port network model is introduced, enabling the formation of antennas with various structures. Furthermore, we integrate the constraint of return loss performance into the non-dominated sorting genetic algorithm II (NSGA-II), eliminating solutions that do not meet performance requirements. Three pixel antennas are designed using the PCMOM method and two of them are fabricated. Experimental results demonstrate that the proposed PCMOM method can effectively address the complex trade-off issues in antenna miniaturization design.

Optimizing irrigation and nitrogen application strategies to improve sunflower yield and resource use efficiency in a cold and arid oasis region of Northwest China.

In arid regions, water scarcity, land degradation and groundwater pollution caused by excessive fertilization are the main constraints to sustainable agricultural production. Optimizing irrigation and fertilizer management regime is an effective means of improving crop water and fertilizer productivity as well as reducing negative impacts on the ecosystem. In order to investigate the effects of different irrigation and nitrogen (N) fertilizer rates on sunflower growth, yield, and water and N use efficiency, and to determine the optimal water and N management strategy, a two-year (2021 and 2022) field experiment with under-mulched drip irrigation was conducted in the Hexi Oasis area of Northwest China. The experiment design comprised three irrigation levels (W1, 55%-65% FC, where FC represents field water capacity; W2, 65%-75% FC; W3, 75%-85% FC) and three N application levels (N1, 120 kg ha-1; N2, 180 kg ha-1; N3, 240 kg ha-1), resulting in a total of nine treatments. The findings indicated that increasing irrigation and N application rates led to improvements in leaf area index (15.39%-66.14%), dry matter accumulation (11.43%-53.15%), water consumption (ET, 1.63%-42.90%) and sunflower yield (6.85%-36.42%), in comparison to the moderate water deficit and low N application (W1N1) treatment. However, excess water and N inputs did not produce greater yield gains and significantly decreased both water use efficiency (WUE) and nitrogen partial factor productivity (NPFP). Additionally, a multiple regression model was developed with ET and N application as explanatory variables and yield, WUE and NPFP as response variables. The results based on the regression model combined with spatial analysis showed that an ET range of 334.3-348.7 mm and N application rate of 160.9-175.3 kg ha-1 achieved an optimal balance between the multiple production objectives: yield, WUE and NPFP. Among the different irrigation and N management strategies we evaluated, we found that W2N2 (65%-75% FC and 180 kg N ha-1) was the most fruitful considering yield, resource use efficiency, etc. This result can serve as a theoretical reference for developing appropriate irrigation and N fertilization regimes for sunflower cultivation in the oasis agricultural area of northwest China.

Efficient production of pullulan by Aureobasidium pullulans using a multi-objective optimization strategy with orthogonal experimental design coupling artificial neural network and genetic algorithm.

Efficient pullulan production has long been a central research focus. This study used maltodextrin as the carbon source for pullulan production by Aureobasidium pullulans fermentation. A hybrid optimization approach, integrating orthogonal experimental design (OED), backpropagation artificial neural network (BP-ANN), and elite strategy non-dominated sequential genetic algorithm-II (NSGA-II), was developed. Range analysis based on OED revealed that MgSO4·7H2O significantly affects production but less impacts molecular weight, while pH notably influences molecular weight with a lesser effect on production, underscoring the need for multi-objective optimization. The BP-ANN model showed strong predictive capabilities, with goodness-of-fit values of 0.984 and 0.980 for production and molecular weight, respectively. Using this model as the fitness function for the optimization algorithm enhanced efficiency. Taking cost factors into account, the BP-ANN-NSGA-II algorithm identified the optimal fermentation medium conditions, resulting in a 6.89 % increase in production, a 368.97 % increase in molecular weight, and a 42.49 % reduction in cost. The maximum comprehensive optimization efficiency is 63.73 %, and the multi-objective optimization is better than the single objective optimization. This method significantly guides the improvement of pullulan fermentation optimization efficiency.

The MOEO algorithm for multi-objective optimization of the cancer immuno-chemotherapy.

In cancer treatment, chemotherapy has the disadvantage of killing both healthy and cancerous cells. Hence, the mixed-treatment of cancer such as chemo-immunotherapy is recommended. However, deriving the optimal dosage of each drug is a challenging issue. Although metaheuristic algorithms have received more attention in solving engineering problems due to their simplicity and flexibility, they have not consistently produced the best results for every problem. Thus, the need to introduce novel algorithms or extend the previous ones is felt for important optimization problems. Hence, in this paper, the multi-objective Equilibrium Optimizer algorithm, as an extension of the single-objective Equilibrium Optimizer algorithm, is recommended for cancer treatment problems. Then, the performance of the proposed algorithm is considered in both chemotherapy and mixed chemo-immunotherapy of cancer, considering the constraints of the tumor-immune dynamic system and the health level of the patients. For this purpose, two different patients with real clinical data are considered. The Pareto front obtained from the multi-objective optimization algorithm shows the points that can be selected for treatment under different criteria. Using the proposed multi-objective algorithm has reduced the total chemo-drug dose to 138.92 and 5.84 in the first patient, and 16.9 and 0.4384 in the second patient, for chemotherapy and chemo-immunotherapy, respectively. Comparing the results with previous studies demonstrates MOEO's superior performance in both chemotherapy and chemo-immunotherapy. However, it is shown that the proposed algorithm is more suitable for mixed-treatment approaches.

An extended goal programming approach with piecewise penalty functions for uncertain supplier-material selection problem in cardboard box manufacturing systems.

In this study a real case multi-objective material and supplier selection problem in cardboard box production industries is studied. This problem for the first time optimizes the objective functions such as total wastage amounts remained from all raw sheets, total costs of the system including purchasing cost and transportation cost (including fixed and variable costs) of the raw sheets, and total overplus of produced cardboard boxes. To be closer to the real situations, as a novelty, the problem is formulated in belief-degree-based uncertain environment with normal distribution where this type of uncertainty applies the ideas of experts. A solution approach including two steps is proposed to solve the problem. In the first step, the proposed uncertain formulation is converted to a crisp form using a typical chance constrained programming scheme. In the second step, a new goal programming approach containing a piecewise penalty function is developed in order to solve the obtained multi-objective crisp formulation. In this approach, based on the ideas of experts, multiple goals are considered with different penalty values. A case study from cardboard box industries is considered to evaluate the proposed formulations and solution approach. According to the obtained results, the proposed solution approach is compared to similar approaches of the literature and its efficiency is studied.

Multi-objective constraints for path planning in screw fixation of scaphoid fractures.

PURPOSE: Scaphoid fractures, a common type of clinical fracture, often require screw placement surgery to achieve optimal therapeutic outcomes. Path planning algorithms can avoid more risks and have vital potential for developing precise and automatic surgeries. Despite the success of surgical path planning algorithms, automatic path planning for scaphoid fractures remains challenging owing to the complex bone structure and individual variations. METHODS: Thus, we propose a Multi-objective constrained Path planning Algorithm (MPA) for fracture screw placement, which includes the identification of the center of the fracture surface. Further, three constraint conditions were introduced to eliminate infeasible paths, followed by adding three objectives to the remaining paths for more accurate planning. Finally, the Nondominated Sorting Genetic Algorithms (NSGA)-II algorithm was used to optimize the surgical paths. RESULTS: We defined the vertical compression distance (VCD), a common observation index in clinics. The experiments show that the average VCD of the MPA paths is measured at 23.88 mm, outperforming the clinical planning paths by 21.71 mm. Ablation experiments demonstrated that all three objectives (distance, length, and angle) effectively optimized the path planning. Additionally, we also used finite element analysis to compare and analyze the MPA path and clinical path. The experimental results showed that the MPA path always outperformed the clinical path in terms of scaphoid strain and screw stress. CONCLUSION: This study presents a solution for the path planning of scaphoid fractures. Our future research will attempt to enhance the model's performance and extend its application to a broader range of fracture types.

Enhancing infectious disease prediction model selection with multi-objective optimization: an empirical study.

As the pandemic continues to pose challenges to global public health, developing effective predictive models has become an urgent research topic. This study aims to explore the application of multi-objective optimization methods in selecting infectious disease prediction models and evaluate their impact on improving prediction accuracy, generalizability, and computational efficiency. In this study, the NSGA-II algorithm was used to compare models selected by multi-objective optimization with those selected by traditional single-objective optimization. The results indicate that decision tree (DT) and extreme gradient boosting regressor (XGBoost) models selected through multi-objective optimization methods outperform those selected by other methods in terms of accuracy, generalizability, and computational efficiency. Compared to the ridge regression model selected through single-objective optimization methods, the decision tree (DT) and XGBoost models demonstrate significantly lower root mean square error (RMSE) on real datasets. This finding highlights the potential advantages of multi-objective optimization in balancing multiple evaluation metrics. However, this study's limitations suggest future research directions, including algorithm improvements, expanded evaluation metrics, and the use of more diverse datasets. The conclusions of this study emphasize the theoretical and practical significance of multi-objective optimization methods in public health decision support systems, indicating their wide-ranging potential applications in selecting predictive models.