Genetic algorithm with skew mutation for heterogeneous resource-aware task offloading in edge-cloud computing.

Recent years, edge-cloud computing has attracted more and more attention due to benefits from the combination of edge and cloud computing. Task scheduling is still one of the major challenges for improving service quality and resource efficiency of edge-clouds. Though several researches have studied on the scheduling problem, there remains issues needed to be addressed for their applications, e.g., ignoring resource heterogeneity, focusing on only one kind of requests. Therefore, in this paper, we aim at providing a heterogeneity aware task scheduling algorithm to improve task completion rate and resource utilization for edge-clouds with deadline constraints. Due to NP-hardness of the scheduling problem, we exploit genetic algorithm (GA), one of the most representative and widely used meta-heuristic algorithms, to solve the problem considering task completion rate and resource utilization as major and minor optimization objectives, respectively. In our GA-based scheduling algorithm, a gene indicates which resource that its corresponding task is processed by. To improve the performance of GA, we propose to exploit a skew mutation operator where genes are associated to resource heterogeneity during the population evolution. We conduct extensive experiments to evaluate the performance of our algorithm, and results verify the performance superiority of our algorithm in task completion rate, compared with other thirteen classical and up-to-date scheduling algorithms.

A hybrid deep learning approach to solve optimal power flow problem in hybrid renewable energy systems.

The reliable operation of power systems while integrating renewable energy systems depends on Optimal Power Flow (OPF). Power systems meet the operational demands by efficiently managing the OPF. Identifying the optimal solution for the OPF problem is essential to ensure voltage stability, and minimize power loss and fuel cost when the power system is integrated with renewable energy resources. The traditional procedure to find the optimal solution utilizes nature-inspired metaheuristic optimization algorithms which exhibit performance drop in terms of high convergence rate and local optimal solution while handling uncertainties and nonlinearities in Hybrid Renewable Energy Systems (HRES). Thus, a novel hybrid model is presented in this research work using Deep Reinforcement Learning (DRL) with Quantum Inspired Genetic Algorithm (DRL-QIGA). The DRL in the proposed model effectively combines the proximal policy network to optimize power generation in real-time. The ability to learn and adapt to the changes in a real-time environment makes DRL to be suitable for the proposed model. Meanwhile, the QIGA enhances the global search process through the quantum computing principle, and this improves the exploitation and exploration features while searching for optimal solutions for the OPF problem. The proposed model experimental evaluation utilizes a modified IEEE 30-bus system to validate the performance. Comparative analysis demonstrates the proposed model's better performance in terms of reduced fuel cost of $620.45, minimized power loss of 1.85 MW, and voltage deviation of 0.065 compared with traditional optimization algorithms.

Gait pattern modification based on ground contact adaptation using the robot-assisted training platform (RATP).

Robot-assisted rehabilitation and training systems are utilized to improve the functional recovery of individuals with mobility limitations. These systems offer structured rehabilitation through precise human-robot interaction, outperforming traditional physical therapy by delivering advantages such as targeted muscle recovery, optimization of walking patterns, and automated training routines tailored to the user's objectives and musculoskeletal attributes. In our research, we propose the development of a walking simulator that considers user-specific musculoskeletal information to replicate natural walking dynamics, accounting for factors like joint angles, muscular forces, internal user-specific constraints, and external environmental factors. The integration of these factors into robot-assisted training can provide a more realistic rehabilitation environment and serve as a foundation for achieving natural bipedal locomotion. Our research team has developed a robot-assisted training platform (RATP) that generates gait training sets based on user-specific internal and external constraints by incorporating a genetic algorithm (GA). We utilize the Lagrangian multipliers to accommodate requirements from the rehabilitation field to instantly reshape the gait patterns while maintaining their overall characteristics, without an additional gait pattern search process. Depending on the patient's rehabilitation progress, there are times when it is necessary to reorganize the training session by changing training conditions such as terrain conditions, walking speed, and joint range of motion. The proposed method allows gait rehabilitation to be performed while stably satisfying ground contact constraints, even after modifying the training parameters.

Near-infrared spectroscopy identification method of cashmere and wool fibers based on an optimized wavelength selection algorithm.

Cashmere and wool fibers have similar chemical compositions, making them difficult to distinguish based on their absorption peaks and band positions in near-infrared spectroscopy. Existing studies commonly use wavelength selection or feature extraction algorithms to obtain significant spectral features, but traditional algorithms often overlook the correlations between wavelengths, resulting in weak adaptability and local optimum issues. To address this problem, this paper proposes a recognition algorithm based on optimal wavelength selection, which can remove redundant information and make the model effective in capturing patterns and key features of the data. The wavelengths are rearranged by computing the information gain ratio for each wavelength. Then, the sorted wavelengths are grouped based on equal density, which ensures that all wavelengths within each group have equal information and avoids over-focusing on individual groups. Meanwhile, the group genetic algorithm is used to find the wavelengths with highly informative and search optimal grouped combinations, in order to explore the entire spectrum wavelength. Finally, combined with a partial least squares discriminant analysis(PLS-DA) model, the recognition accuracy reached 97.3 %. The results indicate that, compared to traditional methods such as CARS, SPA, and GA, our method effectively reduces redundant information, selects fewer but more informative wavelengths, and improves classification accuracy and model adaptability.

A hybrid RSM-BPNN-GA approach for optimizing ultrasound-assisted deep eutectic solvents extraction conditions for Mesona chinensis benth. and investigation of the extraction mechanism.

Mesona chinensis Benth (MCB) is the source of the most commonly consumed herbal beverage in Southeast Asia and China and is thus an economically important agricultural plant. Therefore, optimal extraction and production procedures have significant commercial value. Currently, in terms of green chemistry, researchers are investigating the use of greener solvents and innovative extraction techniques to increase extract yields. This study represents the first investigation of the optimal conditions for ultrasound-assisted deep eutectic solvent (DES) extraction from MCB. The major factors influencing ultrasound-assisted DESs were optimized using the response surface methodcentral-genetic algorithm-back propagation neural networks. This model demonstrated superior predictability and accuracy compared to the RSM model. Various types of DESs were used for the extraction of MCB constituents, with choline chloride-ethylene glycol resulting in the highest yield. The optimal conditions for maximal extraction were the use of choline chloride-ethylene glycol (1:4) as the solvent with a 40% water content, an extraction duration of 60 min at 60°C, and maintaining a leaf-to-solvent ratio of 20 mL/g. Noticeable enhancements in Van der Waals forces and more robust interactions between DESs and the target chemicals were observed relative to those seen with ethanol (70%, v/v) or water. This investigation not only introduced an environmentally friendly approach for highly efficient extraction from MCB but also identified the mechanisms underlying the improved extraction efficacy. These findings have the potential to contribute to the broader utilization of MCB and provide valuable insights into the extraction mechanisms utilizing deep eutectic solvents. PRACTICAL APPLICATION: This work describes an efficient and green ultrasound-assisted deep eutectic solvent (DES) method for Mesona chinensis Benth (MCB) extraction. Molecular dynamics was used to examine the intermolecular interactions between the solvent and the extracted compounds. It is anticipated that green and environmentally friendly solvents, such as DESs, will be used in further research on foods and their bioactive components. With the development of the herbal tea industry, new products made of MCB are becoming increasingly popular, thus gradually making it a research hotspot.

Swin-GA-RF: genetic algorithm-based Swin Transformer and random forest for enhancing cervical cancer classification.

Cervical cancer is a prevalent and concerning disease affecting women, with increasing incidence and mortality rates. Early detection plays a crucial role in improving outcomes. Recent advancements in computer vision, particularly the Swin transformer, have shown promising performance in image classification tasks, rivaling or surpassing traditional convolutional neural networks (CNNs). The Swin transformer adopts a hierarchical and efficient approach using shifted windows, enabling the capture of both local and global contextual information in images. In this paper, we propose a novel approach called Swin-GA-RF to enhance the classification performance of cervical cells in Pap smear images. Swin-GA-RF combines the strengths of the Swin transformer, genetic algorithm (GA) feature selection, and the replacement of the softmax layer with a random forest classifier. Our methodology involves extracting feature representations from the Swin transformer, utilizing GA to identify the optimal feature set, and employing random forest as the classification model. Additionally, data augmentation techniques are applied to augment the diversity and quantity of the SIPaKMeD1 cervical cancer image dataset. We compare the performance of the Swin-GA-RF Transformer with pre-trained CNN models using two classes and five classes of cervical cancer classification, employing both Adam and SGD optimizers. The experimental results demonstrate that Swin-GA-RF outperforms other Swin transformers and pre-trained CNN models. When utilizing the Adam optimizer, Swin-GA-RF achieves the highest performance in both binary and five-class classification tasks. Specifically, for binary classification, it achieves an accuracy, precision, recall, and F1-score of 99.012, 99.015, 99.012, and 99.011, respectively. In the five-class classification, it achieves an accuracy, precision, recall, and F1-score of 98.808, 98.812, 98.808, and 98.808, respectively. These results underscore the effectiveness of the Swin-GA-RF approach in cervical cancer classification, demonstrating its potential as a valuable tool for early diagnosis and screening programs.

GA-GBLUP: leveraging the genetic algorithm to improve the predictability of genomic selection.

Genomic selection (GS) has emerged as an effective technology to accelerate crop hybrid breeding by enabling early selection prior to phenotype collection. Genomic best linear unbiased prediction (GBLUP) is a robust method that has been routinely used in GS breeding programs. However, GBLUP assumes that markers contribute equally to the total genetic variance, which may not be the case. In this study, we developed a novel GS method called GA-GBLUP that leverages the genetic algorithm (GA) to select markers related to the target trait. We defined four fitness functions for optimization, including AIC, BIC, R2, and HAT, to improve the predictability and bin adjacent markers based on the principle of linkage disequilibrium to reduce model dimension. The results demonstrate that the GA-GBLUP model, equipped with R2 and HAT fitness function, produces much higher predictability than GBLUP for most traits in rice and maize datasets, particularly for traits with low heritability. Moreover, we have developed a user-friendly R package, GAGBLUP, for GS, and the package is freely available on CRAN (https://CRAN.R-project.org/package=GAGBLUP).

Visco-hyperelastic material model fitting to experimental stress-strain curves using a genetic algorithm and its application to soft tissue simulants.

Ballistic impacts on human thorax without penetration can produce severe injuries or even death of the carrier. Soft tissue finite element models must capture the non-linear elasticity and strain-rate dependence to accurately estimate the dynamic human mechanical response. The objective of this work is the calibration of a visco-hyperelastic model for soft tissue simulants. Material model parameters have been calculated by fitting experimental stress-strain relations obtained from the literature using genetic algorithms. Several parametric analyses have been carried out during the definition of the optimization algorithm. In this way, we were able to study different optimization strategies to improve the convergence and accuracy of the final result. Finally, the genetic algorithm has been applied to calibrate two different soft tissue simulants: ballistic gelatin and styrene-ethylene-butylene-styrene. The algorithm is able to calculate the constants for visco-hyperelastic constitutive equations with high accuracy. Regarding synthetic stress-strain curves, a short computational time has been shown when using the semi-free strategy, leading to high precision results in stress-strain curves. The algorithm developed in this work, whose code is included as supplementary material for the reader use, can be applied to calibrate visco-hyperelastic parameters from stress-strain relations under different strain rates. The semi-free relaxation time strategy has shown to obtain more accurate results and shorter convergence times than the other strategies studied. It has been also shown that the understanding of the constitutive models and the complexity of the stress-strain objective curves is crucial for the accuracy of the method.

Global Path Planning for Articulated Steering Tractor Based on Multi-Objective Hybrid Algorithm.

With the development of smart agriculture, autopilot technology is being used more and more widely in agriculture. Because most of the current global path planning only considers the shortest path, it is difficult to meet the articulated steering tractor operation needs in the orchard environment and address other issues, so this paper proposes a hybrid algorithm of an improved bidirectional search A* algorithm and improved differential evolution genetic algorithm(AGADE). First, the integrated priority function and search method of the traditional A* algorithm are improved by adding weight influence to the integrated priority, and the search method is changed to a bidirectional search. Second, the genetic algorithm fitness function and search strategy are improved; the fitness function is set as the path tree row center offset factor; the smoothing factor and safety coefficient are set; and the search strategy adopts differential evolution for cross mutation. Finally, the shortest path obtained by the improved bidirectional search A* algorithm is used as the initial population of an improved differential evolution genetic algorithm, optimized iteratively, and the optimal path is obtained by adding kinematic constraints through a cubic B-spline curve smoothing path. The convergence of the AGADE hybrid algorithm and GA algorithm on four different maps, path length, and trajectory curve are compared and analyzed through simulation tests. The convergence speed of the AGADE hybrid algorithm on four different complexity maps is improved by 92.8%, 64.5%, 50.0%, and 71.2% respectively. The path length is slightly increased compared with the GA algorithm, but the path trajectory curve is located in the center of the tree row, with fewer turns, and it meets the articulated steering tractor operation needs in the orchard environment, proving that the improved hybrid algorithm is effective.

Multi-Adaptive Strategies-Based Higher-Order Quantum Genetic Algorithm for Agile Remote Sensing Satellite Scheduling Problem.

The agile remote sensing satellite scheduling problem (ARSSSP) for large-scale tasks needs to simultaneously address the difficulties of complex constraints and a huge solution space. Taking inspiration from the quantum genetic algorithm (QGA), a multi-adaptive strategies-based higher-order quantum genetic algorithm (MAS-HOQGA) is proposed for solving the agile remote sensing satellites scheduling problem in this paper. In order to adapt to the requirements of engineering applications, this study combines the total task number and the total task priority as the optimization goal of the scheduling scheme. Firstly, we comprehensively considered the time-dependent characteristics of agile remote sensing satellites, attitude maneuverability, energy balance, and data storage constraints and established a satellite scheduling model that integrates multiple constraints. Then, quantum register operators, adaptive evolution operations, and adaptive mutation transfer operations were introduced to ensure global optimization while reducing time consumption. Finally, this paper demonstrated, through computational experiments, that the MAS-HOQGA exhibits high computational efficiency and excellent global optimization ability in the scheduling process of agile remote sensing satellites for large-scale tasks, while effectively avoiding the problem that the traditional QGA has, namely low solution efficiency and the tendency to easily fall into local optima. This method can be considered for application to the engineering practice of agile remote sensing satellite scheduling for large-scale tasks.

Fuzzy-PID controller based sliding-mode for suppressing low frequency oscillations of the synchronous generator.

A novel intelligent stabilizer is designed to address the issue of low-frequency electromechanical oscillations in a synchronous generator in this article. This stabilizer incorporates three controllers: a three-level sliding mode controller, a fuzzy logic controller, and a proportional-integral-derivative (PID) controller enhanced through genetic algorithm optimization. The discontinuous segments of the first two levels of the sliding mode controller are substituted with fuzzy-PID links, utilizing error and its rate of change to adjust stabilizer parameters. The discontinuous portion of the third level is replaced by a saturation function to constrain current within permissible limits. The advantage of this proposed controller is that it integrates the benefits of the three constituent controllers and is capable of handling a wide range of disturbances. Additionally, thanks to the fuzzy engine, which considers error variations, there is no longer a need to calculate the error derivative, which could amplify measurement noise. The proposed stabilizer is compared to available literature results. As a result, the proposed stabilizer exhibits an undershoot of -0.003, an overshoot of 0.001, a response time of 0.01s, high robustness for parameter variations ranging from 0.5 to 4 times the nominal value, and very rapid suppression of oscillations compared to other controllers.

Multi-area collision-free path planning and efficient task scheduling optimization for autonomous agricultural robots.

Collision-free path planning and task scheduling optimization in multi-region operations of autonomous agricultural robots present a complex coupled problem. In addition to considering task access sequences and collision-free path planning, multiple factors such as task priorities, terrain complexity of farmland, and robot energy consumption must be comprehensively addressed. This study aims to explore a hierarchical decoupling approach to tackle the challenges of multi-region path planning. Firstly, we conduct path planning based on the A* algorithm to traverse paths for all tasks and obtain multi-region connected paths. Throughout this process, factors such as path length, turning points, and corner angles are thoroughly considered, and a cost matrix is constructed for subsequent optimization processes. Secondly, we reformulate the multi-region path planning problem into a discrete optimization problem and employ genetic algorithms to optimize the task sequence, thus identifying the optimal task execution order under energy constraints. We finally validate the feasibility of the multi-task planning algorithm proposed by conducting experiments in an open environment, a narrow environment and a large-scale environment. Experimental results demonstrate the method's capability to find feasible collision-free and cost-optimal task access paths in diverse and complex multi-region planning scenarios.

Design of electrostatic lenses through genetic algorithm and particle swarm optimisation methods integrated with differential algebra.

Genetic algorithm (GA) and particle swarm optimisation (PSO) techniques have been integrated with the differential algebra (DA) method in charged particle optics to optimise an Einzel lens. The DA method is a robust and efficient tool for the calculation of aberration coefficients of electrostatic lenses, which makes use of nonstandard analysis for ray tracing a particle as it is subjected to the field generated by a lens. In this study, initial populations of lenses with random geometrical configurations are generated. These initial populations are then subjected to GA and PSO algorithms to alter the geometry of each lens for a set number of iterations. The lens performance is evaluated by calculating the spot size using the aberrations coefficients up to third-order generated by the DA method. Moreover, a focusing column comprising two lenses and a Wien filter was optimised using GA method.

Predicting Cd accumulation in crops and identifying nonlinear effects of multiple environmental factors based on machine learning models.

The traditional prediction of the Cd content in grains (Cdg) of crops primarily relies on the multiple linear regression models based on soil Cd content (Cds) and pH, neglecting inter-factorial interactions and nonlinear causal links between external environmental factors and Cdg. In this study, a comprehensive index system of multi-type environmental factors including soil properties, geology, climate, and anthropogenic activity was constructed. The machine learning models of the tree-based ensemble, support vector regression, artificial neural network for predicting Cdg of rice and wheat based on the environmental factor indexes significantly improved the accuracy than the traditional models of linear regression based on soil properties. Among them, the tree-based ensemble models of XGboost and random forest exhibited highest accuracies for predicting Cdg of rice and wheat, with R2 in the test dataset of 0.349 and 0.546, respectively. This study found that soil properties, including Cds, pH, and clay, have greater impacts on Cdg of rice and wheat, with combined contribution rates accounting for 65.2 % and 29.7 % respectively. Since wheat sampling areas are located in central and northern China, they are more constrained by precipitation and temperature than rice sampling areas in the south. Geologic and climate factors have a greater impact on Cdg of wheat, with a combined contribution rate of 49.9 %, which is higher than the corresponding rate of 20.9 % in rice. Furthermore, the Cdg of rice and wheat did not exhibit an absolute linear relationship with Cds, and excessively high Cds can reduce the bioconcentration factor of Cd accumulation in crops. Meanwhile, other environmental factors such as temperature, precipitation, elevation have marginal effects on the increase of Cdg of crops. This study provides a novel framework to optimize traditional soil plant transfer models, as well as offer a step towards realizing high precision prediction of Cd content in crops.

Optimization of accelerated solvent extraction of zeaxanthin from orange paprika using response surface methodology and an artificial neural network coupled with a genetic algorithm.

This study aimed to optimize the accelerated solvent extraction (ASE) condition of zeaxanthin from orange paprika using a response surface methodology (RSM) or an artificial neural network (ANN) with a genetic algorithm (GA). Input variables were ethanol concentration, extraction time, and extraction temperature, while output variable was zeaxanthin. The mean squared error and regression correlation coefficient of the developed ANN model were 0.3038 and 0.9983, respectively. Predicted optimal extraction conditions from ANN-GA for maximum zeaxanthin were 100% ethanol, 3.4 min, and 99.2 °C. The relative errors under the optimal extraction conditions were RSM for 10.46% and ANN-GA for 2.18%. We showed that the recovery of hydrophobic zeaxanthin could be performed using ethanol, an eco-friendly solvent, via ASE, and the extraction efficiency could be improved by ANN-GA modeling than RSM. Therefore, combining ASE and ANN-GA might be desirable for the efficient and eco-friendly extraction of hydrophobic functional materials from food resources. Supplementary Information: The online version contains supplementary material available at 10.1007/s10068-023-01514-8.

A genetic algorithm optimization framework for the characterization of hyper-viscoelastic materials: application to human articular cartilage.

This study aims to develop an automated framework for the characterization of materials which are both hyper-elastic and viscoelastic. This has been evaluated using human articular cartilage (AC). AC (26 tissue samples from 5 femoral heads) underwent dynamic mechanical analysis with a frequency sweep from 1 to 90 Hz. The conversion from a frequency- to time-domain hyper-viscoelastic material model was approximated using a modular framework design where finite element analysis was automated, and a genetic algorithm and interior point technique were employed to solve and optimize the material approximations. Three orders of approximation for the Prony series were evaluated at N = 1, 3 and 5 for 20 and 50 iterations of a genetic cycle. This was repeated for 30 simulations of six combinations of the above all with randomly generated initialization points. There was a difference between N = 1 and N = 3/5 of approximately ~5% in terms of the error estimated. During unloading the opposite was seen with a 10% error difference between N = 5 and 1. A reduction of ~1% parameter error was found when the number of generations increased from 20 to 50. In conclusion, the framework has proved effective in characterizing human AC.

Data-driven identification of environmental variables influencing phenotypic plasticity to facilitate breeding for future climates.

Phenotypic plasticity describes a genotype's ability to produce different phenotypes in response to different environments. Breeding crops that exhibit appropriate levels of plasticity for future climates will be crucial to meeting global demand, but knowledge of the critical environmental factors is limited to a handful of well-studied major crops. Using 727 maize (Zea mays L.) hybrids phenotyped for grain yield in 45 environments, we investigated the ability of a genetic algorithm and two other methods to identify environmental determinants of grain yield from a large set of candidate environmental variables constructed using minimal assumptions. The genetic algorithm identified pre- and postanthesis maximum temperature, mid-season solar radiation, and whole season net evapotranspiration as the four most important variables from a candidate set of 9150. Importantly, these four variables are supported by previous literature. After calculating reaction norms for each environmental variable, candidate genes were identified and gene annotations investigated to demonstrate how this method can generate insights into phenotypic plasticity. The genetic algorithm successfully identified known environmental determinants of hybrid maize grain yield. This demonstrates that the methodology could be applied to other less well-studied phenotypes and crops to improve understanding of phenotypic plasticity and facilitate breeding crops for future climates.

Optimal Design of Formulas for a Single Degree of Freedom Tuned Mass Damper Parameter Using a Genetic Algorithm and H2 Norm.

One of the researchers' concerns in structural engineering is to control the dynamic behavior of structures efficiently. The TMD (tuned mass damper) is one of the effective methods of controlling the vibration of structures, and various numerical techniques have been proposed to find the optimal parameters of the TMD. This paper develops a new explicit formula to derive the optimal parameters of the TMD of a single degree of freedom (SDOF) structure under seismic load using a genetic algorithm (GA). In addition, the state-space model and the H2 norm function are used to identify the optimal frequency ratio and damping ratio of the TMD that minimize the overall vibration energy of the structure. The MATLAB curve fitting toolbox is used for the explicit formula proposal, and the validity of the proposed formula is verified through multidimensional performance verification technique. Finally, the TMD parameters of the SDOF structure are applied to the multi-degrees of freedom (MDOF) structure to compare and analyze with the existing research results, and the results of the explicit formula proposed in this paper are confirmed to be excellent. This paper can suggest a new direction for determining the optimal TMD parameters using a GA and can be effectively applied to vibration control problems of various structures.

Structural optimization using a genetic algorithm aiming for the minimum mass of vertical axis wind turbines using composite materials.

A wind turbine comprises multiple components constructed from diverse materials. This complexity introduces challenges in designing the blade structure. In this study, we developed a structural optimization framework for Vertical Axis Wind Turbines (VAWT). This framework integrates a parametric Finite Element Analysis (FEA) model, which simulates the structure's global behavior, with a Genetic Algorithm (GA) optimization technique that navigates the design domain to identify optimal parameters. The goal is to minimize the mass of VAWT structures while adhering to a suite of complex constraints. This framework quantifies the mass reduction impact attributable to material selection and structural designs. The optimization cases indicate that blades made from Carbon Fiber Reinforced Plastics (CFRP) materials are 47.1 % lighter than those made from Glass Fiber Reinforced Plastics (GFRP), while the structural parts are 44.8 % lighter. This work also provides further recommendations regarding the scale and design of the structures. With the materials and structural design established, future studies can expand to include more load cases and detailed designs of specific components.

Optimization of above-ground environmental factors in greenhouses using a multi-objective adaptive annealing genetic algorithm.

The greenhouse environment represents a dynamic, nonlinear system characterized by hysteresis and is influenced by a myriad of interacting environmental parameters, posing a complex multi-variable optimization challenge. This study proposes a multi-objective adaptive annealing genetic algorithm to optimize above-ground environmental factors in greenhouses, addressing the challenges of variable environmental conditions and extensive heating and humidity infrastructure. Initially, after analyzing the multi-objective model of greenhouse above-ground environmental factors, including temperature, relative humidity, and CO2 concentration, a comprehensive multi-objective, multi-constraint model was developed to encapsulate these factors in greenhouse environments. Subsequently, the model optimization incorporated multi-parameter coding of decision variables, a fitness function, and an annealing dynamic penalty factor. Validation conducted at Yangling Agricultural Demonstration Park revealed that the application of multi-objective adaptive annealing genetic algorithms (schemes 1 and 2) significantly outperformed the single-objective genetic algorithm (scheme 3) and the traditional genetic algorithm (scheme 4). Specifically, the improvements included a reduction in average temperature rise by 2.64 °C and 5.29 °C for schemes 1 and 2, respectively, equating to 20 % and 34 % decreases. Additionally, average humidification reductions of 2.39 % and 3.9 % were observed, alongside decreases in the total lengths of heating and humidification pipes by up to 2.99 km and 0.443 km, respectively, with a maximum reduction of 14 % in heating pipes. The integration of an annealing dynamic penalty factor enhanced the adaptive climbing ability of schemes 1 and 2, improving static stability and robustness. Furthermore, the number of iterations required to achieve convergence was reduced by approximately 170-240 times compared to schemes 3 and 4. This reduction in iterations also resulted in a significant decrease in running time by 5-13 min, corresponding to time savings of 31 %-56 %, thereby achieving further optimization.