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In the era of Industry 4.0, the demand fluctuation has become fiercer due to the characteristics of diversification, customisation, and uncertainty. Reconfigurability of manufacturing systems has been proven to be a useful and necessary feature when it comes to handling demand uncertainty. This feature can be achieved through the implementation of reconfigurable manufacturing system (RMS) and delayed reconfigurable manufacturing system (D-RMS). D-RMS is a subclass of RMS that focuses primarily on improving the convertibility of the manufacturing system. The two main phases involved in implementing D-RMS are part family formation and configuration design. Therefore, we proposed a multi-objective joint optimisation method of part family formation and configuration design according to the philosophy of D-RMS. Firstly, we develop a multi-objective joint optimisation model that takes into account investment cost, reconfiguration cost, similarity coefficient, and delayed reconfiguration to optimise the part family and configuration of D-RMS simultaneously. Three types of machine tools namely dedicated machine tools, flexible machine tools, and reconfigurable machine tools are considered in the optimisation model. Secondly, the non-dominated sorting genetic algorithm-III (NSGA-III) is adopted to solve the proposed multi-objective integer programming problem. Finally, numerical experiments are presented to demonstrate the effectiveness of the proposed multi-objective joint optimisation method. [ABSTRACT FROM AUTHOR]

ABSTRACT The recent global trend is the convergence of information and communications technology (ICT). By applying ICT in various fields such as the humanities, new types of products and services are created, and new values that help people's lives can be created. AI can be selected as a representative technology in such convergence ICT. However, applying AI technology to actual production requires ensuring data security. Federated learning (FL) can achieve secure sharing of data, where all parties participate in model training locally and upload it to the server for aggregation. The data never leaves the parties involved, thus solving the problems of data privacy and data silos. However, FL faces issues such as high communication cost, imbalanced performance distribution among participants, and low privacy protection. To achieve a balance between model accuracy, communication cost, fairness, and privacy, this paper proposes a multi‐objective optimization‐based FL algorithm (M‐FedAvg). The multi‐objective optimization problem of maximising the accuracy of the global model, minimising the communication cost, minimising the variance of the accuracy, and minimising the privacy budget is solved by NSGA‐III. The experimental results show that the algorithm proposed can effectively reduce the communication cost of FL and achieve privacy protection for participants without affecting the accuracy of the global model. [ABSTRACT FROM AUTHOR]

The shortest path problem has emerged as one of the most significant problems in network optimization with broad applications in the real world. Modeling these real-world applications includes a variety of ambiguous phenomena that can be estimated using human beliefs. In dealing with such uncertainty, Liu's uncertainty theory is considered a legitimate tool. This paper examines the uncertain multi-objective multi-route shortest path problem (UMOMRSPP) for directed graphs. The zigzag uncertain variables are used to represent uncertain parameters. For UMOMRSPP, the expected and optimistic value models are developed, and their corresponding deterministic transformation is provided. The deterministic models are solved using three existing multi-objective genetic algorithms (MOGAs), namely the hybrid genetic algorithm, non-dominated sorting genetic algorithm (NSGA)-II & NSGA-III and two proposed aspiration level (AL)- based algorithms: AL-based NSGA-II and AL-based NSGA-III. An emergency medical services problem is studied as an application in the context of UMOMRSPP. A comparison between the proposed methods and the MOGAs mentioned above is presented. A sensitivity analysis of objective functions regarding the shape parameter and AL is performed. The performance measure coverage is computed to demonstrate the effectiveness of the proposed methods. This study suggests that AL-based NSGA-II & III provide solutions based on the decision-maker's AL and can handle real-world UMOMRSPP effectively with efficient outputs. [ABSTRACT FROM AUTHOR]

Pareto dominance-based algorithms face a significant challenge in handling many-objective optimization problems. As the number of objectives increases, the sharp rise in non-dominated individuals makes it challenging for the algorithm to differentiate their quality, resulting in a loss of selection pressure. The application of the penalty-based boundary intersection (PBI) method can balance convergence and diversity in algorithms. The PBI method guides the evolution of individuals by integrating the parallel and perpendicular distances between individuals and reference vectors, where the penalty factor is crucial for balancing these two distances and significantly affects algorithm performance. Therefore, a comprehensive adaptive penalty scheme was proposed and applied to NSGA-III, named caps-NSGA-III, to achieve balance and symmetry between convergence and diversity. Initially, each reference vector's penalty factor is computed based on its own characteristic. Then, during the iteration process, the penalty factor is adaptively adjusted according to the evolutionary state of the individuals associated with the corresponding reference vector. Finally, a monitoring strategy is designed to oversee the penalty factor, ensuring that adaptive adjustments align with the algorithm's needs at different stages. Through a comparison involving benchmark experiments and two real-world problems, the competitiveness of caps-NSGA-III was demonstrated. [ABSTRACT FROM AUTHOR]

In this study, a multi-objective optimal design of a planetary gear set was performed considering mass, power loss, and transmission error. In particular, the results were analyzed from a novel perspective. The implemented multi-objective optimal design methodology minimizes the mass, power loss, and vibration with respect to seven macro-geometry variables. The peak-to-peak static transmission error (PPSTE) of each gear pair corresponds to the excitation source of the planetary gear set, and reducing the PPSTE helps minimize vibration. Thus, the PPSTE was calculated using the time-varying mesh stiffness obtained through an analytical model. The non-dominated sorting genetic algorithm-III (NSGA-III) was implemented as a multi-objective optimization algorithm, and the criteria importance through the inter-criteria correlation method was implemented for quantitative comparison purposes regarding the combination of objective functions. The results showed that obtaining an optimal macro-geometry design for a planetary gear set exhibiting good performance required simultaneous consideration of the mass, power loss, and vibration. Furthermore, the results confirmed that a versatile optimization algorithm, such as NSGA-III, should be used to capture the discontinuous solution distribution owing to various constraints of the planetary gear set. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering Management / Gongcheng Guanli Xuebao is the property of Journal of Engineering Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Coal-fired power plants account for a large share of the power generation market in China. The mainstream method of desulfurization employed in the coal-fired power generation sector now is wet flue gas desulfurization. This process is known to have a high cost and be energy-/materially intensive. Due to the complicated desulfurization mechanism, it is challenging to improve the overall sustainability profile involving energy-, cost-, and resource-relevant objectives via traditional mechanistic models. As such, the present study formulated a data-driven many-objective model for the sustainability of the desulfurization process. We preprocessed the actual operation data collected from the desulfurization tower in a domestic ultra-supercritical coal-fired power plant with a 600 MW unit. The extreme random forest algorithm was adopted to approximate the objective functions as prediction models for four objectives, namely, desulfurization efficiency, unit power consumption, limestone supply, and unit operation cost. Three metrics were utilized to evaluate the performance of prediction. Then, we incorporated differential evolution and non-dominated sorting genetic algorithm-III to optimize the multiple parameters and obtain the Pareto front. The results indicated that the correlation coefficient (R2) values of the prediction models were greater than 0.97. Compared with the original operation condition, the operation under optimized parameters could improve the desulfurization efficiency by 0.25% on average and reduce energy, cost, and slurry consumption significantly. This study would help develop operation strategies to improve the sustainability of coal-fired power plants. [ABSTRACT FROM AUTHOR]

As an important part of urban terminal delivery, automated guided vehicles (AGVs) have been widely used in the field of takeout delivery. Due to the real-time generation of takeout orders, the delivery system is required to be extremely dynamic, so the AGV needs to be dynamically scheduled. At the same time, the uncertainty in the delivery process (such as the meal preparation time) further increases the complexity and difficulty of AGV scheduling. Considering the influence of these two factors, the method of embedding a stochastic programming model into a rolling mechanism is adopted to optimize the AGV delivery routing. Specifically, to handle real-time orders under dynamic demand, an optimization mechanism based on a rolling scheduling framework is proposed, which allows the AGV's route to be continuously updated. Unlike most VRP models, an open chain structure is used to describe the dynamic delivery path of AGVs. In order to deal with the impact of uncertain meal preparation time on route planning, a stochastic programming model is formulated with the purpose of minimizing the expected order timeout rate and the total customer waiting time. In addition, an effective path merging strategy and after-effects strategy are also considered in the model. In order to solve the proposed mathematical programming model, a multi-objective optimization algorithm based on a NSGA-III framework is developed. Finally, a series of experimental results demonstrate the effectiveness and superiority of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

With the proliferation of structured applications in intelligent transportation systems, cloud-edge-end collaboration technology has gained widespread attention. In order to reduce the offloading delay and energy consumption of structured dependency subtasks while balancing the load of edge servers, a subtask dependency structure partitioning strategy was proposed in this paper. This policy categorizes the dependencies between subtasks into two types: serial dependencies and parallel dependencies. Based on these classifications, a popular dependency-aware cooperative caching policy (PACCS) was designed, which considers the fitness of popular subtasks with different server resource sizes. Then, we design a delay model, an energy consumption model, and an edge server load balancing model to achieve a multi-objective optimization that integrates system delay, energy consumption, and edge server load balancing using the improved NSGA-III algorithm (S-NSGA-III). Simulation experiments show that under the same experimental conditions, the integrated cost of the S-NSGA-III adaptive optimization scheme proposed in this paper is 13.0% lower than that of the NSGA-II scheme, 12.2% lower than that of the NSGA-III scheme, and 16.5% lower than that of the PeEA scheme. [ABSTRACT FROM AUTHOR]

This paper mainly examines a multi-objective multi-route shortest path problem (MOMRSPP) for a directed graph. For solving MOMRSPP, we have proposed two variants of genetic algorithm (GA), the aspiration level (AL)-based non-dominated sorting genetic algorithm (NSGA)-II and the AL-based NSGA-III. The transportation and logistics problem has been considered as an application in the context of MOMRSPP. A comparison has been performed between the newly developed techniques and some existing GA variants. The sensitivity of objectives has been determined with shape parameters and AL. The coverage has been calculated to assess the effectiveness of the proposed methodologies. Moreover, fuzzy MOMRSPP (FMOMRSPP) and uncertain MOMRSPP (UMOMRSPP) are modeled and solved using GA variants. This research concludes that the proposed AL-based techniques are appropriate for decision-makers concerning AL. [ABSTRACT FROM AUTHOR]

The weapon-target assignment problem is the key to the combat mission of the UAV against the enemy in the battlefield environment. The purpose is to find a reasonable weapon target assignment scheme based on the threat, value and damage probability of the target, so as to improve the combat efficiency. Aiming at the problem that the current multi-objective optimization algorithm has slow convergence speed and poor convergence stability when solving the static weapon-target assignment problem, and it is difficult to adapt to the high real-time performance of the current battlefield, an improved non-dominated sorting genetic algorithm based on reference points is proposed. The initial population is optimized by binary coding attack scheme, and adaptive mutation and crossover strategy as well as population optimization update strategy is introduced. Based on the threat matrix and advantage matrix obtained by evaluating the battlefield situation, the target attack scheme is generated after multiple iterations of the population. Finally, the Pareto solution set satisfying the constraint condition is calculated, and the relative optimal solution in the Pareto frontier is taken as the attack scheme of multi-UAV. Multiple experiments show that under good conditions, the improved algorithm reduces convergence time by 46.74%, reduces target threat value by 50.5%, reduces total flight range by 26.46%, and increases the number of killing targets by 11.76% compared with the original algorithm. It is proved that the algorithm is reasonable and efficient in solving the problem of target assignment of multi-UAV air-to-ground strike mission.

In response to the demand for collaborative computation of multi-cryptographic jobs in cryptographic on-demand services within a cloud environment, a multi-cryptographic job collaborative scheduling algorithm was proposed. This algorithm effectively addressed new challenges in cloud environments, such as a variety of cryptographic algorithm types, high concurrency demands, random cross-job interactions, and sudden increases in workloads. Considering the dependencies among jobs, makespan of jobs and computational power of computing units, the scheduling problem for multi-cryptographic job collaborative service was modeled as a multi-objective optimization workflow scheduling problem. A two-stage “select-sort” scheduling algorithm was proposed. In the selection stage, the improved NSGA-III algorithm was employed to select computing units for cryptographic computing jobs, and in the sorting stage, the execution order was determined based on the urgency of jobs. Simulation results demonstrate that the proposed algorithm outperforms traditional scheduling algorithms in terms of energy consumption, migration costs, and adaptability to transient surges in loads.

This paper presents an efficient data-driven approach for optimizing the utilization of PODE n and hydrogen in reactivity-controlled compression ignition (RCCI) engines. A novel high dimensional model representation (HDMR) technique is adopted to construct a highly accurate surrogate model for the RCCI engine based on the dataset generated by a detailed CFD model that has been calibrated against experimental data. The HDMR model is then coupled with Non-dominated Sorting Genetic Algorithm III (NSGA III), a multi-objective genetic algorithm, to search for the optimal operating conditions where the RCCI engine fueled by PODE n and H 2 achieves the highest combustion performance. Results suggest that the constructed HDMR model is highly accurate, being able to predict the engine performance within milliseconds. The coupled HDMR-NSGA III approach successfully identifies the optimal engine operating condition, which significantly improves the combustion performance and reduces pollutant emissions compared with the base condition. • A new and efficient data driven approach for engine modeling and optimization is proposed in this work. • An improved high dimensional model representation technique is developed and exhibits excellent performance. • This new approach is able to accurately predict the engine combustion characteristics and pollutant emissions. • The combustion strategy for a PODE n and hydrogen fueled RCCI engine is optimized based on this new approach. [ABSTRACT FROM AUTHOR]

The structural design of prestressed arched trusses presents a complex challenge due to the need to balance multiple conflicting objectives such as structural performance, weight, and constructability. This complexity is further compounded by the interdependent nature of the structural elements, which necessitates a comprehensive optimization approach. Addressing this challenge is crucial for advancing construction practices and improving the efficiency and safety of structural designs. The integration of advanced optimization algorithms and decision-making techniques offers a promising avenue for enhancing the design process of prestressed arched trusses. This study proposes the use of three advanced multi-objective optimization algorithms: NSGA-III, CTAEA, and SMS-EMOA, to optimize the structural design of prestressed arched trusses. The performance of these algorithms was evaluated using generational distance and inverted generational distance metrics. Additionally, the non-dominated optimal designs generated by these algorithms were assessed and ranked using multiple multi-criteria decision-making techniques, including SAW, FUCA, TOPSIS, PROMETHEE, and VIKOR. This approach allowed for a robust comparison of the algorithms and provided insights into their effectiveness in balancing the different design objectives. The results of the study indicated that NSGA-III exhibited superior performance with a GD value of 0.215, reflecting a closer proximity of its solutions to the Pareto front, and an IGD value of 0.329, indicating a well-distributed set of solutions across the Pareto front. In comparison, CTAEA and SMS-EMOA showed higher GD values of 0.326 and 0.436, respectively, suggesting less convergence to the Pareto front. However, SMS-EMOA demonstrated a balanced performance in terms of constructability and structural weight, with an IGD value of 0.434. The statistical significance of these differences was confirmed by the Kruskal–Wallis test, with p-values of 2.50 × 10 − 15 for GD and 5.15 × 10 − 06 for IGD. These findings underscore the advantages and limitations of each algorithm, providing valuable insights for future applications in structural optimization. [ABSTRACT FROM AUTHOR]

Copyright of Aero Weaponry is the property of Aero Weaponry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Human activities and climate change have exacerbated the frequency of extreme weather events such as rainstorms and floods, which makes it difficult to accurately quantify the uncertainty characteristics in runoff prediction. Therefore, the lower and upper boundary estimation method (LUBE) has become an important means to quantify uncertainty and has been widely used. However, the traditional interval prediction evaluation system only relies on coverage and width indicators, and performs poorly in single-objective optimization methods, which limits the large-scale application of the LUBE method. Based on this, this study innovatively proposes the prediction interval fitting coefficient (PIFC), and combines the prediction interval coverage probability (PICP) and normalized average width index (PINAW) to construct the coverage width fitting-based criterion (CWFC) for the first time, which broadens and improves the interval prediction evaluation dimension system. Further, the single-objective and multi-objective LUBE interval forecasting models based on the randomized weighted particle swarm algorithm (RWPSO) and the non-dominated sorting genetic algorithms III (NSGA-III) are constructed in this study. The verification results of cascade hydropower stations in the Yalong river basin show that the calculation efficiency and prediction effect of the single target interval prediction model are both improved after the introduction of PIFC. Under the CWFC objective function, the PINAW and PIFC indexes in the prediction interval are significantly better, and the PICP gap is smaller. Under multi-objective conditions (PICP, PINAW and PIFC), the Pareto non-inferior solution set can provide more choices for decision makers. During the flood season, PICP can reach more than 93%, PINAW is controlled below 10%, and PIFC can reach more than 0.95. This fully proves that the performance of interval prediction has been significantly improved after the introduction of PIFC, and the research results can provide a new way for basin interval prediction. [ABSTRACT FROM AUTHOR]

Feature selection plays an essential role in enhancing the efficiency and effectiveness of machine learning models, particularly in the context of large-scale datasets where the dimensionality of features presents significant challenges. This research presents a novel approach to feature selection, a critical aspect of enhancing machine learning model efficiency, particularly in the context of large-scale datasets. By integrating the Flamingo Search Algorithm (FSA), Non-dominated Sorting Genetic Algorithm III (NSGA-III), and Regularised Extreme Learning Machine (RELM), the proposed method addresses limitations in existing feature selection and multi-objective optimisation algorithms. Leveraging FSA’s emulation of flamingo behaviours, the approach achieves a balance between global exploration and local exploitation, mitigating issues like premature convergence and local optima. Integration with NSGA-III enhances multi-objective optimisation capabilities, maintaining a delicate equilibrium between convergence and diversity. FSA-RELM is employed for accurate feature assessment, given its rapid learning and suitability for large datasets with multiple labels. Experimental evaluations demonstrate the proposed method’s superiority in feature selection accuracy, classification performance, and computational efficiency compared to existing approaches. This research contributes to advancing feature selection methodologies, offering a comprehensive solution for high-dimensional datasets in machine learning and data mining applications. [ABSTRACT FROM AUTHOR]

The key of the multi-objective optimization of injection molding processes lies in achieving a balance between the accuracy of the surrogate model and the multiple objectives while taking the diversity and interdependence of process parameters into consideration. However, the sampling process for building high-precision surrogate models requires a large number of sample points, resulting in high modeling costs for other regions. Moreover, the selection of Pareto fronts often relies solely on the magnitudes of objective values, without considering the uncertainties associated with the information. To address these issues, this research proposes a novel multi-objective optimization method for injection molding process parameters, using hierarchical sampling and integrated entropy weighting. The method introduces a unique hierarchical sampling approach to enhance the accuracy of the surrogate model in injection molding, with a specific focus on critical components. Additionally, our method incorporates entropy calculations for multiple objective defect value parameters during the multi-objective optimization process, enhancing the rationality of the optimization process. The proposed method is utilized to optimize the injection molding parameters of a thin-walled propeller blade. The result shows that our surrogate model fits well and exhibits superior performance compared to the response surface method in optimizing multiple objectives. [ABSTRACT FROM AUTHOR]

Injection molding is a prevalent method for producing plastic components, yet determining the ideal process parameters has predominantly relied on heuristic approaches. In this research, a data‐driven injection molding process optimization framework is developed to simultaneously minimize warpage, cycle time, and clamping force. Employing multi‐objective Bayesian optimization (MBO), the framework is applied to a fan blade model for verification. Incorporating those objectives enables the selection of an injection molding machine with the proper maximum clamping force within acceptable tolerance and production time. Furthermore, non‐moldable regimes are considered in design space, which allows less prior knowledge for setting design space. The optimized results are compared with those obtained using NSGA‐III, a well‐established genetic algorithm‐based optimization technique, as a benchmark. The optimization frameworks produce a Pareto front for the three‐dimensional outputs, revealing distinct trade‐off relationships between cycle time and warpage, as well as clamping force and warpage. The MBO framework demonstrates a superior Pareto front compared to NSGA‐III when utilizing a limited data set, underscoring its benefits in scenarios where costly simulations or experiments are necessary. These findings are anticipated to contribute to the optimization of manufacturing processes, ultimately enhancing productivity in real‐world industries. [ABSTRACT FROM AUTHOR]

This study developed an optimization model for the strategic location of maintenance resource supply sites and the scheduling of multiple resources following failures in urban metro systems, with the objective of enhancing system resilience. The model employs a multi-objective optimization framework, focusing primarily on minimizing resource scheduling time and reducing costs. It incorporates critical factors such as spatial location, network topology, station size, and passenger flow. A hybrid method, combining the non-dominated sorting genetic algorithm III and the technique for order of preference by similarity to ideal solution, is used to solve the model, with its effectiveness confirmed through a case study of the Nanjing Metro system. The simulation results yielded an optimal number of 21 maintenance resource supply stations and provided their placement. In the event of large-scale failures, the optimal resource scheduling strategy ensures demand satisfaction rates exceed 90% at critical stations, maintaining an overall rate of 87.09%, therefore significantly improving resource scheduling efficiency and the system's emergency response capabilities and enhancing the physical resilience and recovery capabilities of the urban metro system. Moreover, the model accounts for economic factors, striving to balance emergency response capabilities with production continuity and cost efficiency through effective maintenance strategies and resource utilization. This approach provides a systematic framework for urban metro systems to manage sudden failures, ensuring rapid recovery to normal operations and minimizing operational disruptions in scenarios of limited resources. [ABSTRACT FROM AUTHOR]

With the continuous improvement of construction management standards, thorough investigation into various management objectives becomes crucial. To address the current gaps in project management concerning time, cost, safety, and carbon emissions interrelationships, this study adopts the multi-objective optimization (MOP) theory and makes the following contributions: (1) proposes an innovative carbon emission model, which enhances the cost model by incorporating carbon cost factors, and establishes a MOP model covering time–cost–safety–carbon emissions; (2) validates the effectiveness of the proposed model and the feasibility of algorithms through empirical research and algorithm comparison analysis—the research demonstrates that NSGA-III exhibits significant advantages in solving MOP problems of this scale; (3) applies the entropy-weighted VIKOR method to objectively analyze the solution set obtained by NSGA-III for optimal solution selection. This study provides practical management tools for project managers and offers significant insights for researchers in construction management regarding multi-objective problem-solving and algorithm selection. [ABSTRACT FROM AUTHOR]

Thermochromic (TC) smart window which allows modulating the transmission of solar heat energy and sunlight in response to environmental conditions has great potential for improving the building energy and daylighting performance simultaneously. In this study, the multi-objective optimization method is used to determine the ideal optical properties of TC windows. Parametric assessment is firstly conducted to investigate the influence of several crucial parameters. Multi-objective optimization is then carried out by applying Non-dominated Sort Genetic Algorithm-III, maximizing daylight availability and building energy efficiency. Finally, the decision-making of the Pareto frontier is performed to achieve the ideal TC windows in 32 representative cities in different climatic zones of China. Results demonstrate that increasing the luminous transmittance at low-temperature and decreasing luminous transmittance modulation ability are beneficial to daylighting and energy performance. Lower solar transmittance at low-temperature contributes to building energy savings in Beijing, Shanghai, Guangzhou and Kunming, except for Harbin. Higher solar transmittance modulation ability is desired for building energy efficiency. The optimum value for the transition temperature is in the range of 23.67–38.85°C. The building energy consumption can be reduced by 13.01%, and the desired daylighting hours can be improved by 8.90%, relative to the clear double-glazed windows. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Fiber-reinforced polymer (FRP) is widely used for retrofitting structural elements due to its easy application. However, establishing a retrofit strategy is challenging due to conflicting objectives, such as cost and performance level, requiring optimization for effective decision-making. This study proposes a many-objective optimization model for seismic retrofitting using FRP and the Non-dominated Sorting Genetic Algorithm (NSGA)-III. The model's efficacy was demonstrated through two numerical examples: a reinforced concrete building retrofitted with FRP jackets and a masonry-infilled reinforced concrete building retrofitted with FRP bracings. Each example included three objective functions and multiple constraints. Nonlinear static pushover analysis provided optimal strategies to enhance base shear and energy dissipation while minimizing costs and retrofitting locations. Among the Pareto-optimal solutions, the optimal solution with the minimum Euclidean distance was selected. NSGA-III offered a wider distribution and more Pareto-optimal solutions compared to NSGA-II, demonstrating its potential in addressing many-objective problems related to retrofit decision-making.

The PROSPECT model, widely employed for leaf radiation transfer analysis, relies heavily on input biochemical parameters to calculate spectral reflectance. This dependence often results in similar simulated spectra for different parameter combinations, which complicates the inversion of leaf chlorophyll content (Cab). To address this ill-posed problem, we enhanced the model's application by integrating a support vector machine (SVM)-based parameter combination discriminator with the Look-Up Table (LUT) constructed from the PROSPECT model. We marked samples in the LUT to reflect their closeness to measured parameters, facilitating the identification of reasonable versus unreasonable parameter combinations. The discriminator could effectively discriminate between reasonable and unreasonable parameter combinations, achieving accuracies of 0.894 and 0.888 in the training and test sets, respectively. The discriminator was then employed to refine the LUT, and an improved third-generation non-dominated ranking genetic algorithm (NSGA-III) was used to optimize the extreme learning machine. The inversion of rice Cab using the refined LUT and the NSGA-III demonstrated substantial improvements. The LUT was significantly improved after integration with the discriminator, yielding R2 and RMSE of 0.665 and 7.220 ​μg ​cm−2, respectively. The NSGA-III inversion, which utilized the “constraint method” with discriminator results as optimization objectives, achieved the best inversion accuracy, with R2 and RMSE values of 0.809 and 4.788, respectively. This study demonstrates that the effective use of a parameter discriminator can significantly enhance the accuracy of Cab inversion based on the PROSPECT model, offering a substantial advancement in addressing its inherent ill-posed challenges.

Maximizing protein expression levels poses a major challenge in bioengineering. To increase protein expression levels, numerous factors, including codon bias, codon context bias, hidden stop codons, homologous recombination, suitable guanine-cytosine ratio, and hairpin loop structure, are crucial and quantified by six objective functions: CAI, CPB, HSC, HD, GC3, and SL. Optimizing these six objectives simultaneously constitutes a multi-objective optimization problem, aiming to identify the favorable Pareto solutions rather than a singular optimal solution. However, achieving satisfactory solutions requires numerous cycles and solutions, thus leading to a large number of functional evaluations. While there are frameworks for multi-objective optimization problems, they often lack efficient support for objective function computation in protein encoding. In this paper, we proposed a method to design a set of coding sequences (CDSs) based on non-dominated sorting genetic algorithm III (NSGA-III), accelerated using NVIDIA graphical processing units (GPUs). Experimental results indicated that our method is 15,454 times faster than the Pymoo framework and is evaluated using 100 solutions and 100 cycles. Since our GPU implementation facilitated the use of larger solutions and more cycles, we were able to design a superior set of CDSs by increasing solutions to 400 and cycles to 12,800. In addition, our NSGA-III-based method consistently surpassed the NSGA-II approach when the number of cycles exceeded 3200 by utilizing 100 solutions. Finally, we observed that a gradual reduction of the mutation probability as the number of cycles increased yielded better quality results than maintaining a fixed mutation probability.

Abstract Virtual network functions (VNFs) have gradually replaced the implementation of traditional network functions. Through efficient placement, the VNF placement technology strives to operate VNFs consistently to the greatest extent possible within restricted resources. Thus, VNF mapping and scheduling tasks can be framed as an optimization problem. Existing research efforts focus only on optimizing the VNFs scheduling or mapping. Besides, the existing methods focus only on one or two objectives. In this work, we proposed addressing the problem of VNFs scheduling and mapping. This work proposed framing the problem of VNFs scheduling and mapping as a multi-objective optimization problem on three objectives, namely (1) minimizing line latency of network link, (2) reducing the processing capacity of each virtual machine, and (3) reducing the processing latency of virtual machines. Then, the proposed VNF-NSGA-III algorithm, an adapted variation of the NSGA-III algorithm, was used to solve this multi-objective problem. Our proposed algorithm has been thoroughly evaluated through a series of experiments on homogeneous and heterogeneous data center environments. The proposed method was compared to several heuristic and recent meta-heuristic methods. The results reveal that the VNF-NSGA-III outperformed the comparison methods.

To address the issues of both water resources allocation and sustainable management in agriculture areas with rising food demand, a simulation-optimization framework based on Flopy and Pymoo was proposed and developed for canal-well combined irrigation districts. The proposed framework first solved the many-objective water resources allocation problem which integrates groundwater simulation, crop production, and farmer income modules to quantitatively reveal the various trade-offs and synergies by using NSGA-III algorithm. The Entropy-TOPSIS method was then applied to recommend proper water allocation schemes. The proposed framework was further tested in Baojixia irrigation district considering various water supply and crop demand scenarios based on Copula-based uncertainty analysis. The Key findings are as follows: (1) the proposed framework could effectively optimize conjunctive water resources allocation problems of both surface water and groundwater; (2) low supply combined with high demand (p=0.17) is more likely to occur than high supply with high demand (p=0.02); (3) increased crop demand and restricted surface water negatively impact both water productivity and groundwater sustainability; and (4) the cumulative groundwater drawdown of recommend schemes is 36.9 % and 6.5 % higher under low to medium supply scenarios, while water productivity of recommend schemes decreases 28.2 % and 9.7 % with high and medium demand. This framework could provide useful insights for sustainable agricultural water management in canal-well combined irrigation district with various uncertainties in supply and demand scenarios.

The advancement toward sustainable infrastructure presents complex multi-objective optimization (MOO) challenges. This paper expands the current understanding of design frameworks that balance cost, environmental impacts, social factors, and structural integrity. Integrating MOO with multi-criteria decision-making (MCDM), the study targets enhancements in life cycle sustainability for complex engineering projects using precast modular road frames. Three advanced evolutionary algorithms—NSGA-II, NSGA-III, and RVEA—are optimized and deployed to address sustainability objectives under performance constraints. The efficacy of these algorithms is gauged through a comparative analysis, and a robust MCDM approach is applied to nine non-dominated solutions, employing SAW, FUCA, TOPSIS, PROMETHEE, and VIKOR decision-making techniques. An entropy theory-based method ensures systematic, unbiased criteria weighting, augmenting the framework's capacity to pinpoint designs balancing life cycle sustainability. The results reveal that NSGA-III is the algorithm converging towards the most cost-effective solutions, surpassing NSGA-II and RVEA by 21.11% and 10.07%, respectively, while maintaining balanced environmental and social impacts. The RVEA achieves up to 15.94% greater environmental efficiency than its counterparts. The analysis of non-dominated solutions identifies the A 4 design, utilizing 35 MPa concrete and B500S steel, as the most sustainable alternative across 80% of decision-making algorithms. The ranking correlation coefficients above 0.94 demonstrate consistency among decision-making techniques, underscoring the robustness of the integrated MOO and MCDM framework. The results in this paper expand the understanding of the applicability of novel techniques for enhancing engineering practices and advocate for a comprehensive strategy that employs advanced MOO algorithms and MCDM to enhance sustainable infrastructure development. [ABSTRACT FROM AUTHOR]

Maximizing protein expression levels poses a major challenge in bioengineering. To increase protein expression levels, numerous factors, including codon bias, codon context bias, hidden stop codons, homologous recombination, suitable guanine-cytosine ratio, and hairpin loop structure, are crucial and quantified by six objective functions: CAI, CPB, HSC, HD, GC3, and SL. Optimizing these six objectives simultaneously constitutes a multi-objective optimization problem, aiming to identify the favorable Pareto solutions rather than a singular optimal solution. However, achieving satisfactory solutions requires numerous cycles and solutions, thus leading to a large number of functional evaluations. While there are frameworks for multi-objective optimization problems, they often lack efficient support for objective function computation in protein encoding. In this paper, we proposed a method to design a set of coding sequences (CDSs) based on non-dominated sorting genetic algorithm III (NSGA-III), accelerated using NVIDIA graphical processing units (GPUs). Experimental results indicated that our method is 15,454 times faster than the Pymoo framework and is evaluated using 100 solutions and 100 cycles. Since our GPU implementation facilitated the use of larger solutions and more cycles, we were able to design a superior set of CDSs by increasing solutions to 400 and cycles to 12,800. In addition, our NSGA-III-based method consistently surpassed the NSGA-II approach when the number of cycles exceeded 3200 by utilizing 100 solutions. Finally, we observed that a gradual reduction of the mutation probability as the number of cycles increased yielded better quality results than maintaining a fixed mutation probability. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks have an event-driven nature that includes a colossal count of sensors to generate unpredictable network traffic that causes network congestion Due to immoderate congestion packets remain in the queue for a longer period. This incites increased packet delay and packet losses. Existing congestion control strategies are either inelastic or have limited elasticity. Numerous elastic approaches, such as Cuckoo Fuzzy PID (CFPID), PSO (Particle Swarm Optimization)-neural PID, and PSO Fuzzy PID have been introduced using meta-heuristics and evolutionary algorithms; nevertheless, these are slow and prematurely converge, indicating an NP-hard problem. Moreover, Multi-objective Proportional Integral Derivative (MPID) is articulated as a multi-objective optimization problem, which is followed by many researchers but suffers from overshoot defects. To address these issues, the hybrid controller is proposed using the Non-dominated Sorting Genetic Algorithm III and MPID called N3-MPID to optimize the data transmission rate for the source node. The four indirect objectives that are Integral Square Error, Integral Absolute Error, Integral of Time multiplied Absolute Error, and Integral of Time multiplied Square Error are taken to design a novel optimal MPID fitness function. To merge these objectives into a single goal, an equally weighted procedure is adopted that is efficient and well-distributed. The proposed technique is simulated using a Network Simulator and is compared with CFPID using principal metrics. Simulation results depict that the N3-MPID outflanks existing algorithms by an 11.76% increase in packet delivery ratio while minimizing packet loss, average delay, and deviation of queue length by 43.16%, 20.55%, and 15.05%, respectively. [ABSTRACT FROM AUTHOR]

This work suggests to optimize the geometry of a quadrupole magnet by means of a genetic algorithm adapted to solve multi-objective optimization problems. To that end, a non-domination sorting genetic algorithm known as NSGA-III is used. The optimization objectives are chosen such that a high magnetic field quality in the aperture of the magnet is guaranteed, while simultaneously the magnet design remains cost-efficient. The field quality is computed using a magnetostatic finite element model of the quadrupole, the results of which are post-processed and integrated into the optimization algorithm. An extensive analysis of the optimization results is performed, including Pareto front movements and identification of best designs. [ABSTRACT FROM AUTHOR]

The scheduling of tower crane operations is a complex process. Overlapping areas between tower cranes often lead to increased collision possibilities, resulting in additional tower crane operation complexity. Single objectives related to time or economic aspects were always considered in dealing with this issue, which neglected other objectives and the relationships between different objectives. Therefore, this article proposes a novel method for the schedule of prefabricated component lifting tasks on the construction site, integrating the multi-objective optimization model with the decision-making method with the aim of minimizing energy consumption costs and minimizing the amplitude of the costs among multiple tower cranes. A non-dominated sorting genetic algorithm-III (NSGA-III) written in Python is used as the multi-objective optimization algorithm—which considers the selection of tasks for each tower crane and the order of lifting for each tower crane and technique for order preference by similarity to an ideal solution (TOPSIS), and is applied as the decision-making method for ranking the Pareto front. Then, a green construction production and education integration training building construction project located in Jinan, China is used as the case study to verify that the method is practical and reasonable. The results show that conflicts can be effectively avoided, energy consumption costs reduced, and equipment utilization increased by rationally distributing lifting tasks among multiple overlapping tower cranes. And among the top 11 solutions, the lifting tasks and priorities for tower crane 1 are close to the same. In contrast, the task lifting for tower crane 2 was assigned based on the balance of the energy consumption costs of the two tower cranes. The discovery of this article is helpful to eliminate collisions, interference, and frequent start and stop of several tower cranes, so as to realize the safe, stable, and efficient operation of the construction site. [ABSTRACT FROM AUTHOR]

In the field of predictive maintenance, accurately predicting the remaining useful life (RUL) of equipment is critical to optimizing operations and avoiding unexpected failures. Traditional models such as Gaussian Process Regression (GPR), Particle Filter (PF), and Kalman Filter (KF) have been widely used, each with their own strengths and limitations. The motivation behind this study stems from the need to improve the accuracy and reliability of RUL predictions. Given the critical importance of predictive maintenance across a variety of industries, improved predictive models can provide significant operational and economic benefits. The main issues addressed are the limitations inherent in individual predictive models. GPR prediction gap or high initial error of his KF. This can lead to suboptimal RUL estimates. To address this problem, an integrated approach combining particle filtering and Gaussian process regression (PF-GPR) was proposed and developed. This integration aims to leverage the strengths of PF and GPR and potentially alleviate the limitations of the individual models. The performance of the PF-GPR model is evaluated and compared with the standalone His GPR, PF, and KF models using the prediction error and root mean square error (RMSE) at different points in the aging process of device #36. The results show that the PF-GPR model consistently outperforms the individual models in terms of both prediction error and RMSE, and significantly improves the accuracy and accuracy of RUL prediction. The PF-GPR model showed excellent performance in his RUL prediction for device #36, achieving the lowest prediction error and RMSE values at all time points as follows: B. The prediction error is only 2.30E-1 and the RMSE after 100 minutes is 7. 12E-3, which significantly outperforms his standalone GPR, PF, and KF models. The PF-GPR model demonstrated its excellent ability to provide robust and reliable predictions, highlighting the benefits of integrating different prediction methods into predictive maintenance applications. [ABSTRACT FROM AUTHOR]

In the context of accelerating urbanisation, the issue of urban stormwater flooding security has garnered increasing attention. Further development of urban stormwater management techniques is imperative to achieve more stable, precise, and expeditious simulation outcomes. The calibration of model parameters, which is a pivotal phase in stormwater simulation endeavours, is hampered by challenges such as substantial subjectivity, time intensiveness, and low efficiency. Therefore, this study introduces a parameter calibration model coupled with the Non-dominated Sorting Genetic Algorithm III (NSGA-III). This model utilises the Nash–Sutcliffe efficiency (NSE) and peak relative error (PE) values for various rainfall events as objective functions to calibrate and assess the study target. The two rainfalls used for rate determination had NSE values greater than 0.9 and absolute PE values less than 0.17; the rainfall used for validation had NSE values greater than 0.9 and absolute PE values less than 0.27. Thus, the results of the model for the rate determination of the parameters are reliable. In addition, the inverted generation distance and hypervolume values indicate that the iterative process of the algorithm during population evolution demonstrated stable iterative outcomes and ensured sound population quality. Both reach relative stability after 40 iterations. In conclusion, the proposed multi-objective parameter calibration model integrated with NSGA-III offers dependable calibration results and robust computational efficacy, presenting novel avenues and perspectives for urban stormwater model parameter calibration and simulation. [ABSTRACT FROM AUTHOR]

Graded cathode catalyst layer (CCL) design can improve performance and reduce the cost of proton exchange membrane fuel cells (PEMFCs). However, it sacrifices the uniformity of oxygen concentration, thereby shortening the duration of PEMFC. Thus, this paper introduces a many-objective optimization algorithm called the Non-dominated sorting genetic algorithm-third version (NSGA-III) to optimize the performance, oxygen distribution quality, and cost of PEMFC with a graded CCL structure via four evaluation indexes. The four evaluation indexes include the new proposed local oxygen transport resistance and the uniformity of oxygen concentration at the Pt surface. Furthermore, artificial intelligence (AI) based models are established to enhance the optimization efficiency. Then, the NSGA-III algorithm is used to optimize the graded CCL structure parameters and operating voltage to find the Pareto solutions. Moreover, two optimization strategies are proposed to improve the power density and uniformity of oxygen concentration of PEMFC or to save the precious Pt and ionomer while maintaining the performance. Lastly, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is used to select the optimal solution for each strategy. Results show that the AI-based models have enough predicting abilities, and the selected optimal solutions can satisfy the goals of the corresponding strategy, which provides efficient ways to develop high-performance, long-lifetime, or low-cost, long-lifespan PEMFCs. • NSGA-III is used to optimize performance, oxygen distribution, and cost of PEMFC. • Two new evaluation indexes are proposed to optimize PEMFC with graded CCL design. • AI-based models for PEMFC are established to enhance optimization efficiency. • High-performance, long-lifetime PEMFC is obtained via many-objective optimization. • Low-cost, long-lifespan PEMFC is developed via many-objective optimization. [ABSTRACT FROM AUTHOR]

Land use allocation (LUA) is of prime importance for the development of urban sustainability and resilience. Since the process of planning and managing land use requires balancing different conflicting social, economic, and environmental factors, it has become a complex and significant issue in urban planning worldwide. LUA is usually regarded as a spatial multi-objective optimization (MOO) problem in previous studies. In this paper, we develop an MOO approach for tackling the LUA problem, in which maximum economy, minimum carbon emissions, maximum accessibility, maximum integration, and maximum compactness are formulated as optimal objectives. To solve the MOO problem, an improved non-dominated sorting genetic algorithm III (NSGA-III) is proposed in terms of mutation and crossover operations by preserving the constraints on the sizes for each land use type. The proposed approach was applied to KaMavota district, Maputo City, Mozambique, to generate a proper land use plan. The results showed that the improved NSGA-III yielded better performance than the standard NSGA-III. The optimal solutions produced by the MOO approach provide good trade-offs between the conflicting objectives. This research is beneficial for policymakers and city planners by providing alternative land use allocation plans for urban sustainability and resilience. [ABSTRACT FROM AUTHOR]

How to evaluate the reliability of deep soft rock tunnels under high stress is a very important problem to be solved. In this paper, we proposed a practical stochastic reliability method based on the third-generation non-dominated sorting genetic algorithm (NSGA–III) and eXtreme Gradient Boosting (XGBoost). The proposed method used the Latin hypercube sampling method to generate the dataset samples of geo-mechanical parameters and adopted XGBoost to establish the model of the nonlinear relationship between displacements and surrounding rock mechanical parameters. And NSGA–III was used to optimize the surrogate model hyper-parameters. Finally, the failure probability was computed by the optimized surrogate model. The proposed approach was firstly implemented in the analysis of a horseshoe-shaped highway tunnel to illustrate the efficiency of the approach. Then, in comparison to the support vector regression method and the back propagation neural network method, the feasibility, validity and advantages of XGBoost were demonstrated for practical problems. Using XGBoost to achieve Monte Carlo simulation, a surrogate solution can be provided for numerical simulation analysis to overcome the time-consuming reliability evaluation of initial support structures in soft rock tunnels. The proposed method can evaluate quickly the large deformation disaster risks of non-circular deep soft rock tunnels. [ABSTRACT FROM AUTHOR]