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This study investigated a multi-objective resequencing scheduling problem in the automotive manufacturing systems due to operational requirements on the color-batching of the paint shop and sequential requirements on the sequence adherence of an assembly shop. Resequencing cars as color-oriented batches reduced the costs of color changes and operational costs for paint shops. Also, assembly shops required paint shops to complete cars with fewer delays so that high sequence adherence with its demand was assured. Based on real-world applications, we investigated two contradictory objectives-color change costs and sequence tardiness-in a single-machine flowshop scheduling environment. A multi-objective-deep-Q-network algorithm was developed to determine the Pareto frontier. Reward shaping was designed to improve the convergence of the neural network. The 2D-folded-normal distribution was designed to sample the preference, which made the exploration and exploitation of the neural network more comprehensive and improved the training efficiency. Two experiments were conducted and showed that the proposed approach outperformed the meta-heuristic algorithm and the envelope Q-learning algorithm in solving time, performance, the convergence of the neural network, and the diversity of the Pareto frontier. Therefore, the proposed approach can be used in automotive paint shops to improve scheduling efficiency and reduce operational costs. [ABSTRACT FROM AUTHOR]

Modeling analysis of underwater pulsed arc discharge can predict the characteristics of plasma channels, providing theoretical guidance for the practical application of underwater pulsed discharge. Due to the complexity of experimental diagnostics for 'kA'-level underwater pulsed discharge, there is currently a lack of precise experimental data to support the initial value selection and result optimization of the modeling. This paper established a plasma channel model for underwater pulsed arc discharge. In conjunction with the Saha ionization equilibrium equation, the model was capable of simulating the current, pressure, temperature, and electron density of the channel after gap breakdown. By utilizing spectroscopic diagnostic data and a multi-objective optimization algorithm, the initial values and key parameters of the model were reasonably determined. The simulation results were in good agreement with the experimental diagnostic results, reasonably representing the trends in electron density and blackbody radiation temperature. Moreover, the model was applicable for reasonably explaining the emission spectral mechanism of the arc channel and shock waves prediction under different discharge conditions. [ABSTRACT FROM AUTHOR]

Geothermal energy is utilized due to its sustainability and ability to provide a continuous low-emission source of heat, making it an ideal complement to the high-efficiency requirements of modern power plants. This study investigates a novel configuration coupling a solid oxide fuel cell-gas turbine unit with a dual-flash binary geothermal structure, enhanced by low-temperature electricity generation and hydrogen production subsystems. This configuration incorporates a regenerative steam Rankine cycle and a proton exchange membrane electrolyzer to achieve an efficient overall design. The setup is evaluated from thermodynamic and economic perspectives using a non-dominated sorting genetic algorithm-II for optimization. A complete parametric study assesses the sensitivity of critical variables. Multi-objective optimization is conducted across three scenarios considering exergy efficiency, hydrogen production rate, sum unit cost of products, and the payback period as objective functions. Results indicate an optimal exergy efficiency of 56.95% and a hydrogen production rate of 0.22 kg/h, with a product unit cost of $7.06/GJ and a payback period of 1.12 years. The parametric study underscores the significant impact of the number of solid oxide fuel cells and their existing density on key variables of the presented configuration. This study highlights the system's potential for efficient and economically feasible power and hydrogen production. • Investigating a novel geothermal-solid oxide fuel cell-gas turbine configuration. • Combining Rankine cycle and proton exchange membrane electrolyzer for higher efficiency. • Enhancing the thermodynamic and economic performances through multi heat recovery. • Achieving 56.95% exergy efficiency and 0.22 kg/h hydrogen production rate. • Achieving unit cost of $7.06/GJ and payback period of 1.12 years. [ABSTRACT FROM AUTHOR]

Summary: Matrix factorization (MF)‐based recommender systems (RSs) as black‐box models fail to provide explanations for the recommended items. While some models attain a degree of explainability by integrating neighborhood algorithms, which compute explainability based on the preferences of proximate users, they overlook the contribution of the subjective preferences of the target user to enhancing model explainability, resulting in suboptimal model explainability. To address this problem, an explainable RS directed by reconstructed explanatory factors and multi‐modal matrix factorization (ERS‐REFMMF) is proposed. By integrating users' subjective sentiment and preference features into the rating matrix to form a multi‐modal matrix, ERS‐REFMMF utilizes the Funk‐singular value decomposition method at the foundational layer to decompose the multi‐modal matrix and generate a candidate item set. At the upper layer, explainability is constructed based on the target user's subjective preferences and latent features derived from MF, and the final recommended list is optimized for accuracy, diversity, novelty, and explainability through multi‐objective optimization algorithms. ERS‐REFMMF models around users' explicit preferences and latent associations, reconstructs explainability with hybrid factors, and enhances overall performance through a many‐objective optimization algorithm. Experimental results on real datasets demonstrate that the proposed model is competitive in both phases compared to existing recommendation methods. [ABSTRACT FROM AUTHOR]

Any multi-objective optimization algorithms are usually introduced by developing a single-objective algorithm. In the current study, a recently developed physics-based algorithm, equilibrium optimizer (EO), is regarded and is extended into a multi-objective algorithm called MOEO. The optimal control problem for cancer treatment is solved using this novel algorithm to confirm its performance. Considering the cancer treatment as an optimal control problem to reduce both the concentration of cancer cells and the concentration of the drugs during treatment is the multi-objective optimization problem of this research. Due to this, this problem's solution leads to the optimal drug administration protocol to minimize cancer cell concentration and drug concentration. This multi-objective problem is solved using the novel MOEO algorithm, and the results are compared with the previous works. The Pareto front curve obtained from the algorithm offers a set of the most optimal solutions. According to the treatment selection criteria, one of these points is selected as the drug-prescribing protocol. The problem is solved based on three different case studies, where the result comparison shows that the MOEO's performance is great. Considering different cost functions, it shows that using integral of the time-weighted absolute (ITA) objective function gets the fastest response with a higher drug dose while using integral of the absolute (IS) objective function gets lesser drugs. [ABSTRACT FROM AUTHOR]

In this paper, the multi-objective optimization problem (MOOP) of selecting suitable materials from four materials for phononic crystals layouts is investigated based on an optimization method combined nondominated sorting-based genetic algorithm II (NSGA-II) and finite element method (FEM). Driven by different optimization objectives, the changes in the optimized structure compared to the seed structure are the change of the bandgap mechanism from a local resonance mechanism to a Bragg scattering mechanism, the change of the included materials from four to three, and the significant change in the location and extent of the pores and other materials, respectively. The obtained nondominated Pareto solution of MOOP can balance the bandgap and the structural mass, which provides the decision-maker trade-off to select the appropriate optimized solution. Compared with the single-objective optimization problem (SOOP), the MOOP not only obtains a nondominated solution close to the result of SOOP, but also obtains other nondominated solutions of MOOP, which proves the effectiveness of the optimization algorithm in this paper. The design method in this paper can be easily extended to select suitable materials from a wider variety of materials for bandgap optimization design of PnCs, which has very promising applications. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of generating an evenly distributed set of Pareto solutions. It appears in real-life applications related to multi-objective optimization when it is important to represent the entire Pareto front with a minimal cost. There exist only a few algorithms which are able to tackle this problem in a general formulation. The Directed Search Domain (DSD) algorithm has proved to be efficient and quite universal. It has successfully been applied to different challengeable test cases. In this paper for the first time the DSD approach is systematically extended and applied to problems with higher dimensions. The modified algorithm does not have any formal limitation on the number of objective functions that is important for practical applications. The efficacy of the algorithm is demonstrated on a number of test cases. [ABSTRACT FROM AUTHOR]

To properly simulate the laser directed energy deposition (L-DED) process, the heat source model is crucial as it represents the absorbed heat energy, which controls the thermal phenomena associated with L-DED. Goldak's double-ellipsoidal heat source model has been broadly used to represent energy distribution in a wide range of laser-based simulation processes. It is therefore important to accurately determine Goldak's model parameters for correct L-DED simulation. In this work, an original inverse identification procedure was proposed to determine Goldak's heat source parameters by coupling a 3D finite element thermal model with a genetic algorithm. The multi-objective Non-dominated Sorting Genetic Algorithm II (NSGA-II) was used by considering two objective functions simultaneously. The temperatures at specific points in the melt pool were extracted from the 3D finite element simulations and then used to construct objective functions for the optimization procedure. By minimizing the differences between the simulated and reference temperatures, Goldak's heat source parameters were identified based on experimental L-DED fabricated single-track deposits for different process conditions. The model accurately estimated the penetration depth and slightly overestimated the heat-affected zone (HAZ). The relative errors are reliable enough to reasonably predict Goldak's heat source model parameters. The inverse identification procedure proposed in this study can help reduce time and experimental costs to estimate heat source parameters for the simulation of the L-DED process. [ABSTRACT FROM AUTHOR]

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

In this paper, the bending, dynamic responses and multi-objective optimization of the pyramidal lattice sandwich plate are studied. Initially, the paper calculates the equivalent elasticity moduli and equivalent shearing moduli of pyramid lattice core. Then, the kinematic relations between the plate and the pyramid grid core are established by using the improved FSDT. The governing differential equations and the conditions for boundaries are deduced from the Hamilton principle, and solved by using Navier’s method. To validate that the theoretical model is accurate, the paper compares the center deflection and natural frequency derived from theoretical calculations with the experimental and finite element results. In detail, this paper studies the influence of the number of single cells, thickness ratio, and lattice rod radius on the bending and free vibration behavior. Then, the key elements are chosen as design parameters. Next, the space of design variables is explored using the hybrid PSO-GWO algorithm to find Pareto optimal solutions. The optimum number of single cells, thickness ratio and radius of the pyramid lattice sandwich plates are finally derived to satisfy the conflicting desired objectives of maximizing natural frequency and minimizing center displacement and mass. The results show that the proposed equivalent model can predict the static bending and dynamic responses of pyramidal lattice sandwich plate well. In order to satisfy maximizing natural frequency and minimizing center displacement and mass, different Pareto optimal solutions are given for different design options by using the hybrid PSO-GWO algorithm. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In modern manufacturing, dynamic allocation of resources is key to enhancing production efficiency and competitiveness. With the advancement of intelligent manufacturing and Industry 4.0, the manufacturing environment has become increasingly complex and variable, making traditional static resource allocation methods insufficient for practical needs. This paper aims to explore multi-objective optimization methods for dynamic manufacturing resource allocation by constructing a multi-objective optimization model and proposing an improved NSGA-II algorithm to address this issue. The study demonstrates that the improved algorithm significantly enhances population diversity and global search capability, effectively coping with dynamic manufacturing environments to provide efficient and reliable resource allocation solutions. This research not only offers new insights into dynamic resource allocation but also serves as a valuable reference for related fields. [ABSTRACT FROM AUTHOR]

The presence of limitations such as processing and energy in many of end devices has resulted in task offloading to other sources. To address these limitations, offloading can be performed in diverse environments such as fog or cloud. This article reviews the multi-objective optimization of task offloading, which is a crucial challenge in fog computing. First, a task offloading model is presented for the simultaneous optimization of response time, energy consumption, and cost criteria, while considering availability criteria. This model utilizes a virtual controller to monitor all three layers of IoT, Fog, and Cloud. In the following, a developed version of the multi-objective crow search algorithm called the Improved Non-Dominated Sorting Crow Search Algorithm (INSCSA) is presented. An advantage of this research, compared to previous works, is the investigation of the optimality of the whole system. Evaluations demonstrate that our method has achieved superior results compared to the other methods. [ABSTRACT FROM AUTHOR]

The free vibration dynamic modellings of sandwich beams with periodic lattice truss cores are established by combining the Bernoulli–Euler beam theory and Timoshenko beam theory, and the fundamental frequency and unit cell mass of lattice sandwich beams are optimized based on the non-dominated sorting genetic algorithm-II (NSGA-II). Firstly, the vibration control equation of lattice sandwich beams is derived using Hamilton's principle. Then, it is compared and verified by modal experiment and finite element numerical simulation. On this basis, the influence of different truss cores on the structure's natural frequency is further analyzed. Finally, taking the sheet thickness, core thickness, and radius of the truss as the optimization design variables and taking the structural fundamental frequency maximization and mass minimization as the optimization objectives, the Pareto front is obtained by NSGA-II, and the optimization results are verified by theoretical and numerically. The research results show that the theoretical method in this article can well predict the natural frequencies of sandwich beams with different truss cores. The optimized sandwich structure has higher fundamental frequency with lighter weight, which effectively improves the anti-vibration ability. [ABSTRACT FROM AUTHOR]

This paper presents a multi-objective optimization approach for developing efficient and environmentally friendly Machine Learning models. The proposed approach uses Genetic Algorithms to simultaneously optimize the accuracy, time-to-solution, and energy consumption simultaneously. This solution proposed to be part of an Automated Machine Learning pipeline and focuses on architecture and hyperparameter search. A customized Genetic Algorithm scheme and operators were developed, and its feasibility was evaluated using the XGBoost ML algorithm for classification and regression tasks. The results demonstrate the effectiveness of the Genetic Algorithm for multi-objective optimization, indicating that it is possible to reduce energy consumption while minimizing predictive performance losses. [ABSTRACT FROM AUTHOR]

Physics-informed neural networks formulation allows the neural network to be trained by both the training data and prior domain knowledge about the physical system that models the data. In particular, it has a loss function for the data and the physics, where the latter is the deviation from a partial differential equation describing the system. Conventionally, both loss functions are combined by a weighted sum, whose weights are usually chosen manually. It is known that balancing between different loss terms can make the training process more efficient. In addition, it is necessary to find the optimal architecture of the neural network in order to find a hypothesis set in which is easier to train the PINN. In our work, we propose a multi-objective optimization approach to find the optimal value for the loss function weighting, as well as the optimal activation function, number of layers, and number of neurons for each layer. We validate our results on the Poisson, Burgers, and advection-diffusion equations and show that we are able to find accurate approximations of the solutions using optimal hyperparameters. [ABSTRACT FROM AUTHOR]

Multi-objective mathematical programming subject to box constraints on system parameters is an important problem in the fields of computer-aided design and decision-making theory, when there is an apparent conflict between multiple preference criteria. In this work, mathematical programming methodology has been proposed using a modern algorithm to solve multi-objective mathematical problems. The proposed algorithm to perform the ranking and classification procedures for the set of alternative solutions with boundary constraints is based on the concepts of parametric multi-aspect modeling of flexible complex systems. Interactive software has been implemented in the .Net environment to aid the decision-maker to identify the preference criteria and constraints on the system parameters, and then to choose the best solution from the morphological set depending on the developed algorithm. In addition, an illustrative numerical example solved by the proposed algorithm is presented at the end of this work. [ABSTRACT FROM AUTHOR]

High polyhydroxyalkanoate (PHA) yield from selected substrates associated with minimal accumulation of residual biomass can improve the process economy. In this study, different carbon (glucose, sucrose, glycerol, and acetic acid) and nitrogen (NH4Cl and urea) sources were screened for PHA production by Cupriavidus necator. The effects of incubation time, nitrogen, and phosphate concentration on biomass growth and PHA production were co-optimized through response surface methodology (RSM) and genetic algorithm-optimized artificial neural network (GA-ANN). Sucrose and urea were found to offer significantly better (p <0.001) biomass (1.468 ± 0.007 g l-1) and PHA (0.924 ± 0.02 g l-1) yield when compared with other carbon and nitrogen sources. Though the performance of both the models remains similar for biomass (R2 = 0.97–0.98), GA-ANN (with six neurones in a hidden layer) seems exceptionally better in predicting PHA yield (R2 = 0.97) when compared to the polynomial model (R2 = 0.92). The maximum PHA concentration of 2.69 g l-1 was predicted by the ANN model at an incubation time of 62.80 h with 2.0 g l-1 of nitrogen and 4.0 g l-1 of phosphate concentration. The multi-composite desirability using the GA-ANN model projected a better polymer-to-biomass ratio compared to the polynomial model. The inclusion of a cost-benefit analysis framework may be warranted before recommending the optimal conditions obtained through multivariate regression and GA-ANN models. [ABSTRACT FROM AUTHOR]

Rectangular microchannel heat sinks (MCHS) are widely used to cool high-heat-flux electronic devices. However, previous studies focused mainly on MCHS with uniform channels (UCs). This study considers a microchannel heat sink with non-uniform channels (NUCs). A mathematical model is developed based on energy equations and the Darcy flow principle. Explicit expressions for total thermal resistance and coolant pressure drop are derived using the thermoelectric analogy. Experiments and numerical simulations are performed to verify the mathematical model. As non-uniformity increases, total coolant pressure drop decreases but at the cost of higher thermal resistance. The overall performance of NUCs is better than that of UCs because of their lower ratio of pumping power to cooling power. Heat transfer performance of NUCs changes little for more than 120 channels and depends mainly on channel arrangement. A multi-objective optimization is conducted to minimize the thermal resistance and pumping power of an NUC. An optimal NUC saves 64% pumping power compared with a conventional UC for the total thermal resistance of 0.1°C/W, indicating that the use of non-uniform channels could be very helpful to reduce the flow resistance of MCHS. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is an attractive paradigm for privacy-aware distributed machine learning, which enables clients to collaboratively learn a global model without sharing clients' data. Recently, many strategies have been proposed to improve the generality of the global model and thus improve FL effect. However, existing strategies either ignore the fairness among clients or sacrifice performance for fairness. They cannot ensure that the gap among clients is as small as possible without sacrificing federated performance. To address this issue, we propose ParetoFed, a new local information aggregation method dedicated to obtaining better federated performance with smaller gap among clients. Specifically, we propose to use multi-objective hyperparameter optimization (HPO) algorithm to gain global models that are both fair and effective. Then, we send Pareto Optimal global models to each client, allowing them to choose the most suitable one as the base to optimize their local model. ParetoFed not only make the global models more fair but also make the selection of local models more personalized, which can further improve the federated performance. Extensive experiments show that ParetoFed outperforms existing FL methods in terms of fairness, and even achieves better federated performance, which demonstrates the significance of our method. [ABSTRACT FROM AUTHOR]

Research in multi-objective particle swarm optimizers (MOPSOs) progresses by proposing one new MOPSO at a time. In spite of the commonalities among different MOPSOs, it is often unclear which algorithmic components are crucial for explaining the performance of a particular MOPSO design. Moreover, it is expected that different designs may perform best on different problem families and identifying a best overall MOPSO is a challenging task. We tackle this challenge here by: (1) proposing AutoMOPSO, a flexible algorithmic template for designing MOPSOs with a design space that can instantiate thousands of potential MOPSOs; and (2) searching for good-performing MOPSO designs given a family of training problems by means of an automatic configuration tool (irace). We apply this automatic design methodology to generate a MOPSO that significantly outperforms two state-of-the-art MOPSOs on four well-known bi-objective problem families. We also identify the key design choices and parameters of the winning MOPSO by means of ablation. AutoMOPSO is publicly available as part of the jMetal framework. [ABSTRACT FROM AUTHOR]

As a new paradigm of distributed computing, mobile edge computing (MEC) has gained increasing attention due to its ability to expand the capabilities of centralized cloud computing. In MEC environments, a software application typically consists of multiple micro‐services, which can be composed together in a flexible manner to achieve various user requests. However, the composition of micro‐services in MEC is still a challenging research issue arising from three aspects. Firstly, composite micro‐services constructed by ignoring the processing capabilities of different micro‐services may cause waste of edge resources. Secondly, edge servers' limitations in terms of computational power can easily cause service occupancy between composite micro‐services, severely affecting the user experience. Thirdly, in dynamic and unstable mobile environments, different edge users have different sensitivities to request latency, which increases the complexity of micro‐service composition. In order to improve edge resource utilization and user experience on micro‐service invocations, in this paper, we comprehensively consider the above three factors, and we first model the micro‐services composition problem in MEC as a constrained multi‐objective optimization problem. Then, a micro‐service composition optimization method M3C combining graph search and branch‐and‐bound strategy is proposed to find a composition solution set with low energy consumption and high success rate for multiple edge users. Finally, we perform a series of experiments on two widely used datasets. Experimental results show that our proposed approach significantly outperforms the four competing baseline approaches, and that it is sufficiently efficient for practical deployment. [ABSTRACT FROM AUTHOR]

Power load data frequently display outliers and an uneven distribution of noise. To tackle this issue, we present a forecasting model based on an improved extreme learning machine (ELM). Specifically, we introduce the novel Pinball-Huber robust loss function as the objective function in training. The loss function enhances the precision by assigning distinct penalties to errors based on their directions. We employ a genetic algorithm, combined with a swift nondominated sorting technique, for multiobjective optimization in the ELM-Pinball-Huber context. This method simultaneously reduces training errors while streamlining model structure. We practically apply the integrated model to forecast power load data in Taixing City, which is situated in the southern part of Jiangsu Province. The empirical findings confirm the method's effectiveness. [ABSTRACT FROM AUTHOR]

The flood control operation of river barrages represents a multi-objective optimization problem with conflicting decision objectives, introducing risks into the decision-making process. Most existing optimization methods for operational rule sets encounter challenges related to the insufficient representation of flood accuracy and prolonged computational duration. Considering these two issues, this study aimed to propose a novel multi-objective barrage optimization operation approach based on a hydrodynamic model and a data-driven evolutionary algorithm. This approach employs a hydrodynamic model to precisely simulate the flood propagation process and provide the required hydraulic characteristics. Utilizing the results provided by the hydrodynamic model as foundational data, a multi-objective particle swarm algorithm was employed to drive the search for Pareto-optimal operational rules. Subsequently, the Kriging model is integrated into the optimization process, wherein only potential nondominated solutions in the offspring population were selected for exact objective function evaluations. This significantly reduced the frequency of calls to the hydrodynamic model, thereby enhancing the efficiency of optimization computations. The proposed approach was applied to a real multi-barrage flood control system for the rivers in the urban city of Chengdu, China. The results indicate that this method can optimize and solve the multi-objective operational rules for barrage flood control with limited computational resources. The obtained Pareto-optimal operational rules also illustrate the trade-off relationships among multiple objectives, suggesting that it is possible to mitigate downstream flood risks at the cost of increasing upstream flood risks, and vice versa. The new method can provide precise guidance for flood control scheduling of barrages during the flood season, enabling decision makers to choose the operation rules according to their own risk preferences. [ABSTRACT FROM AUTHOR]

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Regression testing is a type of software testing that is performed during software maintenance in order to validate any additional modifications made to the functionality of the software. The objective of test case prioritization in regression is to establish a ranking for the test cases, giving priority to those that effectively cover a substantial portion of code or faults, while also minimizing the required execution time. Managing the prioritization of test cases in accordance with the tester's requirements can be a challenging task, given the significant number of test cases generated during development and maintenance. Different algorithms, including Greedy approaches, meta-heuristic techniques, and other optimization methods, are utilized to prioritize test cases, taking into account the constraints specified by the software tester. This paper employs a ranking-based NSGA-2 algorithm for the purpose of test case ordering and prioritization. The focus is on test cases that are responsive to faults, specifically those that result from modifications or the introduction of new software functionality. The proposed methodology entails the prioritization of test cases by leveraging their historical data. In addition to the Sensitive Index, other primary objectives of test prioritization include considering the execution cost and APFD (average percentage of faults detected). The proposed model has undergone testing on a total of eight applications, consisting of five handcrafted and three benchmark Java-based applications. Additionally, it has been subjected to comparison against several state-of-the-art algorithms commonly employed in the field of test case prioritization. [ABSTRACT FROM AUTHOR]

Mining counterfactual antecedents became a valuable tool to discover knowledge and explain machine learning models. It consists of generating synthetic samples from an original sample to achieve the desired outcome in a machine learning model, thus helping to understand the prediction. An insightful methodology would explore a broader set of counterfactual antecedents to reveal multiple possibilities while operating on any classifier. Therefore, we create a tree-based search that requires monotonicity from the objective functions (a.k.a. cost functions); it allows pruning branches that will not improve the objective functions. Since monotonicity is only required for the objective function, this method can be used for any family of classifiers (e.g., linear models, neural networks, and decision trees). However, additional classifier properties speed up the tree search when it foresees branches that will not result in feasible actions. Moreover, the proposed optimization generates a diverse set of Pareto-optimal counterfactual antecedents by relying on multi-objective concepts. The results show an algorithm with working guarantees that enumerates a wide range of counterfactual antecedents. It helps the decision-maker understand the machine learning decision and finds alternatives to achieve the desired outcome. The user can inspect these multiple counterfactual antecedents to find the most suitable one and better understand the prediction. [ABSTRACT FROM AUTHOR]

Recreational sports are essential for boosting physical health and improving quality of life. The goal of this research is to optimize the planning of leisure sports by presenting a new method based on a multi-objective genetic algorithm. Acknowledging the intricacy of organizing recreational sports events, we suggest an approach that concurrently maximizes several goals, such as the use of resources, player happiness, and ecological impact. The topic is first formulated as a multi-objective optimization task in the paper, and a genetic algorithm is used to handle the objectives' inherent conflict. With its ability to effectively explore the solution space, the genetic algorithm produces a set of Pareto-optimal solutions that show trade-offs for the conflicting goals. The integration of several factors, including time preferences, geographical limitations, and environmental sustainability, guarantees a thorough and equitable strategy for leisure sports scheduling. In the area of leisure sports planning, the use of a multi-objective genetic algorithm offers a reliable solution that can be tailored to meet various circumstances and goals. As the need of encouraging healthy lifestyles becomes more widely acknowledged, this research offers a useful tool for maximizing the organization and performance of recreational sports activities, enhancing the overall wellbeing of people and communities. [ABSTRACT FROM AUTHOR]

A significant amount of municipal solid waste (MSW) generation and ineffective management can cause adverse impacts on the environment (air and water pollution) and human health. Therefore, a sustainable approach for handling MSW is required. This study aims to develop a tool to select the most promising MSW management system using the superstructure optimization technique to ensure that the selected system can achieve minimization of the total annual cost and environmental impacts. This can lead to a multi-objective superstructure optimization problem solved by using the augmented ϵ -constraint method. The developed model is based on the concept of material balance and all potential technologies include anaerobic digestion, pyrolysis, gasification, incineration, sanitary landfill, material recovery facility and composting. The proposed methodology is applied to synthesize the MSW management system in Warin Chamrap district to determine its applicability. In this research, the composition of MSW includes organics, paper, plastic, glass, and others. The results show that the Pareto-optimal solutions optimizing both total annual cost and environmental impacts can be developed. The MSW management system with the lower total annual cost tends to have the higher environmental impacts and the trade-offs between each objective can be identified so the decision-makers are able to consider before making their decision. Further investigation on sensitivity analysis reveals that the selling prices of products, annual capital cost, and operating cost factors have different degree of impacts on the waste management strategies and total annual cost. [ABSTRACT FROM AUTHOR]

This work presents a methodology integrating Non-Linear Programming (NLP) for multi-objective and multi-period optimization, addressing sustainable waste management and energy conversion challenges. It integrates waste-to-energy (WtE) technologies such as Anaerobic Digestion (AD), Incineration (Inc), Gasification (Gsf), and Pyrolysis (Py), and considers thermochemical, technical, economic, and environmental considerations through rigorous non-linear functions. Using Mexico City as a case study, the model develops waste management strategies that balance environmental and economic aims, considering social impacts. A trade-off solution is proposed to address the conflict between objectives. The economical optimal solution generates 1.79M$ with 954 tons of CO2 emissions while the environmental one generates 0.91M$ and reduces emissions by 54%, where 40% is due to gasification technology. Moreover, the environmentally optimal solution, with incineration and gasification generates 9500 MWh/day and 5960 MWh/day, respectively, demonstrates the capacity of the model to support sustainable energy strategies. Finally, this work presents an adaptable framework for sustainable waste management decision-making. [ABSTRACT FROM AUTHOR]

Over the last decade, automatic chemical design frameworks for discovering molecules with drug-like properties have significantly progressed. Among them, the variational autoencoder (VAE) is a cutting-edge approach that models the tractable latent space of the molecular space. In particular, the usage of a VAE along with a property estimator has attracted considerable interest because it enables gradient-based optimization of a given molecule. However, although successful results have been achieved experimentally, the theoretical background and prerequisites for the correct operation of this method have not yet been clarified. In view of the above, we theoretically analyze and rigorously reconstruct the entire framework. From the perspective of parameterized distribution and the information theory, we first describe how the previous model overcomes the limitations of the beta VAE in discovering molecules with the desired properties. Furthermore, we describe the prerequisites for training the above model. Next, from the log-likelihood perspective of each term, we reformulate the objectives for exploring latent space to generate drug-like molecules. The distributional constraints are defined in this study, which will break away from the invalid molecular search. We demonstrated that our model could discover a novel chemical compound for targeting BCL-2 family proteins in de novo approach. Through the theoretical analysis and practical implementation, the importance of the aforementioned prerequisites and constraints to operate the model was verified. [ABSTRACT FROM AUTHOR]

A numerical optimization technique used to address nonlinear programming problems is the trust region (TR) method. TR uses a quadratic model, which may represent the function adequately, to create a neighbourhood around the current best solution as a trust region in each step, rather than searching for the original function's objective solution. This allows the method to determine the next local optimum. The TR technique has been utilized by numerous researchers to tackle multi‐objective optimization problems (MOOPs). But there is not any publication that discusses the issue of applying a chaotic search (CS) with the TR algorithm for solving multi‐objective (MO) problems. From this motivation, the main contribution of this study is to introduce trust‐region (TR) technique based on chaotic search (CS) for solving MOOPs. First, the reference point interactive approach is used to convert MOOP to a single objective optimization problem (SOOP). The search space is then randomly initialized with a set of initial points. Second, in order to supply locations on the Pareto boundary, the TR method solves the SOOP. Finally, all points on the Pareto frontier are obtained using CS. A range of MO benchmark problems have demonstrated the efficiency of the proposed algorithm (TR based CS) in generating Pareto optimum sets for MOOPs. Furthermore, a demonstration of the suggested algorithm's ability to resolve real‐world applications is provided through a practical implementation of the algorithm to improve an abrasive water‐jet machining process (AWJM). [ABSTRACT FROM AUTHOR]

This study presents a novel variable thickness nested tube consisting of circular tube and origami tube (VTCO) designed to reduce the initial peak crushing force (PCF) under axial loading while maintaining high energy absorption. The core of this design is to achieve progressive and controlled energy absorption and to reduce peak forces as well as accelerations that can lead to passenger injuries. Nested tube structures were fabricated using short carbon fiber‐reinforced nylon composites through 3D printing technology, and quasi‐static axial compression tests were performed. The results show that equal‐thickness nested tubes consisting of circular tubes and origami tubes (ETCO) perform better in terms of energy absorption and deformation stability compared to circular and origami tubes individually. The further improved VTCO reduced the PCF by 53% and significantly improved the crash force efficiency (CFE) by 54%. The VTCO structure was parametrically investigated by means of a validated finite element model and optimized using the Non‐Symmetric Dominance Sorting Genetic Algorithm II (NSGA‐II). The optimization results provide a feasible way to adjust the structural crashworthiness to meet different engineering requirements. This study provides valuable insights into the energy‐absorbing properties of VTCO structures and is an important guide for the application and material development of novel VTCO structures. Highlights: ETCO offers better energy absorption and stability than circular and origami tubes individually.VTCO is superior to ETCO in reducing PCF and increasing CFE.Multi‐objective optimization using NSGA‐II to balance conflicting crashworthiness indicators. [ABSTRACT FROM AUTHOR]

This paper presents a new version of Multi Agent Collaborative Search (MACS) with Adaptive Weights (named MACS‐AW). MACS is a multi‐agent memetic scheme for multi‐objective optimization originally developed to mix local and population‐based search. MACS was proven to perform well on a number of test cases but had three limitations: (i) the amount of computational resources allocated to each agent was not proportional to the difficulty of the sub‐problem the agent had to solve; (ii) the population‐based search (called social actions in the following) was using only one differential evolution (DE) operator with fixed parameters; (iii) the descent directions were not adapted during convergence, leading to a loss of diversity. In this paper, we propose an improved version of MACS, that implements: (i) a new utility function to better manage computational resources; (ii) new social actions with multiple adaptive DE operators; (iii) an automatic adaptation of the descent directions with an innovative trigger to initiate adaptation. First, MACS‐AW is compared against some state‐of‐art algorithms and its predecessor MACS2.1 on some standard benchmarks. Then, MACS‐AW is applied to the solution of two real‐life optimization problems and compared against MACS2.1. It will be shown that MACS‐AW produces competitive results on most test cases analysed in this paper. On the standard benchmark test set, MACS‐AW outperforms all other algorithms in 11 out of 30 cases and comes second in other 8 cases. On the two real engineering test set, MACS‐AW and its predecessor obtain same results. [ABSTRACT FROM AUTHOR]

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

Most commercial software used to simulate the injection molding process of semi-crystalline thermoplastic polymers do not explicitly take into account the polymer crystallization, which could lead to errors in the estimations of filling as well as warpage and shrinkage. This is mainly due to the common complexity of the models used to describe crystallization and the challenging respective model parameter identification under injection molding conditions. To close this gap, in this work, we use a simple thermo-mechanical crystallization model to describe the flow-induced and quiescent crystallization of an unreinforced semi-crystalline thermoplastic material during injection molding. The crystallization model is implemented in the commercial software Autodesk® Moldflow® Insight 2021 using the Solver API feature alongside crystallization-dependent viscosity, PVT, and solidification models. The model parameters were identified using a calibration workflow that employs surrogate models representing the simulated pressure results to perform a multi-objective optimization. The filling predictions as well as the calculated pressure fields are presented using the calibrated model parameters in comparison to those measured during the actual injection molding of a polyoxymethylene (POM) part using different process conditions. The results show major improvements in the estimations of the time-depending pressure field as well as the level of filling of the produced parts. [ABSTRACT FROM AUTHOR]

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]

Multi-objective optimization and CFD simulation are conducted to optimize the design of a multi-borehole ground heat exchanger (GHE) system and assess its long-time performance. The multi-objective optimization is performed to minimize the entropy generation number (EGN) and total cost rate by using various evolutionary algorithms, including NSGA-II, GDE-3, MOEA/D, PESA-II, SPEA-II, and SMPSO. NSGA-II and GDE-3 algorithms perform best in obtaining Pareto optimal solutions. Three prominent points on the NSGA-II Pareto frontier, representing the results of single-objective thermodynamic, single-objective economic, and multi-objective optimizations, are simulated in three dimensions over three months. The trends of EGN variations extracted from the transient CFD simulation agree well with those from the steady analytical model. The EGN obtained from multi-objective optimization is 58.8% lower than the EGN obtained using single-objective economic optimization and 1.9 times higher than that calculated from single-objective thermodynamic optimization. Likewise, the total cost rate obtained from multi-objective optimization is 64.4% lower than the value obtained from single-objective thermodynamic optimization and four times higher than that calculated using single-objective economic optimization. The proposed optimization approach can be reliably applied to improve the design of multi-borehole GHE systems. [ABSTRACT FROM AUTHOR]

AbstractPolygon characteristics, including area, shape, and topology, may be lost during rasterization, leading to inaccurate analyses. Maintaining multiple characteristics remains a challenging multi-objective optimization problem. This study introduces improved strategies for flexibly maintaining multiple characteristics (ISMMC). Errors related to area (A), shape (S), and topology (T) are addressed using six prioritized sequences: AST, ATS, TAS, TSA, STA, and SAT, where the order of the letters denotes the sequence of error correction. The importance (I) of land-use types (LUT) is also considered when developing a strategy based on the TSA (ITSA) that maintains characteristics for important LUTs. The ISMMC achieved a total improved accuracy (IA) of ≥0.05 for 98.67% of 150 tested cases. The STA, SAT, TAS, and TSA yielded the highest area, shape, topological, and total accuracies. The STA achieved a summed AI increase ≥20.36% compared to the existing version. The runtime was ≥85.46 times the scanline runtime, and the threshold cell size (≤50 m) balanced the IA and runtime. The ITSA improved accuracy by 83.33% and 6.67% of solutions when preferentially maintaining one or two important LUTs, outperforming the existing STA. Overall, these developments improve accuracy with flexibility in rasterization results and subsequent raster applications. [ABSTRACT FROM AUTHOR]

The design and optimization of sensor array configurations is a significant challenge for distributed SAR-GMTI radar systems because the system performance of distributed array radar is a comprehensive result of several conflicting evaluation indicators. This paper developed a multi-objective intelligent optimization method to solve the global optimal problem of array configurations in terms of achieving optimal GMTI performance. Firstly, to formulate the relationship between array configuration and GMTI performance, we established three objective functions derived from evaluating indicators of SAR-GMTI performance. Specifically, in the objective functions, we proposed a novel clutter covariance matrix model that added several typical non-ideal factors of the real-world detection environment. This provides a way to build a bridge between the array configuration, environment clutter, and GMTI performance. Then, we proposed an improved multi-objective snake optimization algorithm (IMOSOA) that combined the Pareto optimization mechanism with snake optimization to solve the multi-objective optimization problem while reconciling the conflicts between different objective functions. Meanwhile, some significant improvements were made to speed up convergence. That is, tent chaotic mapping-based initialization, multi-group coevolution, and individual mutation strategies were applied to solve the non-convergence problem of global searching. Finally, in the case of an airborne SAR-GMTI system, numerical experiments demonstrated that the proposed IMOSOA has superior performance than other contrast methods, especially in terms of GMTI applications. [ABSTRACT FROM AUTHOR]

The rapid development of new energy vehicles demands significant improvements in connector structures and performance standards. Wire harness connectors, crucial for linking various electrical components, face challenges due to their small size and thin-walled structure, which can lead to dimensional shrinkage and warping during injection molding. To address these issues, this study optimizes the injection molding process by fine-tuning parameters such as melt temperature, mold temperature, injection time, holding pressure/time, and cooling time. By integrating the Taguchi method with grey relational analysis, the study enhances the molding process for thin-walled composite connectors. This combined approach provides a comprehensive framework for optimizing multiple quality objectives and improving the overall performance of injection-molded composite components. [ABSTRACT FROM AUTHOR]

Longitudinal corrugated tubes (LCTs) exhibit stable platform force under axial compression but have low specific energy absorption. Conversely, circumferential corrugated tubes (CCTs) offer higher specific energy absorption but with unstable platform force. To overcome these limitations, this paper introduces a novel bi-directional corrugated tube (BCT) that amalgamates the strengths of both the CCT and LCT while mitigating their weaknesses. The BCT is formed by rolling a bi-directional corrugated structure into a circular tubular form. Numerical simulations of the BCT closely align with experimental results. The study further examines the influence of discrete parameters on the BCT's performance through simulations and identifies the tube's optimal design using the integral entropy TOPSIS method. A full factorial experimental approach is then employed to investigate the impact of radial amplitude, axial amplitude, and neutral surface diameter on the crushing behavior of the BCT, comparing it with the CCT and LCT. The results reveal that increasing A i enhances the axial resistance of the structure, while increasing A j reduces the buckling effect, resulting in a higher specific energy absorption and lower ultimate load capacity for the BCT compared to the CCT and LCT. A simultaneous multi-objective optimization of the CCT, LCT, and BCT confirms that the BCT offers superior specific energy absorption and ultimate load capacity. The optimal configuration parameters for the BCT have been determined, providing significant insights for practical applications in crashworthiness engineering. [ABSTRACT FROM AUTHOR]

When aimed at minimizing both the classification error and the number of selected features, feature selection can be treated as a bi-objective optimization problem suitable for solving with multi-objective evolutionary algorithms (MOEAs). However, traditional MOEAs may encounter difficulties due to discrete optimization environments and the curse of dimensionality in the feature space, especially for high-dimensional datasets. Therefore, in this paper an interpolation-based evolutionary algorithm (termed IPEA) is proposed for tackling bi-objective feature selection in classification, where an interpolation based initialization method is designed for covering a wide range of search space and exploring the adaptively detected regions of interest. In experiments, IPEA is been compared with four state-of-the-art MOEAs in terms of two widely-used performance metrics on a list of 20 public real-world classification datasets with the dimensionality ranging from low to high. The overall empirical results suggest that IPEA generally performs the best of all tested algorithms, with significantly better search abilities and much lower computational time cost. [ABSTRACT FROM AUTHOR]