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This paper studies the problem of scheduling \begin{document}$ n $\end{document} jobs with equal processing times on \begin{document}$ m $\end{document} uniform machines to optimize two criteria simultaneously. The main contribution is an \begin{document}$ O(n\log m+n^3) $\end{document}-time algorithm for two general min-max criteria, improving the previous \begin{document}$ O(n\log m+n^4) $\end{document} time complexity. For a particular min-sum criterion (total weighted completion time or total tardiness) in combination with a general min-max criterion, \begin{document}$ O(n\log m+n^3) $\end{document}-time algorithms are also obtained, improving the previous \begin{document}$ O(n\log m+n^3\log n) $\end{document} time complexity. The algorithms can produce all Pareto optimal points together with the corresponding schedules. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of scheduling n equal-processing-time jobs with release dates non-preemptively on identical machines to optimize two criteria simultaneously or hierarchically. For simultaneous optimization of total completion time (and makespan) and maximum cost, an algorithm is presented which can produce all Pareto-optimal points together with the corresponding schedules. The algorithm is then adapted to solve the hierarchical optimization of two min-max criteria, and the final schedule has a minimum total completion time and minimum makespan among the hierarchical optimal schedules. The two algorithms provided in this paper run in O (n 3) time. This paper addresses the problem of scheduling equal-processing-time jobs with release dates non-preemptively on identical machines to optimize two criteria simultaneously or hierarchically. For simultaneous optimization of total completion time (and makespan) and maximum cost, an algorithm is presented which can produce all Pareto-optimal points together with the corresponding schedules. The algorithm is then adapted to solve the hierarchical optimization of two min-max criteria, and the final schedule has a minimum total completion time and minimum makespan among the hierarchical optimal schedules. The two algorithms provided in this paper run in time. [ABSTRACT FROM AUTHOR]

This paper studies the problem of scheduling $ n $ jobs on a single machine to minimize total completion time and maximum cost, simultaneously. Each job is associated with a positional deadline that indicates the largest ordinal number of this job in any feasible schedule. The jobs have agreeable release and processing times, meaning that jobs with larger release times also have larger processing times. The agreeability assumption is reasonable since both the single-criterion problems (without positional deadline constraints) of minimizing total completion time and maximum lateness on a single machine with arbitrary release and processing times are strongly NP-hard. An $ O(n^3) $-time Pareto optimal algorithm is presented. The previously known algorithms only solve two special cases of the agreeability assumption: either the case of equal release times in $ O(n^4) $ time, or the case of equal processing times (without positional deadline constraints) in $ O(n^3) $ time.

Chaotic map is a kind of discrete chaotic system. The existing chaotic maps suffer from optimal parameters in terms of chaos measurements. In this study, a novel approach of optimization of parametric chaotic map (PCM) using triple objective optimization is presented for the first time. A PCM with six parameters is first conceived and then optimized using Pareto-based triple objective artificial bee colony (PT-ABC) algorithm. Pareto optimality is employed to catch the trade-off among the objectives: Lyapunov exponent (LE), sample entropy (SE), and Kolmogorov entropy (KE). A global optimal design including the six parameters is selected for minimizing the reciprocal of the three objectives independently. The chaotic performance of PCM is verified through an evaluation with bifurcation diagram, attractor, LE, SE, KE, and correlation dimension. The results are also validated by comparison with those of which reported elsewhere. Furthermore, the applicability of PCM is examined over image encryption and the results are compared with existing chaos-based IEs. Therefore, the PCM manifests the best ergodicity and complexity thanks to its PT-ABC algorithm. [ABSTRACT FROM AUTHOR]

With the rapid development of renewable energy, traditional power generation such as combined cycle unit needs to startup or change load quickly to balance the variability brought by renewable resources. In the combined cycle unit, the startup of steam turbine has a great influence on the whole set, and it is necessary to ensure safety, shorten the startup time, and reduce pollutant emissions. These optimization objectives are inter-related and have complex contradiction, which is the difficulty for multi-objective optimization. Costs function consisting of startup costs, fatigue life damage costs, pollutant emissions costs, and revenue gained from electricity is proposed to balance the contradiction more objectively. Finite exhaustive method (FEM) combining thermal-structural finite element calculation and multi-objective optimization is proposed for multi-objective startup process optimization, and its results are compared with those of minimum startup time and minimum fatigue life damage optimization. It can automatically search for a unique global optimal solution for engineering practice, rather than solution sets obtained by Pareto Optimality, which is beneficial for application in different combined cycle steam turbines and startup process. Multi-objective optimization scheduling shortens startup time from 105 to 93 min, reduces maximum stress from 493 MPa to 440 MPa, and reduces costs function by 66.6%. The comparison with the multi-objective optimization results of the response surface method (RSM) proves the reliability and validity of this method. The practical inspection results prove that the optimal scheduling is safe and effective. [ABSTRACT FROM AUTHOR]

Lung cancer has is highly prevalent worldwide and is the leading cause of cancer-related deaths. In the clinic, a biopsy sample of the lesion is taken to determine whether a lung mass is benign or malignant. CT-guided percutaneous lung biopsy is a minimally invasive intervention and is commonly used to diagnose lung cancer. Path planning before surgery plays a crucial role in percutaneous lung biopsy. Traditionally, path planning for lung biopsy is performed manually by physicians based on CT images of the patient, which demands knowledge and extensive clinical experience of the operating physicians. In this work, a computer-assisted path planning system for percutaneous lung biopsy is proposed based on clinical objectives. Five constraints are presented to remove unqualified skin entry points and determine a feasible entry region based on clinical criteria. Inspired by the Pareto principle and the concept of geometric weighting, the loose-Pareto and adaptive heptagonal optimization (LPHO) method is introduced to plan the optimal puncture path. CT images of 29 patients were collected from Zigong First People's Hospital. Retrospective experiments and test experiments were conducted to evaluate the effectiveness of the algorithm. The planning paths obtained using the proposed method were clinically feasible for 89.7% of patients, demonstrating the applicability and robustness of the system in surgical path planning for lung biopsy. [ABSTRACT FROM AUTHOR]

The paper presents an evaluation of a Pareto-optimized FaceNet model with data preprocessing techniques to improve the accuracy of face recognition in the era of mask-wearing. The COVID-19 pandemic has led to an increase in mask-wearing, which poses a challenge for face recognition systems. The proposed model uses Pareto optimization to balance accuracy and computation time, and data preprocessing techniques to address the issue of masked faces. The evaluation results demonstrate that the model achieves high accuracy on both masked and unmasked faces, outperforming existing models in the literature. The findings of this study have implications for improving the performance of face recognition systems in real-world scenarios where mask-wearing is prevalent. The results of this study show that the Pareto optimization allowed improving the overall accuracy over the 94% achieved by the original FaceNet variant, which also performed similarly to the ArcFace model during testing. Furthermore, a Pareto-optimized model no longer has a limitation of the model size and is much smaller and more efficient version than the original FaceNet and derivatives, helping to reduce its inference time and making it more practical for use in real-life applications. [ABSTRACT FROM AUTHOR]

Previous models of growers' decision‐making during epidemics have unrealistically limited disease management choices to just two options. Here, we expand previous game‐theoretic models of grower decision‐making to include three control options: a crop that is tolerant, resistant or susceptible to disease. Using tomato yellow leaf curl virus (TYLCV) as a case study, we investigate how growers can be incentivized to use different control options to achieve socially optimal outcomes. To do this, we consider the efforts of a 'social planner' who moderates the price of crops. We find that subsidizing a tolerant crop costs the social planner more in subsidies, as its use encourages selfishness and widespread adoption. Subsidizing a resistant crop, however, provides widespread benefits by reducing the prevalence of disease across the community of growers, including those that do not control, reducing the number of subsidies required from the social planner. We then use Gini coefficients to measure equitability of each subsidization scheme. This study highlights how grower behaviour can be altered using crop subsidies to promote socially optimal outcomes during epidemics. [ABSTRACT FROM AUTHOR]

Optimization of online ride-sharing pick-up points effectively reduces driver pick-up and passenger walking distances, enhances pick-up efficiency, alleviates passenger aggregation and node congestion, and improves passenger travel experience. However, the results of the pick-up point planning are influenced by various factors, and it is challenging to address these factors comprehensively to ensure the feasibility of the planning scheme. In response to these challenges, a multi-objective integer linear planning model is proposed, covering passenger and driver benefit as well as road condition. Moreover, a relaxed segmentation iterative algorithm and a Pareto front generation method based on dynamic constraints are developed. Firstly, the spatial and temporal trajectory data is analyzed and filtered to obtain potential pick-up points that are accessible on the road network. Secondly, the relaxation problem is partitioned into subproblems by the branch-and-bound method, which is solved by the simplex method to convert the objective function to its standard form. Thirdly, the single-objective models are solved separately to obtain the boundary values, which are dynamically adjusted as constraints to generate Pareto surfaces. Finally, the correlation among the objectives is analyzed through a large number of experiments to derive the equilibrium point, so as to effectively recommend pick-up points from different perspectives. [ABSTRACT FROM AUTHOR]

This paper studies the problem of scheduling n jobs on a single machine to minimize total completion time and maximum cost, simultaneously. Each job is associated with a positional deadline that indicates the largest ordinal number of this job in any feasible schedule. The jobs have agreeable release and processing times, meaning that jobs with larger release times also have larger processing times. The agreeability assumption is reasonable since both the single-criterion problems (without positional deadline constraints) of minimizing total completion time and maximum lateness on a single machine with arbitrary release and processing times are strongly NP-hard. An O (n 3) -time Pareto optimal algorithm is presented. The previously known algorithms only solve two special cases of the agreeability assumption: either the case of equal release times in O (n 4) time, or the case of equal processing times (without positional deadline constraints) in O (n 3) time. [ABSTRACT FROM AUTHOR]

One of the most difficult components of stroke therapy is regaining hand mobility. This research describes a preliminary approach to robot-assisted hand motion therapy. Our objectives were twofold: First, we used machine learning approaches to determine and describe hand motion patterns in healthy people. Surface electrodes were used to collect electromyographic (EMG) data from the forearm's flexion and extension muscles. The time and frequency characteristics were used as parameters in machine learning algorithms to recognize seven hand gestures and track rehabilitation progress. Eight EMG sensors were used to capture each contraction of the arm muscles during one of the seven actions. Feature selection was performed using the Pareto front. Our system was able to reconstruct the kinematics of hand/finger movement and simulate the behaviour of every motion pattern. Analysis has revealed that gesture categories substantially overlap in the feature space. The correlation of the computed joint trajectories based on EMG and the monitored hand movement was 0.96 on average. Moreover, statistical research conducted on various machine learning setups revealed a 92% accuracy in measuring the precision of finger motion patterns. [ABSTRACT FROM AUTHOR]

Multiparametric indices offer a more comprehensive approach to voice quality assessment by taking into account multiple acoustic parameters. Artificial intelligence technology can be utilized in healthcare to evaluate data and optimize decision-making processes. Mobile devices provide new opportunities for remote speech monitoring, allowing the use of basic mobile devices as screening tools for the early identification and treatment of voice disorders. However, it is necessary to demonstrate equivalence between mobile device signals and gold standard microphone preamplifiers. Despite the increased use and availability of technology, there is still a lack of understanding of the impact of physiological, speech/language, and cultural factors on voice assessment. Challenges to research include accounting for organic speech-related covariables, such as differences in conversing voice sound pressure level (SPL) and fundamental frequency (f0), recognizing the link between sensory and experimental acoustic outcomes, and obtaining a large dataset to understand regular variation between and within voice-disordered individuals. Our study investigated the use of cellphones to estimate the Acoustic Voice Quality Index (AVQI) in a typical clinical setting using a Pareto-optimized approach in the signal processing path. We found that there was a strong correlation between AVQI results obtained from different smartphones and a studio microphone, with no significant differences in mean AVQI scores between different smartphones. The diagnostic accuracy of different smartphones was comparable to that of a professional microphone, with optimal AVQI cut-off values that can effectively distinguish between normal and pathological voice for each smartphone used in the study. All devices met the proposed 0.8 AUC threshold and demonstrated an acceptable Youden index value. [ABSTRACT FROM AUTHOR]

With the development of ecological economics, energy-saving green energy chain management has been a wide concern of academia and industries. However, the relatively high cost of green investment makes manufacturers face the problem of financial constraints. On this basis, because the green level information of products is proprietary to manufacturers, manufacturers will lie about the green level of products in order to improve their profits out of the principle of profit maximization. As a result, banks cannot obtain the true green level of products, reducing the benefits of the green energy-efficient supply chain system and making the market of green products volatile. In view of this, blockchain technology is introduced in this paper to improve customer's product green level sensitivity and obtain lower green credit interest rates from banks. In this paper, a green supply chain financing model based on blockchain technology was constructed under the condition of green information misreporting, and it is compared with the benchmark without blockchain technology. Research shows that the adoption of blockchain can achieve Pareto improvement of green supply chain members. In addition, manufacturers have an incentive to adopt blockchain if the cost of blockchain investment falls below a certain threshold, and consumer green sensitivity increases below that threshold. We compared the profits of green manufacturers with those of retailers and the total emissions of manufacturers. The results show that: (1) When the financing intensity exceeds a certain value, there is an optimal coverage of green financing to ensure that the profit target of manufacturers, the profit target of retailers and the emission reduction target are achieved simultaneously. (2) The adoption of blockchain can achieve Pareto improvement of green energy supply chain members. The actual data of green transformation of Jinyuan New Technology Company were cited. Through calculation, it was found that green transformation can reduce the emissions of enterprises. When the financing intensity is in a certain range, the profits of manufacturers and retailers can be maximized, and the emission reduction degree is the highest. Thus, the practicability and reliability of this model were proved. (3) Manufacturers have an incentive to adopt blockchain if the cost of blockchain investment falls below a certain threshold, and consumer green sensitivity increases below that threshold. The research results of this paper provide solutions for enterprises with limited funds for green transformation and provide a theoretical basis for the government to formulate emission reduction incentive mechanism. [ABSTRACT FROM AUTHOR]

Improving motion accuracy with energy saving in industrial machines is essential to reduce production costs and meet the demand for precise products. Industrial motion planning is one of the effective methods used to save energy and enhance the production efficiency. This study proposes optimal motion planning by simultaneous path and velocity optimization to achieve the trade-off between time and energy consumption. The multi-objective optimization model for minimizing time and energy consumption is solved by the non-dominated sorting genetic algorithm II. Harmonic motion is introduced to the jerk-limited acceleration profile to attain smooth changes in the jerk profile and increase motion accuracy. The proposed method can be used for machine operations such as milling, laser cutting, inspection, gluing, and polishing. To validate the effectiveness of the proposed approach, simulation and experiments are carried out using a two-axis feed drive system, and the motion accuracy is compared to that of jerk-limited acceleration profile. Experimental results reveal that the best trade-off trajectory of the proposed approach achieves respectively 13.9% and 3.5% of time reduction and energy saving. The mean tracking error is reduced by 16.2% and 14.9% for the x and y axes, respectively, compared to the jerk-limited acceleration profile. [ABSTRACT FROM AUTHOR]

Due to its ability to generate complex and intricate shape features on different hard-to-cut materials with minimum dimensional deviation and higher surface finish, wire electrical discharge machining (WEDM) process has already become well-acknowledged in various modern-day manufacturing industries. For optimizing the machining performance of this process, applications of numerous metaheuristic algorithms and multi-criteria decision making (MCDM) techniques are already available in the existent literature. In this paper, six recently developed but yet to be popular nature-inspired metaheuristic algorithms, i.e. multi-objective ant lion optimization (MOALO) algorithm, multi-objective dragonfly algorithm (MODA), multi-objective grasshopper optimization algorithm (MOGOA), multi-objective grey wolf optimizer (MOGWO), non-dominated sorting moth flame optimization (NSMFO) algorithm and non-dominated sorting whale optimization algorithm (NSWOA) are employed for Pareto optimization of a WEDM process. From the developed Pareto optimal fronts, the optimal parametric combinations of the said process are extracted with the help of a post priori MCDM tool called combined compromise solution (CoCoSo) method. It is revealed that for all the considered nature-inspired metaheuristics, the optimal parametric intermixes vary with respect to the weights assigned to the responses. However, MOGWO, MOGOA and MODA are able to identify the best solutions 47%, 28% and 20% of the cases respectively. [ABSTRACT FROM AUTHOR]

In this study, the inductor optimization design is performed by applying the Pareto optimization technique. As environmental problems emerge, the electric vehicle market is expanding, and accordingly, volume reduction and high efficiency of the onboard charger (OBC) are required. An OBC consists of a PFC stage and a DC/DC stage. The inductor is a major component in a converter and affects the volume and efficiency of the entire converter system. However, reducing the volume of the inductor leads to an increase in loss due to an increase in the change in flux density. Therefore, it is important to derive a suitable design for the target between the two parameters in the trade-off of loss and volume. This paper introduces the optimal design algorithm for boosting inductors of PFC converters in terms of volume and loss. Volume and loss are difficult to compare with each other, making it difficult to set weights. Therefore, Pareto optimization was applied which can be selected according to the needs and purposes of the decision-maker, without weighting as an optimization method. Through a series of procedures of applying Pareto optimization to the inductor design, several optimal inductor designs can be derived. At this time, the optimal designs become a set of designs in which the loss does not decrease without an increase in volume, or the volume does not decrease without an increase in loss. A designer can select a design with an appropriate volume and loss that meets the purpose of the design or preference. Therefore, through the proposed method, the inductor can be flexibly designed according to the target of the application. The proposed algorithm is applied to the interleaved totem-pole bridgeless boost PFC converter, to review its effectiveness. As a result, several inductor designs are derived in the search space, and various optimal designs are visualized through the Pareto Frontier. This facilitates comparative analysis of various inductor designs and helps designers select reasonable inductors. The validity was verified by selecting one of the obtained optimal inductor designs and driving the experiment with the resulting inductor.

Computed tomography (CT)-guided thermal ablation is an emerging treatment method for lung tumors. Ablation needle path planning in preoperative diagnosis is of critical importance. In this work, we proposed an automatic needle path-planning method for thermal lung tumor ablation. First, based on the improved cube mapping algorithm, binary classification was performed on the surface of the bounding box of the patient’s CT image to obtain a feasible puncture area that satisfied all hard constraints. Then, for different clinical soft constraint conditions, corresponding grayscale constraint maps were generated, respectively, and the multi-objective optimization problem was solved by combining Pareto optimization and weighted product algorithms. Finally, several optimal puncture paths were planned within the feasible puncture area obtained for the clinicians to choose. The proposed method was evaluated with 18 tumors of varying sizes (482.79 mm3 to 9313.81 mm3) and the automatically planned paths were compared and evaluated with manually planned puncture paths by two clinicians. The results showed that over 82% of the paths (74 of 90) were considered reasonable, with clinician A finding the automated planning path superior in 7 of 18 cases, and clinician B in 9 cases. Additionally, the time efficiency of the algorithm (35 s) was much higher than that of manual planning. The proposed method is expected to aid clinicians in preoperative path planning for thermal ablation of lung tumors. By providing a valuable reference for the puncture path during preoperative diagnosis, it may reduce the clinicians’ workload and enhance the objectivity and rationality of the planning process, which in turn improves the effectiveness of treatment.

The aim of this paper is to select a depth controller for innovative biomimetic underwater vehicle drives. In the process of optimizing depth controller settings, two classical controllers were used, i.e., the proportional–integral–derivative (PID) and the sliding mode controllers (SM). The parameters of the regulators' settings were obtained as a result of optimization by three methods of the selected quality indicators in terms of the properties of the control signal. The starting point for the analysis was simulations conducted in the MATLAB environment for the three optimization methods on three types of indicators for three different desired depth values. The article describes the methods and quality indicators in detail. The paper presents the results of the fitness function obtained during the optimization. Moreover, the time courses of the vehicle position relative to the desired depth, the side fin deflection angles, the calculated parameters of the control signals, and the observations and conclusions formulated in the research were presented. [ABSTRACT FROM AUTHOR]

A large volume of data is constantly accumulating in contemporary information technology relating to sophisticated computing. Stock price prediction is a popular topic and a difficult task due to the randomness and complexity of stock market-related computing systems. As stock market information web content develops, researchers and investors typically extract different indicator aspects, such as attitudes and events, from stock market real-time financial data predictions. Because of the current financial and unknown aspects in the stock market arena, stock price prediction is a difficult task, even though conventional authors worked on neural networks to improve stock price prediction in several financial domains. In this research, I present a Novel Greedy Heuristic Optimized Multi-instance Quantitative (NGHOMQ) strategy to investigate needed data from factors while rejecting their parameter interactions to improve index-based composite stock market movement prediction in multi-instance quantitative data. By employing unique heuristic calculations to describe sequential stock price-related events, it may be used to blend attitudes and events as well as to analyze quantitative data in a comprehensive way. In heuristic modes with numerous instances, Pareto optimization is utilized to prevent stock price prediction based on optimal statistical performance. Furthermore, our proposed approach may detect data input to produce stock market price forecasts in financial computing technologies. The efficiency of NGHOMQ compared to traditional neural network related frameworks and approaches is demonstrated using data from the Indian stock market. [ABSTRACT FROM AUTHOR]

Most text clustering algorithms adopt a single criterion optimization approach, which often fails to find good clustering solutions for a wide diversity of datasets with different clustering characteristics. The multi-objective meta-heuristic approach is utilized to seek optimal clustering by maximizing (or minimizing) more than two objective functions. In this paper, we propose a multi-objective memetic differential evolution algorithm (MOMDE) for text clustering. The MOMDE text clustering algorithm combines memetic and differential evolution algorithms to improve the search for optimal clustering by improving the balance between exploitation and exploration. Moreover, a combination with the dominance-based multi-objective approach is employed, which may improve the search for optimal clustering by maximizing or/and minimizing two cluster quality measures. The proposed algorithm is tested on six text clustering datasets from the Laboratory of Computational Intelligence. Our experimental results revealed that the performance of the MOMDE algorithm is better than state-of-the-art text clustering algorithms. Further validation is provided using the F-measure to assess the efficiency of the obtained clustering of MOMDE, whilst the multi-objective performance assessment matrices are used to evaluate the quality of Pareto-optimality. [ABSTRACT FROM AUTHOR]

The research introduces a unique deep-learning-based technique for remote rehabilitative analysis of image-captured human movements and postures. We present a ploninomial Pareto-optimized deep-learning architecture for processing inverse kinematics for sorting out and rearranging human skeleton joints generated by RGB-based two-dimensional (2D) skeleton recognition algorithms, with the goal of producing a full 3D model as a final result. The suggested method extracts the entire humanoid character motion curve, which is then connected to a three-dimensional (3D) mesh for real-time preview. Our method maintains high joint mapping accuracy with smooth motion frames while ensuring anthropometric regularity, producing a mean average precision (mAP) of 0.950 for the task of predicting the joint position of a single subject. Furthermore, the suggested system, trained on the MoVi dataset, enables a seamless evaluation of posture in a 3D environment, allowing participants to be examined from numerous perspectives using a single recorded camera feed. The results of evaluation on our own self-collected dataset of human posture videos and cross-validation on the benchmark MPII and KIMORE datasets are presented. [ABSTRACT FROM AUTHOR]

Solving non‐cooperative zero‐sum multi‐objective Games (zsMOGs), under undecided objective preferences results, for each of the players, in a Set of Rationalizable Strategies (SRS) to choose from. First, this paper deals with finding for each of the players a preferred subset of such rationalizable strategies based on a‐priori incorporation of partial preferences of the decision‐makers using secondary criteria. The obtained subset is termed the Set of Preferred Strategies (SPS). Here, a novel archive‐based co‐evolutionary algorithm is suggested to search for the SPS for each of the players. An academic example is suggested to demonstrate and validate the algorithm. It concerns a zsMOG that involves two adversarial planar manipulators. Based on a theorem that is proven here, a theoretic reference SRS is found for each of the players. This reference SRS is applied to find a reference SPS, which is used for validating the algorithm. Next, a comparison study is performed between the proposed archive‐based co‐evolutionary algorithm and an elite‐based version of this algorithm. The results clearly show that the archive‐based algorithm is superior to the elite‐based version, yielding results that correspond well to the theoretic sets. [ABSTRACT FROM AUTHOR]

In presence of multiple objectives to be optimized in Search-Based Software Engineering (SBSE), Pareto search has been commonly adopted. It searches for a good approximation of the problem’s Pareto-optimal solutions, from which the stakeholders choose the most preferred solution according to their preferences. However, when clear preferences of the stakeholders (e.g., a set of weights that reflect relative importance between objectives) are available prior to the search, weighted search is believed to be the first choice, since it simplifies the search via converting the original multi-objective problem into a single-objective one and enables the search to focus on what only the stakeholders are interested in. This article questions such a “weighted search first” belief. We show that the weights can, in fact, be harmful to the search process even in the presence of clear preferences. Specifically, we conduct a large-scale empirical study that consists of 38 systems/projects from three representative SBSE problems, together with two types of search budget and nine sets of weights, leading to 604 cases of comparisons. Our key finding is that weighted search reaches a certain level of solution quality by consuming relatively less resources at the early stage of the search; however, Pareto search is significantly better than its weighted counterpart the majority of the time (up to 77% of the cases), as long as we allow a sufficient, but not unrealistic search budget. This is a beneficial result, as it discovers a potentially new “rule-of-thumb” for the SBSE community: Even when clear preferences are available, it is recommended to always consider Pareto search by default for multi-objective SBSE problems, provided that solution quality is more important. Weighted search, in contrast, should only be preferred when the resource/search budget is limited, especially for expensive SBSE problems. This, together with other findings and actionable suggestions in the article, allows us to codify pragmatic and comprehensive guidance on choosing weighted and Pareto search for SBSE under the circumstance that clear preferences are available. [ABSTRACT FROM AUTHOR]

This paper studies the Pareto scheduling problem of minimizing total weighted completion time and maximum cost on a single machine. It is known that the problem is strongly NP-hard. Algorithms with running time $ O(n^3) $ are presented for the following cases: arbitrary processing times, equal release dates and equal weights; equal processing times, arbitrary release dates and equal weights; equal processing times, equal release dates and arbitrary weights.

Understanding microplastics' (MPs') transport and settling behaviors in aquatic environments is crucial for devising effective management strategies. This study contributes a novel modeling framework to develop accurate and interpretable drag and velocity models for MPs using machine learning techniques. It achieves faster model creation and improved accuracy than traditional methods like theoretical analysis and data fitting. The framework demonstrates high predictive accuracy across different MP types (1D, 2D, 3D, and mixed), with a coefficient of determination CD = 0.86–0.95 for the drag models and CD = 0.92–0.95 for the velocity models. Compared with best-performing empirical approaches, the new drag models exhibit an average reduction in root mean square error (RMSE) by 59% and mean absolute error (MAE) by 62%. Similarly, the velocity models show a mean decrease in RMSE and MAE by 27% and 25%, respectively. Moreover, the framework outperforms commonly used symbolic regression methods, reducing errors by 18%–27%. The sensitivity analysis reveals that the relative density difference and the dimensionless diameter are essential for predicting the settling of all MP types, while the effective shape parameters vary across different MP categories. By providing accurate predictions of MPs' settling dynamics, this study offers insights for developing targeted mitigation strategies to reduce MPs' environmental impacts. [Display omitted] • An intelligent framework for developing accurate and interpretable models for MPs. • Enhanced MPs' drag and velocity prediction with 59% and 27% RMSE reduction. • 1111 datasets of MPs with varying shapes, sizes, and densities for model evaluation. [ABSTRACT FROM AUTHOR]

De novo molecular design is the process of learning knowledge from existing data to propose new chemical structures that satisfy the desired properties. By using de novo design to generate compounds in a directed manner, better solutions can be obtained in large chemical libraries with less comparison cost. But drug design needs to take multiple factors into consideration. For example, in polypharmacology, molecules that activate or inhibit multiple target proteins produce multiple pharmacological activities and are less susceptible to drug resistance. However, most existing molecular generation methods either focus only on affinity for a single target or fail to effectively balance the relationship between multiple targets, resulting in insufficient validity and desirability of the generated molecules. To address the problems, an approach called clustered Pareto-based reinforcement learning (CPRL) is proposed. In CPRL, a pre-trained model is constructed to grasp existing molecular knowledge in a supervised learning manner. In addition, the clustered Pareto optimization algorithm is presented to find the best solution between different objectives. The algorithm first extracts an update set from the sampled molecules through the designed aggregation-based molecular clustering. Then, the final reward is computed by constructing the Pareto frontier ranking of the molecules from the updated set. To explore the vast chemical space, a reinforcement learning agent is designed in CPRL that can be updated under the guidance of the final reward to balance multiple properties. Furthermore, to increase the internal diversity of the molecules, a fixed-parameter exploration model is used for sampling in conjunction with the agent. The experimental results demonstrate that CPRL is capable of balancing multiple properties of the molecule and has higher desirability and validity, reaching 0.9551 and 0.9923, respectively. • Design a molecular clustering method to remove some unbalanced molecules. • Employ the Tanimoto-inspired Pareto method to find the optimal trade-off solution. • Devise a reinforcement learning agent to generate molecules in accordance with properties. • Propose an exploration policy to improve the internal diversity of molecules. [ABSTRACT FROM AUTHOR]

One of the most difficult aspects of scheduling operations on virtual machines in a multi-cloud environment is determining a near-optimal permutation. This task requires assigning various computing jobs with competing objectives to a collection of virtual machines. A significant number of NP-hard problem optimization methods employ multi-objective algorithms. As a result, one of the most successful criteria for discovering the best Pareto solutions is Pareto dominance. In this study, the Pareto front is calculated using a novel multi-objective minimum weight approach. In particular, we use particle swarm optimization (PSO) to expand the FR-MOS multi-objective scheduling algorithm by using fuzzy resource management to maximize variety and obtain optimal Pareto convergence. The competing objectives include reliability, cost, utilization of resources, risk probability, and time makespan. Most of the previous studies provide numerous symmetry or equivalent solutions as trade-offs for different objectives, and selecting the optimum solution remains an issue. We propose a novel decision-making strategy named minimum weight optimization (MWO). Multi-objective algorithms use this method to select a set of permutations that provide the best trade-off between competing objectives. MWO is a suitable choice for attaining all optimal solutions, where both the needs of consumers and the interests of service providers are taken into consideration. (MWO) aims to find the best solution by comparing alternative weights, narrowing the search for an optimal solution through iterative refinement. We compare our proposed method to five distinct decision-making procedures using common scientific workflows with competing objectives: Pareto dominance, multi-criteria decision-making (MCDM), linear normalization I, linear normalization II, and weighted aggregated sum product assessment (WASPAS). MWO outperforms these strategies according to the results of this study.

This paper develops a multi-objective optimization model for national high-speed railway network planning. Three objectives are proposed from the macro, meso, and micro perspectives, and these three objectives are considered simultaneously. Using real data, case studies are conducted to optimize China’s “four east–west and four north–south railway lines” network, which includes Beijing, Shanghai, Wuhan, Guangzhou, and Zhengzhou as main hubs. The results show that, on the one hand, the optimization model reduces the overlap between long-distance passengers and short-distance passengers on the high-speed railway line, facilitates the travel of passengers, and improves the line service capability; on the other hand, optimization of the network shortens the travel time of the passengers, reduces the cost of the railway, and improves the operation efficiency of the high-speed railway line network. The results show that the total travel time of all high-speed railway passengers in the optimization model is reduced by 18.4%, while the benefit rate of the operator increased by 21.99%.

Evolutionary multiobjective algorithms have become a popular choice to tackle the clustering problem. On the one hand, the simultaneous optimization of complementary clustering criteria offers an increased robustness to changes in data characteristics. On the other hand, the evolutionary search is able to approximate the Pareto optimal front and deliver a set of trade-offs between these criteria in a single algorithm execution. Decision making is the concluding stage of the pipeline, having as its goal the selection of a single, final solution from the set of candidate trade-offs produced. This is a complex task for which a definitive answer does not seem to be available, as the underlying assumptions of existing techniques may not hold for all applications. In this paper, we investigate an alternative approach to address this challenge: posing it as a learning problem. The key idea is to build a model that, given a proper characterization of solutions and their context (defined by the full approximation solution set and the specific clustering task at hand), is able to estimate quality and facilitate the identification of the best choice. To evaluate the suitability of this approach, we conduct a series of experiments over diverse synthetic and real-world datasets, including comparisons against a range of representative decision-making strategies from the literature. Our proposal exhibits greater flexibility in dealing with problems of varying characteristics, consistently outperforming the reference methods considered. This study demonstrates that it is possible to learn from the decision-making process in example settings and generalize the acquired knowledge to new scenarios.

Precision livestock farming promises substantial advantages in terms of animal welfare, product quality and reducing methane emissions, but requires continuous and reliable data on the animal's behavior. While systems suitable for use within the barn exist, grazing over long distances poses challenges. Here, we address this issue by proposing an ultra low-power Edge AI device, minimizing data transmission requirements and potentially improving accuracy as compared to classification-based solutions. Namely, we propose cow behavior distribution regression with Recurrent Neural Networks (RNNs), dubbed TinyCowNet, to estimate mixed-label sample spaces. Without quantization, the random search to minimize resources and maximize accuracy shows networks requiring a memory of 76kB on average and offering an accuracy up to 95.7%. These are implementable on a wide range of low-power Micro Controller Units (MCU) and Field Programmable Gate Arrays (FPGA). Furthermore, our proposed post-training full-integer quantization for RNNs combined with power estimation on 45nm CMOS using experimental literature shows a TinyCowNet occupying a memory around $\approx 2$ kB, having a hypothetical power consumption on the order of 200nW, delivering an accuracy of 95.2% and a Matthews correlation coefficient of 0.86. This work paves the way for the future creation of low-cost, highly accurate cow behavior estimation devices with long battery life that reduce the entry barriers currently hindering precision livestock farming outside the barn.

There have been numerous attempts at solving the optimal camera placement problem across multiple applications. Exact linear programming-based, as well as, heuristic combinatorial optimization methods were shown to be successful in providing optimal or near-optimal solutions to this problem. Working over a discrete space model is the general practice when solving the camera placement problem. However, discretized environments often limit the methods’ usage only to small-scale datasets due to resource and time constraints that grow exponentially with the number of 3D points collected from the discrete space. We propose a multi-resolution approach that enables the usage of existing optimization algorithms on large real-world problems modelled using high resolution 3D grids. Our method works by grouping together the given discrete set of possible camera locations into clusters of points, multiple times, resulting in multiple resolution levels. Camera placement optimization is repeated for all resolution levels while propagating the optimized solution from low to high resolutions. Our experiments on both simulated and real data with grids of varying sizes show that using our multi-resolution approach, existing camera placement optimization methods can be used even on high resolution grids consisting of hundreds of thousands of points. Our results also show that the strategy of grouping points together by exploiting underlying 3D geometry to optimize camera poses is not only significantly faster than optimizing on the entire set of samples but, it also provides better camera coverage.

The optimal capacitor allocation problem is suggested in this article. This study purposes to maximize the voltage stability, minimize the power losses, and consolidate the annual net savings. Calculating the optimal locations and sizes of fixed and switched capacitors is done in two steps. The first step is using the fuzzy expert rules in calculating the most candidate buses for capacitor allocation. While the second step is using a nondominated sorting genetic algorithm II (NSGA-II) in determining the list of Pareto optimal solutions and then applying a fuzzy decision maker to pick the most compromise. To emphasize the effectiveness of the proposed method, radial distribution systems are proposed; IEEE 33-bus system and the actual Portuguese IEEE 94-bus system. To demonstrate the strength and applicability of this method, a multiobjective water cycle algorithm (MOWCA), multiobjective grey wolf optimizer (MOGWO), and other optimizers used in the published papers are used. From simulation and analysis, the proposed NSGA-II outperforms other optimizers considered for comparison in achieving the maximum percentages of minimization in a real power loss of 32.369% and 31.1011% for the 33-bus system and 25.6296% and 25.3027% for the 94-bus system, the maximum percentages of minimization in a reactive power loss of 31.7916% and 30.4948% for 33-bus system and 25.9457% and 25.8001% for the 94-bus system, the maximum annual net savings of 23,612 $ and 23,131 $ for the 33-bus system for fixed and switched capacitors, respectively, and boosting the total voltage stability, which show its superior ability to give high-grade solutions.

Multi‐objective optimization (MOO) to produce low‐density polyethylene (LDPE) in a tubular reactor is performed for three different purposes, namely, maximizing the monomer exchange rate, lowering operational costs, and increasing the productivity. The ASPEN simulator is used to resolve optimization problems. Here, MOO of particle swamp optimization (MOPSO), non‐dominated sorting genetic algorithm (NSGA‐II), and a hybrid strategy of NSGA II‐MOPSO are employed as a model‐based optimization for LDPE production in a tubular reactor. The results demonstrated that the hybrid strategy of NSGA II‐MOPSO is the most effective MOO strategy. Using the hybrid strategy, the discovered solution set provides the utmost precision and diversity along the Pareto front. [ABSTRACT FROM AUTHOR]

Recently, large-scale multiobjective optimization has received increasing attention from the evolutionary multiobjective optimization (EMO) community. This has led to the emergence of a specialized research area called evolutionary large-scale multiobjective optimization (ELMO). In general, it is believed that multiobjective optimization problems become more difficult as the number of decision variables increases. However, the following two counterintuitive observations are obtained from careful examinations of recent ELMO studies. One is that experimental results on some large-scale multiobjective test problems were improved by increasing the number of decision variables. The other is that better results were obtained for some other large-scale multiobjective test problems by conventional EMO algorithms (EMOAs) than state-of-the-art ELMO algorithms (ELMOAs). These observations suggest that ELMOAs have not always been evaluated on appropriate test problems. Moreover, their performance is not always better than the performance of conventional EMOAs. In this letter, we first re-examine the performance of ELMOAs and conventional EMOAs on a wide variety of scalable multiobjective test problems. Then, counterintuitive experimental results are analyzed using the anytime performance evaluation scheme and distributions of randomly generated initial solutions. Based on the analysis, suggestions on how to handle large-scale multiobjective test problems with counterintuitive results are proposed. [ABSTRACT FROM AUTHOR]

Conventional multiobjective optimization algorithms (MOEAs) with or without preferences are successful in solving multi- and many-objective optimization problems. However, a strong hypothesis underlying their performance is that MOEAs are able to find a representative solution set to cover the entire Pareto-optimal front (PF) and decision makers are able to conveniently and precisely articulate their preference, which is not always easy to fulfill in practice. Accordingly, it is suggested that representative solutions in the naturally interesting regions of the PF rather than the whole PF should be targeted. A large body of research has been proposed to search or identify the knees or knee regions over the past decades. Therefore, this article aims to provide a comprehensive survey of the research on knee-oriented optimization. We start with a discussion of the importance and basic concepts of the knees, followed by a summary of knee-oriented benchmarks and indicators. After that, knee-oriented frameworks and techniques, and real-world applications are presented. Finally, potential challenges are pointed out and a few promising future lines of research are suggested. The survey offers a new perspective to develop MOEAs for solving multi- and many-objective optimization problems. [ABSTRACT FROM AUTHOR]

We present a novel algorithm that allows us to gain detailed insight into the effects of sparsity in linear and nonlinear optimization. Sparsity is of great importance in many scientific areas such as image and signal processing, medical imaging, compressed sensing, and machine learning, as it ensures robustness against noisy data and yields models that are easier to interpret due to the small number of relevant terms. It is common practice to enforce sparsity by adding the $\ell _1$ ℓ 1 -norm as a penalty term. In order to gain a better understanding and to allow for an informed model selection, we directly solve the corresponding multiobjective optimization problem (MOP) that arises when minimizing the main objective and the $\ell _1$ ℓ 1 -norm simultaneously. As this MOP is in general non-convex for nonlinear objectives, the penalty method will fail to provide all optimal compromises. To avoid this issue, we present a continuation method specifically tailored to MOPs with two objective functions one of which is the $\ell _1$ ℓ 1 -norm. Our method can be seen as a generalization of homotopy methods for linear regression problems to the nonlinear case. Several numerical examples – including neural network training – demonstrate our theoretical findings and the additional insight gained by this multiobjective approach. [ABSTRACT FROM AUTHOR]

This study deals with the problem of road side unit (RSU) location optimization for origin-destination (OD) demand estimation. With the point-to-point measurement provided by RSUs in connected vehicle environment, the errors of OD demand estimation come from two sources: 1) the lack of enough path flow information; and 2) the vehicle-to-RSU (V2R) communication delay. However, increasing the amount of path flow information collected by RSUs results in the increase of V2R communication delay encountered by each collected data packet. Moreover, it is difficult to find a global optimal solution by formulating the problem as a single objective program. To address the investigated problem, this study proposes a novel framework consisting of solving a bi-objective RSU location optimization problem and an OD demand estimation problem. This RSU location optimization problem is formulated as a bi-objective nonlinear binary integer program to balance the maximization of the amount of path flow information and the minimization of V2R communication delay. The OD demand estimation problem is formulated as a least square estimator to identify the RSU location scheme with the smallest OD demand estimation error, among the Pareto optimal solutions to the bi-objective program. An efficient $\varepsilon $ -constraint method is developed to generate the Pareto optimal solutions. The numerical example demonstrates that the proposed framework achieves 6.95 lower root-mean-square error of OD demand estimation, compared with the baseline framework. [ABSTRACT FROM AUTHOR]

In contrast to internal combustion engine vehicles, electric vehicles (EVs) obtain the power required for the compressor of air conditioning system from an electric source. Therefore, an optimal design for electric motor, the main component of an electric compressor, is essential for improving EV mileage. A multi-objective optimal design is required because the characteristics of the motor are in a trade-off relationship with each other. When the finite element method (FEM) is used, multi-objective optimal designs for the motor take a significant amount of time because of the diversity analyses required for the optimal-model search. To solve this problem, in this study, a multi-objective optimal design method of an SPMSM for an EVs air conditioner system compressor was proposed and applied using the NSGA-II and an analytical method. The validity of the proposed method was confirmed by comparing the characteristics of the optimal design model with those of the initially designed model. [ABSTRACT FROM AUTHOR]

CT image-guided lung biopsy surgery is the gold standard for lung tumor diagnosis. The preoperative path planning of the surgery is necessary, which aims to find some optimal needle insertion points on the patient's skin. The traditional path planning method is that the surgeons estimate the puncture paths by observing the patient's CT images, which is highly dependent on the experience of surgeons and affects the success rate of the surgery due to improper path selection. In this work, we proposed an intelligent lung biopsy surgical path planning method, which solved a multi-objective optimization problem. We segmented some chest important organs based on CT images, and quantitatively analyzed multiple clinical criteria. The concept of Pareto optimization was introduced to solve the multi-objective optimization problem in the method. Finally, some optimal surgery paths were provided for the surgeons. The retrospective evaluation of clinical data was performed, which was proved that the proposed method could be accepted in lung biopsy surgery. [ABSTRACT FROM AUTHOR]

Multiobjective evolutionary algorithms (EAs) based on decomposition are becoming successful and popular. Particularly, a conical area EA (CAEA) was developed to heighten the convergence and population diversity of decomposition-based algorithms for bi-objective optimization by employing a cone decomposition approach. The global Pareto optimality of cone decomposition was proved. It means that the optimum of every conical subproblem of the cone decomposition approach is Pareto optimal in the entire bi-objective space in the presence of a continuous frontier segment within its associated subregion. In this article, based on the global Pareto optimality, we further reveal that the cone decomposition approach also has a desired ability of $\mu $ -distribution convergence for bi-objective optimization. We first prove by the monotonicity and additivity of the Lebesgue measure and the squeeze theorem for limits that the hypervolume of the optimal solutions of all $\mu $ subproblems of the cone decomposition approach is infinitely close to that of the optimal $\mu $ -distribution for large enough $\mu $ in the presence of a continuous frontier. Meanwhile, the original CAEA is further improved to better approximate the optimal solutions of conical subproblems. The experimental results on bi-objective benchmark problems show that the improved CAEA achieves higher qualities of frontiers and better $\mu $ -distribution convergence in the terms of hypervolume error compared with six other popular multiobjective algorithms. [ABSTRACT FROM AUTHOR]

Expensive multiobjective optimization problems pose great challenges to evolutionary algorithms due to their costly evaluation. Building cheap surrogate models to replace the expensive real models has been proved to be a practical way to reduce the number of costly evaluations. Supervised learning techniques from the community of machine learning have been widely applied to build either regressors, which approximate the fitness values of candidate solutions, or classifiers, which estimate the categories of candidate solutions. Considering the characteristics of the data produced in optimization, this article proposes a new surrogate model, called a relation model, for evolutionary multiobjective optimization. Instead of estimating the qualities of candidate solutions directly, the relation model tries to estimate the relationship between a pair of solutions $\langle \mathbf {x}, \mathbf {y}\rangle $ , i.e., $\mathbf {x}$ dominates $\mathbf {y}$ , $\mathbf {x}$ is dominated by $\mathbf {y}$ , or $\mathbf {x}$ is nondominated with $\mathbf {y}$ in the case of multiobjective optimization. To implement this idea, first a balanced training set is prepared, then a classifier is built based on the training data set to learn the relationship, and finally, the classifier with a voting-scoring strategy is applied to estimate the relationship between the candidate solutions and parent solutions. By this way, the promising candidate solutions are recognized and evaluated by the real models. The new approach is applied to three well-known benchmark suites and two real-world applications, and the experimental results suggest that the proposed method outperforms some state-of-the-art methods based on regression and classification models on the given instances. [ABSTRACT FROM AUTHOR]

Due to the high variability and uncertainty of the wind speed, an interval forecast can provide more information for decision makers to achieve a better energy management compared to the traditional point forecast. In this article, a knee-based lower upper bound estimation method (K-LUBE) is proposed to construct wind speed prediction intervals (PIs). First, we analyze the underlying limitations of traditional direct interval forecast methods, i.e., their obtained PIs often fail to achieve a good balance between the interval width and the coverage probability. K-LUBE resolves the difficulty based on a multiobjective optimization framework in conjunction with a knee selection criterion. Specifically, a PI-NSGA-II multiobjective optimization algorithm is designed to obtain a set of Pareto-optimal solutions. A parameter transfer and a sample training strategies are developed to significantly improve the convergence speed of the optimization procedure. Then, the knee selection criterion is introduced to select the best tradeoff solution among the obtained solutions. In comparison with traditional methods, this method can always provide a reliable PI for decision makers. The procedure is automatic and requires no parameter to be specified in advance, making it more practical for use. The effectiveness of the proposed K-LUBE method is demonstrated through extensive comparisons with four traditional direct interval forecast methods and four classical benchmark models. [ABSTRACT FROM AUTHOR]

Sparse multiobjective optimization problems (MOPs) have become increasingly important in many applications in recent years, e.g., the search for lightweight deep neural networks and high-dimensional feature selection. However, little attention has been paid to sparse large-scale MOPs, whose Pareto-optimal sets are sparse, i.e., with many decision variables equal to zero. To address this issue, this article proposes an enhanced competitive swarm optimization algorithm assisted by a strongly convex sparse operator (SCSparse). A tricompetition mechanism is introduced into competitive swarm optimization, aiming to strike a better balance between exploration and exploitation. In addition, the SCSparse is embedded in the position updating of the particles to generate sparse solutions. Our simulation results show that the proposed algorithm outperforms the state-of-the-art methods on both sparse test problems and application examples. [ABSTRACT FROM AUTHOR]