Pareto Optimal Research Articles

Oppositional based artificial rabbits optimization applied for optimal allocation of nonlinear DG in distribution networks considering total harmonic distortion limit

This paper proposes a new optimization approach to identify the optimal placement and sizes of distributed generation (DG) units in a radial distribution network (RDN). Inverter-based DG also known as nonlinear DG (NLDG) units inject nonlinear current into the system, which can cause harmonic distortion. This harmonic distortion can limit the penetration of DG in the system by affecting the stability and reliability of the power system. Therefore, it is essential to consider harmonic distortion when identifying the optimal placement and sizes of DG units in RDNs to ensure DG’s safe and effective integration into the power system. The aim of the proposed work is to minimize real power loss, voltage deviation (VD), and total harmonic distortion limit (THD), and improve the voltage stability index (VSI) by integrating the search strategies of opposition-based learning and artificial rabbits optimization (ARO) to achieve better performance. The proposed algorithm, called opposition-based artificial rabbits optimization (OARO), considers different types of DG units like DG TYPE I and DG TYPE III and is suggested for two familiar RDNs: IEEE 33-bus, and 118-bus systems. The Pareto optimality concept has been introduced to solve the multi-objective problems of the systems. The simulation outcomes have been analyzed by comparing several optimization techniques in various cases. OARO has been shown to produce high-quality results with minimal iterations, reducing the time needed to solve the issue and conserving energy. Overall, the proposed approach offers a promising solution for identifying DG units’ optimal placement and sizes in RDNs while considering various operating scenarios.

Encoding prior knowledge in ensemble refinement.

The proper balancing of information from experiment and theory is a long-standing problem in the analysis of noisy and incomplete data. Viewed as a Pareto optimization problem, improved agreement with the experimental data comes at the expense of growing inconsistencies with the theoretical reference model. Here, we propose how to set the exchange rate a priori to properly balance this trade-off. We focus on gentle ensemble refinement, where the difference between the potential energy surfaces of the reference and refined models is small on a thermal scale. By relating the variance of this energy difference to the Kullback-Leibler divergence between the respective Boltzmann distributions, one can encode prior knowledge about energy uncertainties, i.e., force-field errors, in the exchange rate. The energy uncertainty is defined in the space of observables and depends on their type and number and on the thermodynamic state. We highlight the relation of gentle refinement to free energy perturbation theory. A balanced encoding of prior knowledge increases the quality and transparency of ensemble refinement. Our findings extend to non-Boltzmann distributions, where the uncertainty in energy becomes an uncertainty in information.

Research on Flexible Job Shop Scheduling Problem with Handling and Setup Time Based on Improved Discrete Particle Swarm Algorithm

With the gradual emergence of customized manufacturing, intelligent manufacturing systems have experienced widespread adoption, leading to a surge in research interests in the associated problem of intelligent scheduling. In this paper, we study the flexible job shop scheduling problem (FJSP) with setup time, handling time, and processing time in a multi-equipment work center production environment oriented toward smart manufacturing and make-to-order requirements. A mathematical model with the optimization objectives of minimizing the maximum completion time, the total number of machine adjustments, the total number of workpieces handled and the total load of the machine is constructed, and an improved discrete particle swarm algorithm based on Pareto optimization and a nonlinear adaptive inertia weighting strategy is proposed to solve the model. By integrating the model characteristics and algorithm features, a hybrid initialization method is designed to generate a higher-quality initialized population. Next, three cross-variance operators are used to implement particle position updates to maintain information sharing among particles. Then, the performance effectiveness of this algorithm is verified by testing and analyzing 15 FJSP test instances. Finally, the feasibility and effectiveness of the designed algorithm for solving multi-objective FJSPs are verified by designing an FJSP test example that includes processing time, setup time and handling time.

Multi-Objective Advantage Actor-Critic Algorithm for Hybrid Disassembly Line Balancing with Multi-Skilled Workers

The scheduling of disassembly lines is of great importance to achieve optimized productivity. In this paper, we address the Hybrid Disassembly Line Balancing Problem that combines linear disassembly lines and U-shaped disassembly lines, considering multi-skilled workers, and targeting profit and carbon emissions. In contrast to common approaches in reinforcement learning that typically employ weighting strategies to solve multi-objective problems, our approach innovatively incorporates non-dominated ranking directly into the reward function. The exploration of Pareto frontier solutions or better solutions is moderated by comparing performance between solutions and dynamically adjusting rewards based on the occurrence of repeated solutions. The experimental results show that the multi-objective Advantage Actor-Critic algorithm based on Pareto optimization exhibits superior performance in terms of metrics superiority in the comparison of six experimental cases of different scales, with an excellent metrics comparison rate of 70%. In some of the experimental cases in this paper, the solutions produced by the multi-objective Advantage Actor-Critic algorithm show some advantages over other popular algorithms such as the Deep Deterministic Policy Gradient Algorithm, the Soft Actor-Critic Algorithm, and the Non-Dominated Sorting Genetic Algorithm II. This further corroborates the effectiveness of our proposed solution.

IoT-enabled coordination for recommerce circular supply chain in the industry 4.0 era

The current advances in integrating devices through the Internet of Things (IoT) have encouraged researchers to focus on the applications of IoT in the recommerce business. Recommerce and the circular economy are growing worldwide as customers and businesses prioritize sustainability. A lack of coordination between channel participants is a significant hurdle to successfully adopting Circular principles in the recommerce industry. This study explores the coordination issue for a circular supply chain consisting of a Web-based recommerce platform and an IoT-enabled original equipment manufacturer. The proposed study is analyzed using a mixed-method research strategy combining a case study with a game-theoretic model. We analyze the problem under a consignment contract with revenue sharing for different channel structures. We introduce a hybrid contract to coordinate the decentralized channel in a cooperative game model. The results show that the consignment contract cannot coordinate the business in a decentralized structure. The recommerce platform achieves perfect coordination through the hybrid contract and obtains a unique Pareto optimization. We are proposing a IoT-enabled system in which resources are not wasted, thereby providing a way for businesses to use the circular economy concept to achieve sustainability goals.

Machine Learning Algorithms for Predictive Maintenance in Industrial Environments: A Comparative Study

In the realm of Industry 4.0, the utilization of artificial intelligence (AI) and machine learning for anomaly detection faces challenges due to significant computational demands and associated environmental consequences. This study aims to tackle the need for high-performance machine learning models while promoting environmental sustainability, contributing to the emerging concept of 'Green AI.' We meticulously assessed a wide range of machine learning algorithms, combined with various Multilayer Perceptron (MLP) configurations. Our evaluation encompassed a comprehensive set of performance metrics, including Accuracy, Area Under the Curve (AUC), Recall, Precision, F1 Score, Kappa Statistic, Matthews Correlation Coefficient (MCC), and F1 Macro. Concurrently, we evaluated the environmental footprint of these models by considering factors such as time duration, CO2 emissions, and energy consumption during training, cross-validation, and inference phases.
 
 While traditional machine learning algorithms like Decision Trees and Random Forests exhibited robust efficiency and performance, optimized MLP configurations yielded superior results, albeit with a proportional increase in resource consumption. To address the trade-offs between model performance and environmental impact, we employed a multi-objective optimization approach based on Pareto optimality principles. The insights gleaned emphasize the importance of striking a balance between model performance, complexity, and environmental considerations, offering valuable guidance for future endeavors in developing environmentally conscious machine learning models for industrial applications

Security Challenges of Vehicular Cloud Computing

In the realm of Industry 4.0, the utilization of artificial intelligence (AI) and machine learning for anomaly detection faces challenges due to significant computational demands and associated environmental consequences. This study aims to tackle the need for high-performance machine learning models while promoting environmental sustainability, contributing to the emerging concept of 'Green AI.' We meticulously assessed a wide range of machine learning algorithms, combined with various Multilayer Perceptron (MLP) configurations. Our evaluation encompassed a comprehensive set of performance metrics, including Accuracy, Area Under the Curve (AUC), Recall, Precision, F1 Score, Kappa Statistic, Matthews Correlation Coefficient (MCC), and F1 Macro. Concurrently, we evaluated the environmental footprint of these models by considering factors such as time duration, CO2 emissions, and energy consumption during training, cross-validation, and inference phases. While traditional machine learning algorithms like Decision Trees and Random Forests exhibited robust efficiency and performance, optimized MLP configurations yielded superior results, albeit with a proportional increase in resource consumption. To address the trade-offs between model performance and environmental impact, we employed a multi-objective optimization approach based on Pareto optimality principles. The insights gleaned emphasize the importance of striking a balance between model performance, complexity, and environmental considerations, offering valuable guidance for future endeavors in developing environmentally conscious machine learning models for industrial applications.

Machine Learning Algorithms for Predictive Maintenance in Industrial Environments: A Comparative Study

In the realm of Industry 4.0, the utilization of artificial intelligence (AI) and machine learning for anomaly detection faces challenges due to significant computational demands and associated environmental consequences. This study aims to tackle the need for high-performance machine learning models while promoting environmental sustainability, contributing to the emerging concept of 'Green AI.' We meticulously assessed a wide range of machine learning algorithms, combined with various Multilayer Perceptron (MLP) configurations. Our evaluation encompassed a comprehensive set of performance metrics, including Accuracy, Area Under the Curve (AUC), Recall, Precision, F1 Score, Kappa Statistic, Matthews Correlation Coefficient (MCC), and F1 Macro. Concurrently, we evaluated the environmental footprint of these models by considering factors such as time duration, CO2 emissions, and energy consumption during training, cross-validation, and inference phases. While traditional machine learning algorithms like Decision Trees and Random Forests exhibited robust efficiency and performance, optimized MLP configurations yielded superior results, albeit with a proportional increase in resource consumption. To address the trade-offs between model performance and environmental impact, we employed a multi-objective optimization approach based on Pareto optimality principles. The insights gleaned emphasize the importance of striking a balance between model performance, complexity, and environmental considerations, offering valuable guidance for future endeavors in developing environmentally conscious machine learning models for industrial applications.

Security Challenges of Vehicular Cloud Computing

In the realm of Industry 4.0, the utilization of artificial intelligence (AI) and machine learning for anomaly detection faces challenges due to significant computational demands and associated environmental consequences. This study aims to tackle the need for high-performance machine learning models while promoting environmental sustainability, contributing to the emerging concept of 'Green AI.' We meticulously assessed a wide range of machine learning algorithms, combined with various Multilayer Perceptron (MLP) configurations. Our evaluation encompassed a comprehensive set of performance metrics, including Accuracy, Area Under the Curve (AUC), Recall, Precision, F1 Score, Kappa Statistic, Matthews Correlation Coefficient (MCC), and F1 Macro. Concurrently, we evaluated the environmental footprint of these models by considering factors such as time duration, CO2 emissions, and energy consumption during training, cross-validation, and inference phases.
 
 While traditional machine learning algorithms like Decision Trees and Random Forests exhibited robust efficiency and performance, optimized MLP configurations yielded superior results, albeit with a proportional increase in resource consumption. To address the trade-offs between model performance and environmental impact, we employed a multi-objective optimization approach based on Pareto optimality principles. The insights gleaned emphasize the importance of striking a balance between model performance, complexity, and environmental considerations, offering valuable guidance for future endeavors in developing environmentally conscious machine learning models for industrial applications.

Pareto optimality of stochastic cooperative differential game with general delays in finite horizon

The Pareto optimality of stochastic cooperative differential game with a discrete delay, a moving-average delay and a noisy memory process is studied. We establish two sets of equivalent necessary and sufficient conditions for Pareto efficient strategies. The first set comes from reduction to a discrete delayed Pareto optimality, while the second set given by Malliavin derivative is derived by the decomposition of the adjoint equation and the relationship between two Hamiltonian functions. As applications, we use the theoretical results to an indefinite linear quadratic (LQ) Pareto game with general delays.

Cardinality-Constrained Multi-objective Optimization: Novel Optimality Conditions and Algorithms

In this paper, we consider multi-objective optimization problems with a sparsity constraint on the vector of variables. For this class of problems, inspired by the homonymous necessary optimality condition for sparse single-objective optimization, we define the concept of L-stationarity and we analyze its relationships with other existing conditions and Pareto optimality concepts. We then propose two novel algorithmic approaches: the first one is an iterative hard thresholding method aiming to find a single L-stationary solution, while the second one is a two-stage algorithm designed to construct an approximation of the whole Pareto front. Both methods are characterized by theoretical properties of convergence to points satisfying necessary conditions for Pareto optimality. Moreover, we report numerical results establishing the practical effectiveness of the proposed methodologies.

Collective or individual rationality in the Nash bargaining solution: efficiency-free characterizations

AbstractIn the classical bargaining problem, we propose a very mild axiom of individual rationality, which we call possibility of utility gain. This requires that for at least one bargaining problem, there exists at least one player who reaches a higher utility level than their disagreement utility. This paper shows that the Nash solution (Nash in Econometrica 18(2):155–162, 1950) is characterized by possibility of utility gain and continuity with respect to feasible sets together with Nash’s axioms except weak Pareto optimality. We also show that in Nash’s theorem, weak Pareto optimality can be replaced by conflict-freeness (introduced by Rachmilevitch in Math Soc Sci 76(C):107–109, 2015). This demands that when the agreement most preferred by all players is feasible, this should be chosen. Furthermore, we provide alternative and unified proofs for other efficiency-free characterizations of the Nash solution. This clarifies the role of each axiom in the related results.

Pedestrian evacuation planning under dam-break flood disaster considering road risk and road pedestrian demand

When facing disasters, a well-planned evacuation of people can significantly reduce casualties and disaster losses. Hurricane Katrina struck New Orleans in 2005, causing multiple dam failures and resulting in Lake Pontchartrain flooding back into the city. Taking this disaster as an example, this paper evaluates the risk of pedestrian evacuation on roads during dam-break flood disasters. The evaluation considers three aspects: road evacuation difficulty, disaster risk and road pedestrian demand. The fuzzy VIKOR model was used to evaluate 8 indicators for 50 candidate shelters. A dual-objective planning model was proposed to consider the time-varying and diverse emergency needs of disaster victims. By assessing the risk of pedestrian evacuation on the road and delineating restricted areas, the optimal path distance is calculated. Pareto optimality is then employed to achieve a trade-off between the convenience, cost, and applicability of the evacuation plan. In a complex post-disaster environment, the model can adapt flexibly to emergency rescue situations at various stages by adjusting the parameters in the objective and constraint equation. The Pareto optimal frontier can assist in determining the most suitable solution for the current scenario. In the case of New Orleans, 22 and 18 shelters were respectively selected from 34 candidate shelters for evacuation based on the simulated scenarios.

Vaccine supply chain decision and coordination with manufacturers competition under blockchain technology

With vaccine incidents occur frequently and the wide application of blockchain technology, the construction of blockchain-based vaccine supply chain (VSC) has become a feasible solution to effectively solve vaccine safety problems. However, there is a lack of research on the operation decision of blockchain-based VSC. Based on this, combined with the current situation of vaccine manufacturers competition in the vaccine market, we build a game model of blockchain-based VSC and explore its optimal decision and coordination strategies. The results show that the higher the vaccine substitutability, the higher the profit of each member. When the production cost ratio of the two vaccine manufacturers is low, they will be more willing to join the blockchain system. When the proportion of problematic vaccines is low, it is more beneficial for the VSC to adopt blockchain technology. The cost-sharing contract can neither effectively coordinate the blockchain-based VSC nor realize its Pareto optimality. The blockchain-based VSC coordination can be achieved by two vaccine manufacturers signing revenue-sharing contracts or two-part tariff contracts with the vaccination unit respectively.

Iterative multi-objective design of hydrogen embrittlement resistant high-strength steels using Bayesian optimization

The infiltration of hydrogen can weaken the strength and toughness of materials, especially in high-strength steel. Considering the inherent trade-off between strength and toughness in steel design, balancing these two factors with the material's hydrogen embrittlement sensitivity has become a huge challenge in steel design. The long cycle nature of hydrogen embrittlement testing makes the iteration of traditional trial and error methods infeasible for steel development. This work adopts active learning algorithms based on Bayesian and Pareto optimization to iteratively optimize the chemical composition and processing technology of high-strength steel, in order to simultaneously improve its hydrogen filled tensile strength (UTS_H) and hydrogen filled total elongation (TEL_H). After two rounds of iterative optimization, the iterative optimization algorithm significantly improved the strength prediction of materials after hydrogen charging. The machine learning model found 16 types of steel from over 15 million unknown materials. Eight materials showed better comprehensive performance than known data, with one material having a tensile strength exceeding 1800 MPa and an elongation of 12.6%. Subsequent experiments showed that the optimization algorithm demonstrated an understanding of the impact of austenite content on hydrogen embrittlement sensitivity in multiple experiments, and designed high-strength steel resistant to hydrogen embrittlement through the austenite content of martensitic steel.

Electronic Trading and Financial Crisis Effects in the e-MID Interbank Market: A Multivariate Multiplicative Error Model Analysis

We employ a multivariate multiplicative error model (MMEM) to explore the interplay between high-frequency return volatilities, trading volume, and trading intensities within the Italian Electronic Interbank Credit Market (e-MID). Our research question also aims at analysing the e-MID and, more specifically, the behaviour of traders in relation to the financial crisis. For this purpose, we consider four time periods: the first period before the first intervention by the ECB, the second period before the collapse of Lehman Brothers, and finally, the last intervention by the ECB. Utilising five-minute intervals, our analysis reveals a robust causal relationship among volatilities, volumes, and trading intensities in this electronic market, yielding highly significant coefficient estimates of the multivariate multiplicative error model (MMEM). However, these relationships change qualitatively, showing a shift in e-MID market behaviour before, during, and after the outbreak of the financial crisis. Notably, we find evidence that trading was in a Pareto optimum in the first period, but as soon as uncertainties hit the market, this Pareto optimum becomes unstable and breaks down completely in the last period of an extremely illiquid market state. To the best of our knowledge, this paper represents the first and inaugural empirical application of MMEM to an interbank credit market, contributing valuable insights into its intricate dynamics.

Leveraging trust for joint multi-objective and multi-fidelity optimization

In the pursuit of efficient optimization of expensive-to-evaluate systems, this paper investigates a novel approach to Bayesian multi-objective and multi-fidelity (MOMF) optimization. Traditional optimization methods, while effective, often encounter prohibitively high costs in multi-dimensional optimizations of one or more objectives. Multi-fidelity approaches offer potential remedies by utilizing multiple, less costly information sources, such as low-resolution approximations in numerical simulations. However, integrating these two strategies presents a significant challenge. We propose the innovative use of a trust metric to facilitate the joint optimization of multiple objectives and data sources. Our methodology introduces a modified multi-objective (MO) optimization policy incorporating the trust gain per evaluation cost as one of the objectives of a Pareto optimization problem. This modification enables simultaneous MOMF optimization, which proves effective in establishing the Pareto set and front at a fraction of the cost. Two specific methods of MOMF optimization are presented and compared: a holistic approach selecting both the input parameters and the fidelity parameter jointly, and a sequential approach for benchmarking. Through benchmarks on synthetic test functions, our novel approach is shown to yield significant cost reductions—up to an order of magnitude compared to pure MO optimization. Furthermore, we find that joint optimization of the trust and objective domains outperforms sequentially addressing them. We validate our findings with the specific use case of optimizing particle-in-cell simulations of laser-plasma acceleration, highlighting the practical potential of our method in the Pareto optimization of highly expensive black-box functions. Implementation of the methods in existing Bayesian optimization frameworks is straightforward, with immediate extensions e.g. to batch optimization possible. Given their ability to handle various continuous or discrete fidelity dimensions, these techniques have wide-ranging applicability in tackling simulation challenges across various scientific computing fields such as plasma physics and fluid dynamics.

Repeated game behavior between bidder and regulatory agency of construction engineering with intertemporal choice

The traditional theory of bidder and regulatory agency of construction engineering does not take into account the repeated periodicity of the game between the regulator and regulated party, so that the mathematical point of game equilibrium deviates from actual behavioral expression. According to the intertemporal nature of bidder and regulatory agency, this paper analyzed the payoff matrix of the subject of bidder and regulatory agency, constructed the repeated game behavior model of bidder and regulatory agency, and explored the game conditions of the behavioral expression (steady state and unsteady state) between the two game parties of construction engineering. The results shows that: (1) The administrative triggers are adopted in the normalized regulation, which could make both parties between bidder and regulatory agency reach Pareto Optimality; (2) The intertemporal choice behavior of the bidder is related to the economic punishments, extraneous benefits and legitimate benefits. The increase of economic punishments and legitimate benefits could reduce the illegal behaviors; (3) The larger the discounted function, the easier it is for the bidder to choose long-term legal behavior. Our work indicated that the key to establishing a long-term market mechanism between bidder and regulatory agency is to increase the future impact on the present, and construct the administrative trigger measures of infinitely repeated game.

Pareto task inference analysis reveals cellular trade-offs in diffuse large B-Cell lymphoma transcriptomic data

One of the main challenges in cancer treatment is the selection of treatment resistant clones which leads to the emergence of resistance to previously efficacious therapies. Identifying vulnerabilities in the form of cellular trade-offs constraining the phenotypic possibility space could allow to avoid the emergence of resistance by simultaneously targeting cellular processes that are involved in different alternative phenotypic strategies linked by trade-offs. The Pareto optimality theory has been proposed as a framework allowing to identify such trade-offs in biological data from its prediction that it would lead to the presence of specific geometrical patterns (polytopes) in, e.g., gene expression space, with vertices representing specialized phenotypes. We tested this approach in diffuse large B-cell lymphoma (DLCBL) transcriptomic data. As predicted, there was highly statistically significant evidence for the data forming a tetrahedron in gene expression space, defining four specialized phenotypes (archetypes). These archetypes were significantly enriched in certain biological functions, and contained genes that formed a pattern of shared and unique elements among archetypes, as expected if trade-offs between essential functions underlie the observed structure. The results can be interpreted as reflecting trade-offs between aerobic energy production and protein synthesis, and between immunotolerant and immune escape strategies. Targeting genes on both sides of these trade-offs simultaneously represent potential promising avenues for therapeutic applications.

Toward Unrivaled Chromatographic Resolving Power in Proteomics: Design and Development of Comprehensive Spatial Three-Dimensional Liquid-Phase Separation Technology.

Spatial comprehensive three-dimensional chromatography (3D-LC) offers an innovative approach to achieve unprecedented resolving power in terms of peak capacity and sample throughput. This advanced technique separates components within a 3D separation space, where orthogonal retention mechanisms are incorporated. The parallel development of the second- and third-dimension stages effectively overcomes the inherent limitation of conventional multidimensional approaches, where sampled fractions are analyzed sequentially. This review focuses on the design aspects of the microchip for spatial 3D-LC and the selection of orthogonal separation modes to enable the analysis of intact proteins. The design considerations for the flow distributor and channel layout are discussed, along with various approaches to confine the flow during the subsequent development stages. Additionally, the integration of stationary phases into the microchip is addressed, and interfacing to mass spectrometry detection is discussed. According to Pareto optimality, the integration of isoelectric focusing, size-exclusion chromatography, and reversed-phase chromatography in a spatial 3D-LC approach is predicted to achieve an exceptional peak capacity of over 30,000 within a 1-h analysis, setting a new benchmark in chromatographic performance.