Pareto Optimal Research Articles

Can search-based testing with pareto optimization effectively cover failure-revealing test inputs?

AbstractSearch-based software testing (SBST) is a widely-adopted technique for testing complex systems with large input spaces, such as Deep Learning-enabled (DL-enabled) systems. Many SBST techniques focus on Pareto-based optimization where multiple objectives are optimized in parallel to reveal failures. However, it is important to ensure that identified failures are spread throughout the entire failure-inducing area of a search domain, and not clustered in a sub-region. This ensures that identified failures are semantically diverse and reveal a wide range of underlying causes. In this paper, we present a theoretical argument explaining why testing based on Pareto optimization is inadequate for covering failure-inducing areas within a search domain. We support our argument with empirical results obtained by applying two widely used types of Pareto-based optimization techniques, namely NSGA-II (an evolutionary algorithm) and OMOPSO (a swarm-based algorithm), to two DL-enabled systems: an industrial Automated Valet Parking (AVP) system and a system for classifying handwritten digits. We measure the coverage of failure-revealing test inputs in the input space using a metric, that we refer to as the Coverage Inverted Distance (CID) quality indicator. Our results show that NSGA-II and OMOPSO are not more effective than a naïve random search baseline in covering test inputs that reveal failures. We show that this comparison remains valid for failure-inducing regions of various sizes of these two case studies. Further, we show that incorporating a diversity-focused fitness function as well as a repopulation operator in NSGA-II improves, on average, the coverage difference between NSGA-II and random search by 52.1%. However, even after diversification, NSGA-II still does not outperform random testing in covering test inputs that reveal failures. The replication package for this study is available in a GitHub repository (Replication package. https://github.com/ast-fortiss-tum/coverage-emse-24 2024.

Cooperative Strategies in Transboundary Water Pollution Control: A Differential Game Approach

This paper, based on differential game theory, examines governance models and cooperative strategies for managing cross-border water pollution in regions with uneven economic development. To address cross-regional water pollution, three differential game models are constructed under different scenarios: the Nash noncooperative mechanism, the pollution control cost compensation mechanism, and the collaborative cooperation mechanism. This study analyzes the dynamic changes in pollution emissions, governance investments, and economic returns within each model. The results indicate that the collaborative cooperation mechanism is the most effective, as it significantly reduces pollution emissions, maximizes overall regional benefits, and achieves Pareto optimality. In comparison, the pollution control cost compensation mechanism is suboptimal under certain conditions, while the Nash noncooperative mechanism is the least efficient, resulting in the highest pollution emissions. Furthermore, the research explores the influence of cooperation costs on the selection of governance models. It finds that high cooperation costs reduce local governments’ willingness to engage in collaborative cooperation. However, an appropriate compensation mechanism can effectively encourage less-developed regions to participate. Numerical analysis confirms the dynamic evolution of pollution stocks and economic returns under different models, and provides corresponding policy recommendations. This paper offers theoretical insights and practical guidance for cross-regional water pollution management, highlighting the importance of regional cooperation and cost-sharing in environmental governance.

Full-color electrophoretic display with high halftone image quality, reduced power, and deep-learning-enabled fast implementation

The electrophoretic display (EPD) with low power consumption, good sunlight readability, and flexibility is ideal for the Internet of Things. However, color EPDs have very limited grayscales (typically 1-bit) because it is challenging to precisely control the electrophoretic particles of multiple colors. Thus, halftone is usually employed to achieve continuous-tonal full-color EPDs alternatively. Nevertheless, halftone achieves continuous tones at the expense of a significant increase in driving current. This study first proposes and experimentally verifies a model that can accurately predict a driving current from image content. Next, a multi-objective optimized (MOO) halftone algorithm based on MOEA/D (multi-objective evolutionary algorithm based on decomposition) is proposed to consider image quality and driving current simultaneously. As a result, Pareto optimality is achieved, i.e., no image simultaneously performs better in image quality and driving current. A mean driving current reduction of 33.8% concerning the traditional error-diffusion algorithm is experimentally verified on a seven-color EPD with maintained halftone image quality. Considering the high computational complexity of the iteration-based MOO algorithm, this study also discusses the real-time generation of optimal halftone images using a generative adversarial network (GAN). Compared with the optimal halftone images slowly generated by the MOO, the GAN achieves almost the same driving current and a mean absolute error of 2.04, in terms of CIE76 color difference. The proposed algorithm enables full-color EPDs with high-quality continuous tones, reduced power consumption, and a GAN-based real-time implementation.

Professionally significant qualities of teaching producing in the context of digital development: Evaluating the effectiveness by education stakeholders

Introduction. The article is devoted to the problem of increasing the level of communicative culture in future economists in the digital era. The purpose of the research is to substantiate the effectiveness of professionally important qualities of teaching producing in universities of Economics, taking into account the digital format of interaction. Materials and Methods. The methodological basis of the study is the Pareto optimality principle (2080), which allows the authors to determine the necessary minimum of teaching resources in terms of modern communication knowledge for training economists in the digital age. During the study, an online survey was conducted in order to substantiate the effectiveness of professionally important qualities of teaching producing in the context of the digital specifics of the socio-economic space. The research sample consisted of students and academic staff of economics departments of the Financial University under the Government of the Russian Federation, Omsk State Pedagogical University, and Omsk State Technical University. Results. The theoretical analysis of the research problem revealed professionally important qualities of teaching producing (communication skills, digitals kills, knowledge of the principles of teaching ethics to create an atmosphere of trust and psychological safety), which facilitate the development of future economists’ communicative culture (motivational, cognitive, and functional components). According to the results of the empirical part of the study, the education stakeholders highly appreciated the effectiveness of professionally significant qualities of teaching producing and their positive correlation. Conclusions. The use of the Pareto rule allows the authors to conclude that the formation of professionally significant t qualities of teaching producing, which make up about 20% of the professional qualities composition for academic staff at universities of economics, determines 80% of the productivity of educational outcomes in terms of developing future economists’ communicative culture. This, in turn, determines the ability of graduates to build communication taking into account the career prospects at various levels of digitalization and interaction with artificial intelligence systems.

Multi-stage automatic and rapid ablation and needle trajectory planning method for CT-guided percutaneous liver tumor ablation.

Computer-assisted planning methods have increasingly contributed to preoperative ablation planning; however, these methods cannot automatically obtain the final optimal solution within a short time and are rarely validated in practice, greatly limiting their clinical applicability. We aimed to propose a full-automatic multi-stage ablation and needle trajectory planning method for CT-guided percutaneous liver ablation to attain the final optimal plans under multiple clinical constraints rapidly. Our proposed method integrates the ablation zone planning fulfilling complete tumor coverage and critical structure avoidance while reaching a trade-off between ablation number and healthy tissue damage, and needle trajectory planning under multiple clinical constraints. Our needle trajectory planning determines feasible skin entry regions based on hard constraints, where the multi-objective optimization (MOO) considering soft constraints is performed using the Pareto Optimality and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods for the final optimal solution. The performance of our proposed method was evaluated on 30 tumors of various characteristics from 23 patients and clinically validated in five clinical cases. Our proposed method achieved 99.8% treatment zone coverage and 40.5% ablation efficiency without involving critical structures, and completely satisfied multiple clinical constraints in all needle trajectory planning results. The average planning time was 23.6 s for tumors of different sizes. All the plans were considered clinically acceptable by the doctors' evaluation. Our method achieved complete tumor coverage without complications in clinical case validation. Our proposed planning method can generate a final optimal plan satisfying multiple clinical constraints within a short time, potentially facilitating preoperative planning for hepatic tumor ablation.

Aging-aware co-optimization of topology, parameter and control for multi-mode input- and output-split hybrid electric powertrains

Existing research has focused on battery intrinsic optimization, Hybrid Energy Storage System (HESS), and powertrain control perspectives to enhance battery life. However, the impact of powertrain physical architecture -control multi-layer co-optimization on battery life has been neglected. Therefore, this study innovatively proposes a multi-mode dedicated hybrid transmission (DHT)—from the perspective of powertrain architecture—to reduce battery life degradation by switching between input split (IS) and output split (OS) modes. In order to fully optimize the battery degradation potential, a topology-parameter-control multi-layer co-optimization (TPCMC) method is proposed in this paper. In the topology layer, graph theory method is used for automatic generation, screening and modeling. In the parameter and control layer, a novel near-optimal energy management strategy, Rapid Dynamic Programming+ (Rapid-DP+), is proposed by introducing Pareto optimization, slope filtering, and Battery Life Aging Optimization (BLAO) methods, which improves the computational efficiency by 3500 times with 9.09–21.1 % battery life optimization. The results show that, relative to the Toyota Prius, the optimized powertrain improves the acceleration by 41.3 %, fuel economy by 6.08 %–8.1 %, and battery life degradation by 9.9 %–22.5 % under different driving cycles. This paper contributes to battery life degradation in the electrified transportation through an advanced TPCMC approach.

Policy iteration based cooperative linear quadratic differential games with unknown dynamics

This article investigates the Pareto optimality of infinite horizon cooperative linear quadratic (LQ) differential games by policy iteration technique where the system dynamics are partially or completely unknown. Firstly, the policy iteration algorithm for the approximate solutions of the corresponding algebraic Riccati equation (ARE) without any prior knowledge of the matrix parameters of the dynamic system is derived by collecting the input and state information of each player. Secondly, when the presented specific rank condition is satisfied, the convergence of the proposed algorithm is rigorously demonstrated by recursion. Moreover, the weighting approach is employed to obtain the Pareto optimal strategy and the Pareto optimal solutions on the basis of the convex optimization theory. Finally, simulation results are reported to verify the feasibility and correctness of the proposed theoretical results.

Surrogate-assisted multi-objective Bayesian optimization for improved rheological design of bioinks

Extrusion bioprinting is a popular biofabrication technique for creating viable biological constructs with applications ranging from regenerative medicine and cancer research to therapeutics screening. A primary challenge within extrusion bioprinting lies in preserving cell viability and function amidst the shear forces generated during extrusion. Therefore, the formulation of shear-thinning bioinks with tailored flow properties is essential to minimize shear stress accumulation during printing. However, this task is complex, as the bioink’s flow properties are contingent upon numerous factors, including but not limited to the polymer concentration, spatial cell density in the bioink, molecular weight of the polymer, and the nozzle’s geometry. The current methods for adjusting flow properties are heuristic, posing challenges to the rapid development models and automation of the bioprinting workflow. In this study, we introduce a Gaussian Process-based multi-objective Bayesian optimization framework that rapidly identifies Pareto-optimal flow property values to minimize hydrodynamic shear stresses during extrusion. The optimization focuses on four flow parameters, minimum viscosity, maximum viscosity, consistency index, and flow behavior index from the power-law model of shear-thinning fluids alongside the nozzle outlet diameter. Following an initial set of 24 experimental runs, our sequential design approach required 21 iterations to achieve convergence on a set of flow parameters that meet the Pareto optimality criteria. The presented strategy, which integrates physics-based numerical models with data-driven optimization models, emerges as a tool for the rapid fine-tuning of bioink flow behaviors. The multi-objective framework also sets the stage for future exploration into cell viability and functional outcomes post-bioprinting.

Efficiency decomposition and frontier projection of two-stage network DEA under variable returns to scale

The efficiency decomposition and frontier projection of traditional two-stage network data envelopment analysis (DEA) model under variable returns to scale (VRS) are often not equivalent; which not only contradicts DEA theory, but also reduces the scientificity of the model. The main reason for this inequivalence is that there is a synergistic effect of variable scale return in two different stages. Therefore, this paper describes the production frontier of two-stage DEA under VRS for analyzing this synergistic effect, and then the efficiency evaluation pitfalls of two-stage DEA under VRS are identified. From the input orientation, output orientation, non-orientation perspectives, different two-stage network DEA models under VRS are respectively constructed to solve these evaluation pitfalls, and the equivalence relationships of their multiplier model and envelopment model are proved; and then the efficiency decomposition and frontier projection with equivalence relationship can be obtained to meet the different needs of decision-makers. Furthermore, variable intermediate element is discussed in the non-orientation model for achieving the Pareto optimality of two stages during the process of efficiency decomposition and frontier projection. By these models, the theoretical foundation of two-stage network DEA under VRS has been further improved. Finally, two examples are provided to illustrate the effectiveness of the new models.

Development of Sensor Data Fusion and Optimized Elman Neural Model-based Sign Language Recognition System

The sign language recognition system has placed an important role in disabled people’s lives. The researchers utilize various methods to fulfill disabled people’s requirements. However, the methods fail to access their sign at a reasonable cost with minimum computational difficulties. The improper sign access causes a reduction of the sign language recognition accuracy. Therefore, this study uses the sensor of wearable devices to capture people’s motions and actions to identify sign language. The collected information is fused into a competition level that understands disabled people’s requirements. After that, fused information is processed by the Pareto Optimized Hypertuned Deep Elman Neural Model (POHDENM). In addition, the data augmentation process is incorporated to train the data, which helps to recognize the large volume of sentences. Here, the fused information is split into words processed by the neural model that recognizes the sign language features. The extracted features are analyzed by an optimized method that recognizes the language with maximum accuracy. During the analysis, network parameters are hyper-tuned with the help of the Pareto model, which reduces the misrecognition error rate. Then, the created system efficiency is evaluated using the experimental analysis. The POHDENM approach achieves a high accuracy of 97.86% and an F1-score of 98.08%. The model’s high performance is achieved by fine-tuning its hyperparameters using the Pareto optimization algorithm, which balances precision of 98.06% and recall of 98.12%.

Bi-level expansion planning of power system considering wind farms based on economic and technical objectives of transmission system operator

Planning for the expansion of generation and transmission infrastructure, with a focus on integrating wind farms is presented in this study according to the goals of economic efficiency, operational performance, security, and reliability of the transmission system operator. The approach incorporates a bi-level optimization framework, with the upper level outlining the formulation of power system expansion planning to reduce construction and operational costs while adhering to the investment budget limits. In the lower-level model, which economic reliable-secure functioning is formulated for the transmission network. The objective function of the problem aims to minimize operational costs, energy losses, and anticipated energy not supplied, and the voltage security index. The constraints of this problem include AC optimal power flow model, security, and reliability limits. Scheme extracts a convex-linear model for the formulation using the conventional linearization technique. Pareto optimization driven by the aggregation of weighted functions results in a single-objective framework for the lower-level problem, and the Karush-Kuhn-Tucker technique presents a single-level model for the scheme. The approach utilizes stochastic optimization has been adopted to model load, wind farms, and network equipment availability uncertainties. Finally, the study cases yielded numerical findings that showcase the efficacy of the proposed scheme in achieving the desired economic, operational, security, and reliability status within the transmission network. This is particularly true in situations where there is appropriate generation and transmission expansion.

Accessibility of Pareto optima

Non-tâtonnement processes and planning procedures have been defined in different economic contexts, as dynamic processes to reach efficient allocations, with or without price adjustment, satisfying the property that, along the process, the utility of every agent is non-decreasing and transactions can occur, thus making a clear distinction with the study of tâtonnement processes whose goal is to reach competitive equilibria with transactions occurring only at equilibrium.In this paper, we provide sufficient conditions guaranteeing that every Pareto optimum which is preferred or indifferent to some given initial situation by every agent is accessible by a monotone efficient dynamic process. The framework considered is general enough to encompass the accessibility of Pareto optima by a non-tâtonnement barter process in an exchange economy, the neutrality of the MDP procedure in an economy with public goods, and other types of planning procedures.

Multi-armed Bandit Experimental Design: Online Decision-Making and Adaptive Inference

Multi-armed bandit has been well known for its efficiency in online decision-making in terms of minimizing the loss of the participants’ welfare during experiments (i.e., the regret). In clinical trials and many other scenarios, the statistical power of inferring the treatment effects (i.e., the gaps between the mean outcomes of different arms) is also crucial. Nevertheless, minimizing the regret entails harming the statistical power of estimating the treatment effect because the observations from some arms can be limited. In this paper, we investigate the trade-off between efficiency and statistical power by casting the multi-armed bandit experimental design into a minimax multi-objective optimization problem. We introduce the concept of Pareto optimality to mathematically characterize the situation in which neither the statistical power nor the efficiency can be improved without degrading the other. We derive a useful sufficient and necessary condition for the Pareto optimal solutions to the minimax multi-objective optimization problem. Additionally, we design an effective Pareto optimal multi-armed bandit experiment that can be tailored to different levels of the trade-off between the two objectives. Moreover, we extend the design and analysis to the setting where the outcome of each arm consists of an adversarial baseline reward and a stochastic treatment effect, demonstrating the robustness of our design. Finally, motivated by clinical trials, we examine the setting where the employed experiment must split the experimental units into a small number of batches, and we propose a flexible Pareto optimal design. This paper was accepted by George Shanthikumar, data science. Funding: The authors thank the Massachusetts Institute of Technology (MIT)-IBM partnership in Artificial Intelligence and the MIT Data Science Laboratory for support. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00492 .

Methods of Multi-Criteria Optimization of Technological Processes in a Fuzzy Environment Based on the Simplex Method and the Theory of Fuzzy Sets

Many modern technological objects in practice are characterized by the uncertainty of the initial information necessary for their management. Recently, one of the pressing scientific and practical problems is the development of new optimization methods for controlling the operating modes of such objects in a fuzzy environment. In this regard, the objective of this study is to develop methods of multi-criteria optimization in a fuzzy environment by modifying the simplex method and various optimality principles based on fuzzy mathematics methods. The methodology of the proposed study is based on a hybrid approach, which consists of the integrated use and modification of simplex methods and optimization methods with various optimality principles for working in a fuzzy environment. The main results are as follows: a simplex method of multi-criteria optimization of immeasurable criteria (here, we are talking about the impossibility of physical measurements of criteria, the values of which are estimated by decision maker); a theorem on the convergence of the solution sequence obtained using the proposed method to the minimum value of the criteria; a heuristic method based on a modification for fuzziness and a combination of the maximin and Pareto optimality principles, which allows effectively solving multi-criteria optimization problems in a fuzzy environment. The heuristic method proposed will be used to solve a real production problem—optimization of the technological process of benzene production.

Optimal banking with delegated monitoring

We propose a model of financial intermediation based on delegated monitoring, where firms' returns are private information that lenders can ascertain through costly state verification. Our model has two key features: lenders cannot commit to their verification strategies and there are aggregate shocks. Simple debt contracts are Pareto optimal with or without intermediation. We show that the benefits of intermediation can be limited by financial instability in the presence of aggregate shocks. However, a well-designed resolution mechanism ensures the Pareto optimality of financial intermediation, and a bail-out policy can restore financial stability.

Decision-making in low carbon supply chains: a blockchain-based lcsp perspective and a differential game model

Blockchain technology has reshaped how members of supply chains transfer information, effectively avoiding the phenomenon of information silos and helping to improve the emissions reduction performance and profit of each subject in the supply chain. It is now critical to understand how supply chain members can be encouraged to collaboratively invest in low-carbon service platforms based on blockchain technology to realise chain-wide systematic carbon reduction. In this regard, considering the time-dynamic characteristics of enterprise emissions reduction, this paper establishes a differential game model of collaborative emissions reduction in a low-carbon supply chain composed of a Stackelberg leader manufacturer and a supplier. We compare and analyse the four investment decision scenarios regarding whether the supplier and manufacturer invest in the blockchain low-carbon service platform under decentralised decision-making, as well as the equilibrium solutions of supply chain members under centralised decision-making scenarios by solving the Hamilton function. Finally, we introduce a bilateral cost-sharing contract to make the supply chain perfectly coordinated. We find that the significant unit return is an important incentive for supply chain members to take the lead in investing in a low carbon service platform (LCSP). In this regard, when only one member invests, the other one demonstrates free-riding behaviour. Under centralised decision-making, the supply chain can achieve Pareto optimality, and the bilateral cost-sharing contract can achieve perfect coordination of the supply chain, which is the best choice for the decision-makers of low-carbon supply chains. As the influence level of the LCSP gradually increases from small to large, the optimal decision-making of supply chain members gradually transitions from waiting for the right time to “hitchhike” to a strong willingness to cooperate. This study is of great reference value and practical significance for economic entities to improve profits, promote systematic carbon reduction in the whole chain and promote the sustainable development of low-carbon supply chains.

Probabilistic prediction-based multi-objective optimization approach for multi-energy virtual power plant

Virtual power plants (VPPs) are encountering multiple challenges due to market uncertainties and power network instability. In this paper, a novel probabilistic prediction-based multi-objective optimization framework for VPP is proposed to maximize operating profit while minimizing pollutant emissions and voltage deviations in the distribution network, which considers the uncertainties of wind power and electricity price. In this framework, the VPP that participates in the energy and ancillary service markets firstly aggregates the wind farms, the electric vehicle charging stations (EVCS), and the combined cooling, heating, and power subsystems to improve the utilization efficiency and operational flexibility of multiple energy sources. Then, a new Pareto optimizer, called multi-objective hybrid sand cat swarm optimization and strength firefly algorithm, is proposed to tackle the multi-objective optimization model of VPP. The proposed hybrid algorithm utilizes the advantages of sand cat swarm optimization and strength firefly algorithm mechanisms to facilitate local exploitation and global exploration. Finally, a new deep reinforcement learning probabilistic prediction approach based on quantile regression deep deterministic policy gradient is modeled to evaluate the uncertainties. The proposed models and methods have been thoroughly discussed on a modified distributed network. It is calculated that compared with the VPP without EVCS, the operating profit of the proposed VPP increases by 18.69%, and the emissions and voltage deviation of the proposed VPP are reduced by 3.42% and 10.44%, respectively. Experimental results also prove that the performance of the proposed Pareto optimizer and probabilistic prediction approach is superior to other benchmark techniques.

Pareto optimality in cooperative differential game of nonlinear mean-field backward stochastic system

This paper is concerned with a necessary condition and a sufficient condition for the existence of Pareto optimal strategy in a cooperative game driven by a nonlinear mean-field backward stochastic system. First, the game problem is equivalently converted into a set of constrained single objective optimal control problems. Next, we utilise Ekeland's variational principle to dispose constraints, and propose a necessary condition in the form of stochastic maximum principle. Meanwhile, we point out that under certain convex assumptions, the necessary condition we proposed is also sufficient. Finally, we give an example to explain the above theoretical results.

Fair and efficient allocations when preferences are single-dipped

One unit of an infinitely divisible and non-disposable commodity has to be allocated among a group of agents with single-dipped preferences. We combine Pareto optimality with equal treatment of equals, the equal division lower bound, the equal division core, envy-freeness, and group envy-freeness. For each of these fairness requirements, we provide a necessary and sufficient condition for compatibility with Pareto optimality and we characterize all corresponding allocations for each preference profile.

Trust region based chaotic search for solving multi‐objective optimization problems

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