Optimal Theory Research Articles

Time-Reversion Fast-Sampling Score-Based Model for Limited-Angle CT Reconstruction.

The score-based generative model (SGM) has received significant attention in the field of medical imaging, particularly in the context of limited-angle computed tomography (LACT). Traditional SGM approaches achieved robust reconstruction performance by incorporating a substantial number of sampling steps during the inference phase. However, these established SGM-based methods require large computational cost to reconstruct one case. The main challenge lies in achieving high-quality images with rapid sampling while preserving sharp edges and small features. In this study, we propose an innovative rapid-sampling strategy for SGM, which we have aptly named the time-reversion fast-sampling (TIFA) score-based model for LACT reconstruction. The entire sampling procedure adheres steadfastly to the principles of robust optimization theory and is firmly grounded in a comprehensive mathematical model. TIFA's rapid-sampling mechanism comprises several essential components, including jump sampling, time-reversion with re-sampling, and compressed sampling. In the initial jump sampling stage, multiple sampling steps are bypassed to expedite the attainment of preliminary results. Subsequently, during the time-reversion process, the initial results undergo controlled corruption by introducing small-scale noise. The re-sampling process then diligently refines the initially corrupted results. Finally, compressed sampling fine-tunes the refinement outcomes by imposing regularization term. Quantitative and qualitative assessments conducted on numerical simulations, real physical phantom, and clinical cardiac datasets, unequivocally demonstrate that TIFA method (using 200 steps) outperforms other state-of-the-art methods (using 2000 steps) from available [0°, 90°] and [0°, 60°]. Furthermore, experimental results underscore that our TIFA method continues to reconstruct high-quality images even with 10 steps. Our code at https://github.com/tianzhijiaoziA/TIFADiffusion.

Optimization Strategy for Storage Location Allocation in Air Cargo Warehouse Area

Facing rapid growth in air cargo demand within the global supply chain and limitations of traditional storage location allocation methods, this study proposes an intelligent optimization strategy for storage location allocation. The strategy considers key factors like flight schedules, cargo collection and delivery data, cargo terminal topology, and in-and-out paths. It intelligently allocates each cargo batch to the most suitable location, assigning more flights' cargo to locations near the exit, thereby reducing cargo handling distance and offloading time, and enhancing operational efficiency and cargo handling capacity in the air freight sector. The algorithm, based on the latest optimization theories and techniques, seeks optimal solutions under various constraints. The model and algorithm’s effectiveness is validated through simulation experiments using actual flight data. The results indicate that, compared to traditional strategies, the optimized strategy reduces response time by 62%.

General Equilibrium Models in the Economy and in the Field of Development Policy

Economics is heavily reliant on mathematics, and with the advent of computers we can now make better decisions based on vast amounts of data. This article provides an overview of the mathematical structure underlying decision-making, which is motivated by elements of optimisation theory, which are currently supported by algorithms developed in equilibrium models in economics. We show a motivational overview of the theory of general equilibrium models and their advantage today. Although the approach of a computable general equilibrium model is a complicated task, it undoubtedly allows better decision making, especially in governmental decisions and little has been done in some developing countries.

An iterative approach for the solution of fully fuzzy linear fractional programming problems via fuzzy multi-objective optimization

<abstract><p>The primary goal of optimization theory is to formulate solutions for real-life challenges that play a fundamental role in our daily lives. One of the most significant issues within this framework is the Linear Fractional Programming Problem (LFrPP). In practical situations, such as production planning and financial decision-making, it is often feasible to express objectives as a ratio of two distinct objectives. To enhance the efficacy of these problems in representing real-world scenarios, it is reasonable to utilize fuzzy sets for expressing variables and parameters. In this research, we have worked on the Fully Fuzzy Linear Fractional Linear Programming Problem (FFLFrLPP). In our approach to problem-solving, we simplified the intricate structure of the FFLFrLPP into a crisp Linear Programming Problem (LPP) while accommodating the inherent fuzziness. Notably, unlike literature, our proposed technique avoided variable transformation, which is highly competitive in addressing fuzzy-based problems. Our methodology also distinguishes itself from the literature in preserving fuzziness throughout the process, from problem formulation to solution. In this study, we conducted a rigorous evaluation of our proposed methodology by applying it to a selection of numerical examples and production problems sourced from the existing literature. Our findings revealed significant improvements in performance when compared to established solution approaches. Additionally, we presented comprehensive statistical analyses showcasing the robustness and effectiveness of our algorithms when addressing large-scale problem instances. This research underscores the innovative contributions of our methods to the field, further advancing the state-of-the-art in problem-solving techniques.</p></abstract>

Joint or not: unpacking the decision-making mechanism of pandemic control between two nations – from the perspective of cost optimization

Joint control has been proposed to alleviate the negative spillover of the pandemic. The authority faces a decision dilemma on whether to undertake joint control, which is overlooked by the scientific community. This paper, aiming to clarify the decision-making mechanism under cost optimization, draws on the optimization theory to find the optimal strategies and start time under independent and joint scenarios with an improved infectious disease model. Besides, a simulation analysis, based on the survey in Ruili – a border area between Myanmar and China, is employed. The crucial findings highlight that (1) The control measures contribute to reducing the peak number or peak time of confirmed patients under optimal decision. (2) As the delay of start time, the epidemic control for both countries undergoes from validity to failure. (3) More cost-savings come with earlier joint control but with uneven distribution. Joint strategy mitigates the cost for the one with severe epidemics, but the effect is insignificant for the other. This study, concentrating on cost optimization, provides a novel insight into pandemic control. The results enrich the decision analysis in the context of pandemic joint governance while providing inter-regional control support for the decision-maker to tackle various epidemics.

Task scheduling in multi-cloud environment via improved optimisation theory

Task scheduling in multi-cloud environment via improved optimisation theory

Efficiency conditions in multiple-objective optimal control models under generalized hypotheses

<p>Since not every problem in optimization theory involves convex functionals, in this study, we introduced new classes of generalized convex functionals. More precisely, under generalized hypotheses, we stated new efficiency conditions associated with a class of multiple-objective optimal control models. To this end, we first defined the $ G_{\theta} $-Fritz John problem and, by considering it, we established a link between the solutions of $ G_{\theta} $-Fritz John problem and efficient solutions of the considered model $ (P) $. In addition, we formulated the $ G_{\theta} $-necessary efficiency conditions for a feasible solution in $ (P) $. After that, we established a connection between the newly defined concept of $ G_{\theta}-KT $ points to $ (P) $ and the efficient solutions of $ (P) $. Finally, we turned our attention to the $ G_{\theta} $-sufficient efficiency conditions for a feasible solution to $ (P) $. More precisely, we established that any feasible solution to $ (P) $ will be an efficient solution if the assumption of $ G_{\theta} $-convexity (and/or $ G_{\theta} $-quasiconvexity, $ G_{\theta} $-strictly quasiconvexity, $ G_{\theta} $-monotonic quasiconvexity) is imposed on the involved functionals.</p>

Efficiency Improvement Mechanism of Smart Logistics Technology in Rural E-commerce Logistics and Distribution

Abstract This paper addresses the problems existing in the current two-way logistics and distribution model of rural e-commerce and proposes the study of rural e-commerce distribution efficiency with the technical support of intelligent logistics. Based on the knowledge of multi-objective optimization theory, the maximization of distribution efficiency of logistics enterprises and the maximization of demand coverage as the objective function, while the constraints are vehicle load, service time, path constraints, and finally, complete the construction of the multi-objective optimization model of rural e-commerce logistics and distribution efficiency. According to the NSGA-II algorithm with low computational complexity, fast operation speed, and good convergence, the NSGA-II algorithm is used to solve the constructed model, and then the optimal solution of rural e-commerce logistics distribution efficiency is derived. With regional logistics as the research subject, a multi-objective optimization model for rural e-commerce logistics distribution efficiency supported by intelligent logistics technology is simulated and analyzed. The data show that compared with other algorithms, the NSGAII algorithm has a smoother learning curve oscillation, while the final result of convergence (-1.473), the objective function is smaller (20.42), and the optimal total transportation time is smaller (609.22s). Finally, in order to solve the current rural e-commerce logistics and distribution problems in part, from the improvement of rural e-commerce logistics and distribution system, strengthen the rural logistics infrastructure construction of the two aspects of rural e-commerce logistics and distribution efficiency improvement strategy.

Модификация модели поддержки принятия решений при распределении пожарных автомобилей

Purpose. The article deals with improving the decision making support model for fire trucks distribution to territorial fire and rescue garrisons. The necessity of this improvement is caused by the decision making dimension increase due to introducing a new criterion that allows assessing adequacy of territorial fire and rescue garrisons’ means and forces. The authors’ purpose is to modify the practice-oriented procedure for supporting decision making by officials of EMERCOM of Russia. The set purpose determines the following tasks: to analyze decisions in the field of assessing means and forces sufficiency; to improve the multi-criteria model previously developed by the authors; to derive importance coefficients for the model based on four criteria; to set a vector for further research in the proposed area. Methods. During the research methods of the relative importance of criteria in multi-criteria optimization theory have been used. Findings. The authors propose to improve methods for data acquisition and models identification, forecasting and institutes management based on retrospective, current and expert information, which was implemented by introducing a supplementary criterion into the existing decision making support model. Research application field. The results obtained can be used by decision makers in assessing territorial fire and rescue garrisons in their activities. Conclusions. The authors have contributed to developing a mathematical model basic for decision making support procedures for fire trucks distribution to territorial fire and rescue units. The multi-criteria model application will make it possible to take into account additional criteria of personnel provision in fire and rescue garrisons.

Stage-by-Stage Wavelet Optimization Refinement Diffusion Model for Sparse-View CT Reconstruction.

Diffusion model has emerged as a potential tool to tackle the challenge of sparse-view CT reconstruction, displaying superior performance compared to conventional methods. Nevertheless, these prevailing diffusion models predominantly focus on the sinogram or image domains, which can lead to instability during model training, potentially culminating in convergence towards local minimal solutions. The wavelet transform serves to disentangle image contents and features into distinct frequency-component bands at varying scales, adeptly capturing diverse directional structures. Employing the wavelet transform as a guiding sparsity prior significantly enhances the robustness of diffusion models. In this study, we present an innovative approach named the Stage-by-stage Wavelet Optimization Refinement Diffusion (SWORD) model for sparse-view CT reconstruction. Specifically, we establish a unified mathematical model integrating low-frequency and high-frequency generative models, achieving the solution with an optimization procedure. Furthermore, we perform the low-frequency and high-frequency generative models on wavelet's decomposed components rather than the original sinogram, ensuring the stability of model training. Our method is rooted in established optimization theory, comprising three distinct stages, including low-frequency generation, high-frequency refinement and domain transform. The experimental results demonstrated that the proposed method outperformed existing state-of-the-art methods both quantitatively and qualitatively.

Optimization of Biped Robot Walking Based on the Improved Particle Swarm Algorithm

The central pattern generator (CPG) is widely applied in biped gait generation, and the particle swarm optimization (PSO) algorithm is commonly used to solve optimization problems for CPG network controllers. However, the canonical PSO algorithms fail to balance exploration and exploitation, resulting in reduced optimization accuracy and stability, decreasing the control effectiveness of CPG controllers. In order to address this issue, a balanced PSO (BPSO) algorithm is proposed, which achieves better performance by balancing the algorithm’s exploration and exploitation capabilities. The BPSO algorithm’s solving process consists of two phases: the free exploration phase (FEP), which emphasizes exploration, and the attention exploration phase (AEP), which emphasizes exploitation. The proportion of each phase during optimization is controlled by an adjustable parameter. The BPSO algorithm is subjected to qualitative, numerical, convergence, and statistical analyses based on 13 benchmark functions. The experimental results from the benchmark functions demonstrate that the BPSO algorithm outperforms other comparison algorithms. Finally, a linear walking optimization method for humanoid robots based on the BPSO algorithm is established and tested in the Webots simulator. Comparative results with two other optimization methods show that the BPSO‐based optimization method enables the robot to achieve greater walking distance and smaller lateral deviation within a fixed number of iterations. Compared to the other two methods, walking distance increases by at least 60.98% and lateral deviation decreases by at least 1.96%. This research contributes to enhancing the locomotion capabilities of CPG‐controlled humanoid robots, enriching biped gait optimization theory and promoting the application of CPG gait control methods in humanoid robots.

Research on rural landscape garden planning and plantscape construction method based on digital twin technology under rural revitalization strategy

Abstract This paper integrates the landscape information model, designs the digital twin rural landscape architecture, and analyzes the advantages of the digital twin rural landscape model. The practical application of digital twin technology is examined from the planning and design stage to the construction stage. Considering the actual construction site layout and the consumption of construction materials, the NSGA-II algorithm is selected to optimize the dynamic layout of temporary facilities by combining multi-objective optimization theory. Using the digital twin to drive the dynamic layout of the construction site, a digital twin-driven construction site layout mechanism is proposed in combination with BIM technology. The actual construction scheduling tasks are used to analyze the feasibility of the construction site layout scheme generated using digital twin technology. Analyze the construction of rural landscape gardens planned using digital twin technology in terms of ecological, social, and economic benefits. The convergence minimum is achieved at 0.15 and 0.09 when comparing the number of iterations between the initialized layout and the digital twin-driven layout. The corresponding scheme reaches its optimal state during the construction stage and can provide the most suitable site arrangement scheme during that stage. The rural landscape garden planned by the digital twin technology can generate an economic return value of 71.756 yuan/m² per year, which can further promote rural revitalization.

An Optimized Multi-Band Antenna for UWB Ad-Hoc Network Based on Topology Optimization Theory

An Optimized Multi-Band Antenna for UWB Ad-Hoc Network Based on Topology Optimization Theory

A multi-objective fuzzy flexible job shop scheduling problem considering the maximization of processing quality

This paper analyzes practical production characteristics, including customer's stringent quality requirements and uncertain processing time in aircraft shaft parts manufacturing. Considering the above characteristics, we propose a multi-objective fuzzy aircraft shaft parts production scheduling problem considering the maximization of production quality. We define this problem as a multi-objective fuzzy flexible job shop scheduling problem (MO-fFJSP) with fuzzy processing time. To address this problem, we developed an improved multi-objective spider monkey optimization (IMOSMO) algorithm. IMOSMO integrates strategies such as genetic operators, variable neighborhood search and Pareto optimization theory on the framework of the conventional Spider Monkey Optimization (SMO) framework and discretize the continuous SMO algorithm to solve MO-fFJSP. To enhance the efficiency of the algorithm, we further adjust the sequence of the local leader learning phase and the global leader learning phase within the proposed IMOSMO framework. We conduct a comparative analysis between the performance of IMOSMO and NSGA-Ⅱ using 28 cases of varying scales. The computational results demonstrate the superiority of our algorithm over NSGA-Ⅱ in terms of both solution diversity and quality. Moreover, the performance of the proposed algorithm upgrades as the problem scale increases.

Digital Transformation of Optimal Decision-making in Economic and Engineering Systems Based on the Theory and Methods of Vector Optimization

Objective: The purpose of the work is to analyze, study the processes of digital transformation and make optimal decisions in economic and engineering systems based on the theories and methods of vector optimization. Methods: Methods for studying the processes of digital transformation of economic and engineering systems are based on the author's theory of vector optimization, which is represented by: axiomatics and optimality principles that show how one solution is better than another. On the basis of the principles of optimality, constructive methods for solving vector problems have been built, firstly, with equivalent criteria, and, secondly, with a given priority of the criterion: https://rdcu.be/bhZ8i (Springer Nature's "Vector optimization with equivalent and priority criteria" is distributed free of charge.) The paper presents that the problem of accepting the optimal process is carried out as a result of the digital transformation of the initial (statistical, experimental) data into vector problems in optimization, which represents a mathematical model of economic and engineering systems. Depending on the number and type of criteria, we solve problems with a vector of criteria, each component of which is directed to the maximum, and with a vector of criteria, each component of which is aimed at a minimum, in aggregate. Results: The result of the study is the practical orientation of digital transformation, which is shown in the computer-aided design of economic and engineering systems based on vector optimization. For this purpose, software for solving vector problems of linear and nonlinear programming has been developed. Linear vector problems are used in economic systems (forecasting, planning). Software for solving vector problems of nonlinear programming are used digital transformation of optimal decision-making in engineering problems. Conclusion: In conclusion, numerical examples in the field of economic systems are presented by the digital transformation of optimal decision-making in the development of the company. When making optimal decisions in engineering systems, the following has been developed: the construction of initial data (technical task) for modeling; transformation of a mathematical model under uncertainty into a model under conditions of certainty; making an optimal decision with equivalent criteria; making the optimal decision with a given priority criterion

영어 무성치경폐쇄음의 기식음화와 성문음화의 자질 보존성 관계 연구

This study is to argue that the choice between aspiration and glottalization of /t/ by speakers in the same phonological environment can be influenced by differences in the ranking of feature preservation constraints. Besides, I propose that if Korean learners of English tend to produce aspiration in environments where glottalization are prevalent, it results from applying the constraint favoring listener's perceptive ease being ranked higher than that favoring speaker's articulatory ease. Allophones of /t/ can vary even among native speakers due to different sociolinguistic factors. When Korean learners of English apply the constraint of [aspirated] feature preservation at a high ranking, with the intention of enhancing listener's perception, pronunciation errors may occur. Also, if they rank high the constraint for avoiding consonant cluster, which is not allowed in Korean language, they can produce awkward pronunciation. Therefore, by analyzing aspiration and glottalization of /t/ through Optimality Theory, this paper aims to demonstrate that the reason English /t/ exhibits more phonological variants and allophones is attributed to the low feature preservation it holds.

Estimation of the Noise Immunity Characteristics of Telecommunication Network

The harmful impacts of various interference sources in the communication channel of data transmission systems and an increasing noise immunity in linear distortion conditions are important problems in the development of information transmission systems. To improve reliability and the data receiving accuracy, a mathematical model is developed, which allows to assessment of the survivability of telecommunication systems to interference at incoherent receiving. In this case is assumed that system impact the unintentional sources. The mathematical model considers the communication quality, spectral efficiency, coding method, and modulation principle used during the multimedia services. Presently, the development of the corporate multiservice communication networks is in conditions where the size of user and service traffic packets is rapidly increase. To ensure transmission reliability is promising of an implementation of the coherent modem. It will allow a decrease in the failures of such systems. To increase the transmission rate used coherent modem with noise-resistant characteristics. However, the communication quality in this case is decreased, due to more influence of the inter-symbol interference to the data quality receiving. The synthesis of optimal signals based on rectangular pulses with different shapes by the coherent modem for the data transmission system aims is solved by using of matching filter that has a pulse transient response. To obtain this the frequency limiter is used at the output of the channel digital demodulator. One of the ways to properly solve the problem noted above is by applying in the development of telecommunication multiservice networks the mathematical model with correction ability. This one can be developed based on the optimal signal-receiving theory.

Energy-optimal adaptive artificial potential field method for path planning of free-flying space robots

Aiming at planning a collision-free path for a free-flying space robot (FFSR), an energy-optimal collision avoidance strategy is proposed using the adaptive artificial potential field (AAPF) approach. To address the goals non-reachable with obstacles nearby (GNRON) issues of artificial potential field (APF), an adaptive and secure path planning method is proposed by incorporating a smooth decay function into the repulsive potential field. Meanwhile, a time-varying shaping parameter of attractive potential field is optimized combined with the state-dependent Riccati equation technique to reduce the energy cost of the FFSR. In case the system gets trapped in local minimum, an escape force is elaborately designed to replace artificial potential force based on optimal theory, whose direction vector guides the FFSR to jump out of the local minimum with minimal energy. Finally, simulation results on a planar FFSR demonstrate the effectiveness of the proposed methodology and show improved performance compared with the existing artificial potential field (APF) in terms of escaping local minimum and reducing energy consumption.

Trade-off in the partitioning of recent photosynthate carbon under global change.

There may be trade-offs in the allocation patterns of recent photosynthetic carbon (RPC) allocation in response to environmental changes, with a greater proportion of RPC being directed towards compartments experiencing limited resource availability. Alternatively, the allocation of RPC could shift from sources to sinks as plants processing excess photosynthates. It prompts the question: Does the pattern of RPC allocation vary under global changes? If so, is this variation driven by optimal or by residual C allocation strategies? We conducted a meta-analysis by complicating 273 pairwise observations from 55 articles with 13 C or 14 C pulse or continuous labeling to assess the partitioning of RPC in biomass (leaf, stem, shoot, and root), soil pools (soil organic C, rhizosphere, and microbial biomass C) and CO2 fluxes under elevated CO2 (eCO2 ), warming, drought and nitrogen (N) addition. We propose that the increased allocation of RPC to belowground under sufficient CO2 results from the excretion of excess photosynthates. Warming led to a significant reduction in the percentage of RPC allocated to shoots, alongside an increase in roots allocation, although this was not statistically significant. This pattern is due to the reduced water availability resulting from warming. In conditions of drought, there was a notable increase in the partitioning of RPC to stems (+7.25%) and roots (+36.38%), indicative of a greater investment of RPC in roots for accessing water from deeper soil. Additionally, N addition led to a heightened allocation of RPC in leaves (+10.18%) and shoots (+5.78%), while reducing its partitioning in soil organic C (-8.92%). Contrary to the residual C partitioning observed under eCO2 , the alterations in RPC partitioning in response to warming, drought, and N supplementation are more comprehensively explained through the lens of optimal partitioning theory, showing a trade-off in the partitioning of RPC under global change.

New sufficient conditions for the solvability of a new class of Sylvester-like absolute value matrix equation

In this article, some new sufficient conditions for the unique solvability of a new class of Sylvester-like absolute value matrix equation \(AXB - \vert CXD \vert =F\) are given. This work is distinct from the published work by Li [Journal of Optimization Theory and Application, 195(2), 2022]. Some new conditions were also obtained, which were not covered by Li. We also provided an example in support of our result.