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Information engines harness measurement and feedback to convert energy into useful work. In this study, we investigate the fundamental trade-offs between ergotropic output power, thermodynamic efficiency and information-to-work conversion efficiency in such engines, explicitly accounting for the finite time required for measurement. As a model engine, we consider a two-level quantum system from which work is extracted via a temporarily coupled quantum harmonic oscillator that serves as the measurement device. This quantum device is subsequently read out by a classical apparatus. We compute trade-offs for the performance of the information engine using Pareto optimisation, which has recently been successfully used to optimise performance in engineering and biological physics. Our results offer design principles for future experimental implementations of information engines, such as in nano-mechanical systems and circuit quantum electrodynamics (QED) platforms.

Artificial neural networks are successfully used to solve a wide variety of scientific and technical problems. The purpose of the study is to increase the efficiency of distributed solutions for problems involving structural-parametric synthesis of neural network models of complex systems based on GRID (geographically disperse computing resources) technology through the integrated application of the apparatus of evolutionary optimization and queuing theory. During the course of the research, the following was obtained: (i) New mathematical models for assessing the performance and reliability of GRID systems; (ii) A new multi-criteria optimization model for designing GRID systems to solve high-resource computing problems; and (iii) A new decision support system for the design of GRID systems using a multi-criteria genetic algorithm. Fonseca and Fleming’s genetic algorithm with a dynamic penalty function was used as a method for solving the stated multi-constrained optimization problem. The developed program system was used to solve the problem of choosing an effective structure of a centralized GRID system that was configured to solve the problem of structural-parametric synthesis of neural network models. To test the proposed approach, a Pareto-optimal configuration of the GRID system was built with the following characteristics: average performance–103.483 GFLOPS, cost–500 rubles per day, availability rate–99.92%, and minimum performance–51 GFLOPS.

This study presents a Multi-layer Quasi-Zero-Stiffness (ML-QZS) vibration isolation platform for variable loads in large-amplitude and low-frequency dynamic environments. In one isolation mount of the proposed ML-QZS isolation platform, Multi-layer permanent magnets are constructed to generate discontinuous Multi-layer negative-stiffness regions. The first design criterion is to achieve the low-frequency and wide-amplitude vibration isolation range for different loads. The second design criterion is carried out for the dynamic performances of transient and steady states. Since both structural design and damping determine vibration transient time and the displacement transmissibility, which often exhibit contradictions depending on system parameters, a bi-objective Pareto optimization criterion is proposed to balance the vibration transients between different layers while ensuring significant isolation effectiveness in one layer. Finally, the relevant experimental prototype is constructed, and the results verify the design principle of Multi-layer double magnetic ring construction and optimization criterions for structural parameters and damping coefficients. This study provides an advanced nonlinear isolation platform with a wide QZS range for different loads, and the optimization method to coordinate the vibration performances, which provides important theoretical and experimental guidance for the design and realization of isolation platforms in practical engineering applications for large-amplitude and low-frequency dynamic environments.

Polymers have a wide range of applications in materials science, chemistry, and biomedical domains. Conventional design methods for polymers are mostly event-oriented, directed by intuition, experience, and abstract insights. Nevertheless, they have been effectively utilized to determine several essential materials; these techniques are facing important challenges owing to the great requirement of original materials and the huge design area of organic polymers and molecules. Enhanced and inverse materials design is the best solution to these challenges. With developments in high-performing calculations, artificial intelligence (AI) (particularly Deep learning (DL) and Machine learning (ML))-aided materials design is developing as a promising tool to show development in various domains of materials science and engineering. Several ML and DL methods are established to perform well for polymer classification and detection presently. In this paper, we design and develop a Simplified Molecular Input Line Entry System Based Polymer Property Detection and Classification Using Pareto Optimization Algorithm (SMILES-PPDCPOA) model. This study presents a novel deep learning framework tailored for polymer property classification using SMILES input. By integrating a one-dimensional convolutional neural network (1DCNN) with a gated recurrent unit (GRU) and optimizing the model via Pareto Optimization, the SMILES-PPDCPOA model demonstrates superior classification accuracy and generalization. Unlike existing methods, our model is designed to capture both local substructures and long-range chemical dependencies, offering a scalable and domain-specific solution for polymer informatics. Furthermore, the proposed SMILES-PPDCPOA model executes a one-dimensional convolutional neural network and gated recurrent unit (1DCNN-GRU) technique for the classification process. Finally, the Pareto optimization algorithm (POA) adjusts the hyperparameter values of the 1DCNN-GRU algorithm optimally and results in greater classification performance. Results on a benchmark dataset show that SMILES-PPDCPOA achieves an average classification accuracy of 98.66% (70% Training, 30% Testing) across eight polymer property classes, with high precision and recall metrics. Additionally, it demonstrates superior computational efficiency, completing tasks in 4.97 s, outperforming other established methods such as GCN-LR and ECFP-NN. The experimental validation highlights the potential of SMILES-PPDCPOA in polymer property classification, making it a promising approach for materials science and engineering. The simulation result highlighted the improvement of the SMILES-PPDCPOA system when compared to other existing techniques.

In Iran, the intensity of energy consumption in the building sector is almost 3 times the world average, and due to the consumption of fossil fuels as the main source of energy in this sector, as well as the lack of optimal design of buildings, it has led to excessive release of toxic gases into the environment. This research develops an efficient approach for the simulation-oriented Pareto optimization (SOPO) of building energy efficiency to assist engineers in optimal building design in early design phases. To this end, EnergyPlus, as one of the most powerful and well-known whole-building simulation programs, is combined with the Multi-objective Ant Colony Optimization (MOACO) algorithm through the JAVA programming language. As a result, the capabilities of JAVA programming are added to EnergyPlus without the use of other plugins and third parties. To evaluate the effectiveness of the developed method, it was performed on a residential building located in the hot and semi-arid region of Iran. To obtain the optimum configuration of the building under investigation, the building rotation, window-to-wall ratio, tilt angle of shading device, depth of shading device, color of the external walls, area of solar collector, tilt angle of solar collector, rotation of solar collector, cooling and heating setpoints of heating, ventilation, and air conditioning (HVAC) system are chosen as decision variables. Further, the building energy consumption (BEC), solar collector efficiency (SCE), and predicted percentage of dissatisfied (PPD) index as a measure of the occupants’ thermal comfort level are chosen as the objective functions. The single-objective optimization (SO) and Pareto optimization (PO) are performed. The obtained results are compared to the initial values of the basic model. The optimization results depict that the PO provides optimal solutions more reliable than those obtained by the SOs, owing to the lower value of the deviation index. Moreover, the optimal solutions extracted through the PO are depicted in the form of Pareto fronts. Eventually, the Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP) technique as one of the well-known multi-criteria decision-making (MCDM) methods is utilized to adopt the optimum building configuration from the set of Pareto optimal solutions. Further, the results of PO show that although BEC increases from 136 GJ to 140 GJ, PPD significantly decreases from 26% to 8% and SCE significantly increases from 16% to 25%. The introduced SOPO method suggests an effective and practical approach to obtain optimal solutions during the building design phase and provides an opportunity for building engineers to have a better picture of the range of options for decision-making. In addition, the method presented in this study can be applied to different types of buildings in different climates.

To improve the machining quality of free-form surfaces, machining deformation and chatter should be considered in 5-axis ball-end milling. In this paper, a Pareto multi-objective optimization method for cutter orientation is presented. Firstly, the cutting force is calculated by the differential element method. To improve the computational efficiency of cutting force, the proposed cutting force model considers the influence of surface curvature on the cutter workpiece engagement (CWE) region and gives an analytical calculation method. Then, the deformation and chatter of 5-axis ball-end milling are studied. The static deflection corresponding to different cutter orientations is calculated based on the large-scale sparse matrix inversion algorithm. The chatter-free stability region is calculated based on the 3-dimensional semi-discretization method (SDM). It was found experimentally that the smaller the eigenvalue of the SDM transition matrix is, the more stable the machining is and the better the surface quality is. On this basis, the minimum eigenvalue is proposed as optimization criteria for chatter. Finally, a non-dominated sorting genetic algorithm (NSGA-II) is used to obtain the Pareto optimal region of the cutter orientations. The optimization model takes the minimum value of static deflection and stability eigenvalue as optimization objectives. For each key cutter location point, the feasible region of the cutter orientations with high precision and surface quality is obtained based on NSGA-II. Furthermore, based on the optimization results at the key cutter location points, Dijkstra algorithm and quaternion interpolation algorithm were used to optimize the cutter orientations of the whole free-form surface, which ensured the kinematics performance of the 5-axis machine. The experimental results are in good agreement with the simulation results, which shows that the theoretical model is effective. The results provide a theoretical basis for the comprehensive optimization of machining deformation and chatter of thin-walled parts with the free-form surface.

This paper explores related aspects to post-Pareto analysis arising from the multi-criteria optimization problem. It consists of two main parts. In the first one, we give first-order necessary optimality conditions for a semi-vectorial bi-level optimization problem: the upper level is a scalar optimization problem to be solved by the leader, and the lower level is a multi-objective optimization problem to be solved by several followers acting in a cooperative way (greatest coalition multi-players game). For the lower level, we deal with weakly or properly Pareto (efficient) solutions and we consider the so-called optimistic problem, i.e. when followers choose amongst Pareto solutions one which is the most favourable for the leader. In order to handle real-life applications, in the second part of the paper, we consider the case where each follower objective is expressed in a quadratic form. In this setting, we give explicit first-order necessary optimality conditions. Finally, some computational results are given to illustrate the paper.

This paper brings forward an infeasible proximal bundle method designed for nonsmooth convex multiobjective optimization problems that involve constraints. The method is flexible as it does not require initial feasible points. It directly handles constrained multiobjective functions without scalarization. An aggregation technique is introduced to avoid excessive elements and accelerate convergence. Under reasonable assumptions, the method converges to a global Pareto optimal solution.

Exergoeconomic assessment of an energy conversion system based on energy-exergy analysis and appropriate economic principles, is essential to identify the costs of the inefficiencies both for the whole integrated system and for individual energy components. The current study contributes to an exergoeconomic analysis focusing on the steady-state performance of a biomass-fed combined heat and power (CHP) system including a two-stage auto-thermal biomass gasifier, a direct internal reforming planar solid oxide fuel cell (DIR-PSOFC) and a micro-gas turbine (mGT). A one-dimensional model of the DIR-PSOFC is used to investigate the temperature gradient within the solid structure of the fuel cell under different operating conditions. In order to assess the effect of the main system input parameters on the performance of the cogeneration system, a comprehensive parametric analysis is carried out. The results show that the highest rate of exergy destruction takes place in the gasifier with an amount of 39.23%, followed by the afterburner and the SOFC due to the highly irreversible nature of the process of these components. The system input exergy supplied by biomass is 525.7 kW, of which 53.2% is wasted in the system components and the exergy efficiency of the total CHP system is determined to be 49.72%. Furthermore, the results indicate that the highest exergy destruction cost rate is related to the afterburner with 2.39 ($⁄h). Based on the results of the sensitivity analysis, the trends of the performance parameters demonstrate some conflicts with the variation of the operating parameters, which implies the necessity of an optimization procedure. In all the operating conditions considered, the temperature difference along the cell length is kept below the maximum allowable temperature gradient, which is 150 K. Two-step multi-objective optimization has been conducted by use of non-dominated sorting genetic algorithm technique. Significant and newsworthy relationships between the optimal operating parameters and the considered design variables have been unveiled using the Pareto-based multi-objective optimization procedure.

Resetting, in which a system is regularly returned to a given state after a fixed or random duration, has become a useful strategy to optimize the search performance of a system. While earlier theoretical frameworks focused on instantaneous resetting, wherein the system is directly teleported to a given state, there is a growing interest in physical resetting mechanisms that involve a finite return time. However employing such a mechanism involves cost and the effect of this cost on the search time remains largely unexplored. Yet answering this is important in order to design cost-efficient resetting strategies. Motivated from this, we present a thermodynamic analysis of a diffusing particle whose position is intermittently reset to a specific site by employing a stochastic return protocol with external confining trap. We show for a family of potentials U R ( x ) ∼ | x | m with m  > 0, it is possible to find optimal potential shape that minimises the expected first-passage time for a given value of the thermodynamic cost, i.e. mean work. By varying this value, we then obtain the Pareto optimal front, and demonstrate a trade-off relation between the first-passage time and the work done.