Efficient Optimization Research Articles

Estimation of vessel link-level travel time distribution: A directed network-driven approach

Accurate vessel travel time estimation is essential for operational efficiency and route optimization. Despite the prevalent use of the automatic identification system (AIS) in garnering multifaceted real-time data, ambiguities and inaccuracies in travel time predictions persist. It leads to planning uncertainties, inefficient resource allocations, and heightened operational costs in maritime logistics. To addresses these issues, this paper proposed a nuanced method to enhance the precision and reliability of vessel travel time estimations. Firstly, a directed maritime network is constructed by extracting essential information from AIS-based historical vessel trajectories. This lays the foundation for the subsequent analytical processes. Secondly, the non-parametric kernel density estimation (KDE) is applied to this constructed network, enabling the estimation of vessel travel time distributions across various network links. The non-parametric KDE is used in combination with AIS data, which improves the specificity and accuracy of the travel time estimations at the link level. Finally, this paper employs cellular automata (CA) simulations to validate the accuracy of the KDE-based estimations. Comparison between the simulation results and real-world data reveals a high degree of accuracy in the proposed method, confirming its applicability and effectiveness in estimating vessel travel times.

Innovative simplified approach and numerical investigation for flow and heat transfer in internally threaded tubes

Internally threaded tubes exhibit exceptional heat transfer capabilities, holding paramount significance in enhancing energy efficiency, reducing energy consumption, and tackling thermal challenges across aerospace, automotive, and environmental domains. Nonetheless, their intricate structural features, software modelling intricacies, extensive grid requirements, and challenges in maintaining grid quality render numerical simulation investigations a demanding endeavor. This study elucidates the influence mechanisms of thread spacing on the distribution of single-phase and two-phase flows. It delves into the condensation heat transfer of the R32 refrigerant and the single-phase heat transfer of water within internally threaded tubes. At the same Reynolds number, the heat transfer rate can be improved by approximately 19.61% with the optimized internally threaded parameters. When the Reynolds number varies from 18,626 to 51,222, the heat transfer rate per unit length for Tube-5 remains around 2,478.9 W/m, with a variation of less than 5.19%. Furthermore, it presents a simplified numerical simulation approach tailored for internally threaded pipes. This method manipulates wall roughness and regulates wall rotation speed to mimic fluid dynamics phenomena akin to conventional numerical simulation techniques. The findings reveal that the proposed simplified numerical simulation approach yields heat transfer and pressure drop predictions with less than 3% discrepancies compared to traditional numerical simulation predictions. Simultaneously, compared to directly simulating threaded pipes through conventional means, this approach significantly mitigates grid generation complexities, slashing computational time costs by at least 85% and facilitating efficient thermal design and structural optimization processes.

A novel compact xerographic system for 3D printing of fluoropolymer powders onto metal surfaces

Abstract This study introduces a compact xerographic 3D printing system that utilizes precise layer-by-layer dry powder transfer techniques, facilitating the fabrication of 3D objects directly on metal substrates. By leveraging electrostatic force to coat dry fluoroethylene vinyl ether powder onto metallic surfaces, our innovative method significantly broadens the spectrum of printable materials. Through the optimization of electrostatic potentials and powder transfer efficiency, the system successfully demonstrates the ability to produce intricate 3D structures with heights ranging from millimeters to centimeters. This novel approach not only showcases the potential for creating flexible electronic materials with complex 3D geometries directly on the metal substrate but also opens new avenues for diverse material applications within the field of advanced xerographic 3D printing technology.

Dynamic production scheduling and maintenance planning under opportunistic grouping

Maintenance and production scheduling are intertwined activities that should be addressed simultaneously to uphold production systems’ reliability and production efficiency. The digital twin is an emerging technology that advances industrial digitalization, as it embeds a “virtual” image of reality that runs in line with the real system, enabling evaluation, optimization, and prediction of the physical system’ state. On the other hand, deep reinforcement learning (DRL) can provide real-time decision-making by leveraging real-time data from the digital twin such as condition monitoring and production progress data. This research addresses the joint flexible job shop scheduling and maintenance planning problem, considering new job insertions and multi-component machines with economic dependencies among components. The objective is to minimize both the expected total tardiness and maintenance cost, considering opportunistic grouping of maintenance activities on components and breakdown costs associated with failure risk. To achieve this joint decision-making, we develop a hierarchical architecture with two interconnected hierarchies. The upper-level hierarchy determines whether to switch the decision-making process between production scheduling and maintenance planning. Subsequently, the lower-level hierarchy selects an action through the corresponding agent: a multi-head Deep Q-Network (DQN) if the maintenance option is chosen, and a DQN with seven dispatching rules for the production option. The computational experiment results reveal that the proposed scheduling method can learn a high-quality dispatching policy, outperforming the non-hierarchical DRL approach and individual dispatching rules in solution quality, CPLEX in runtime efficiency, and the genetic algorithm and particle swarm optimization in both solution quality and runtime efficiency.

Toward digitally twinning the process of creating machine controller digital twins – A G-code generation scenario

This paper proposes BatchEGO, an extension of Efficient Global Optimization (EGO), in an active learning context for creating and maintaining digital twins (DT). The motivation for this study is to address the challenges of obtaining high-quality yet cost and time effective data for developing accurate DTs of machine tool controllers that are robust to noise and physical system changes. BatchEGO is employed to generate manufacturing recipes (G-codes) recursively and iteratively, based on the spatial constraints of the physical machine, providing G-code based data for DTs. BatchEGO Interm is also proposed that extends BatchEGO by sampling additional intermediate points on the generated trajectory, to improve convergence and accuracy of DTs. To account for the stochastic nature of proposed methods, a total of 70 experiments based around repetitions of 4 correlation functions, 3 acquisition functions, and 4 sampling schemes are used to evaluate the performance of both methods. The results showcase impressive and faster convergence, in terms of sum of squared errors, for BatchEGO Interm with specific settings producing highly accurate and smooth G-codes. For instance, BatchEGO Interm with Matérn 3/2 correlation function and acquisition function of lower confidence bound can reach an error range of 0.0026 within 3-5 iterations. The work demonstrates the novelty and potential of such frameworks for enhancing data quality and quantity for DTs. The importance of G-code generation for DTs is also highlighted, as it can enable better control and optimization of machine tools in various industrial applications. The paper also discusses the different approaches for stitching the sampled points into G-code and compares their accuracy and efficiency.

Analytical Overview of the Methods of Controlling the Parameters of Vibratory Compaction of the Concrete Mixes

Introduction. A historical (retrospective) overview of the evolution and application of the methods of concrete mixture compaction by vibration, as well as an overview of the breakthrough scientific+ advancements in the studied field have been presented. The use of the dry concrete mixes that reduce cement consumption, accelerate the strength gain of concrete, reduce the shrinkage strain and heat emission and increase the strength and durability of products has been proposed. The theoretical and practical issues of vibratory compaction of the concrete mixes, the research on the effect reached by the various vibration modes of the vibrating tables, the issues of choosing an efficient mode of vibration impact during compacting the concrete mixes and optimisation of their parameters have been studied. The vibrating table design, the installation layout of the vibrator’s eccentric weights to obtain a given value of the exciting force and the layout of a vibration module with the directed vibrations have been studied in detail. The criteria of duration of the concrete mixture compaction have been defined, changes of the impact parameters during compacting the concrete mixes have been characterised and technical specifications of the vibrating tables have been defined. In the article considerable attention has been paid to the main parameters of the vibrocompacting machines: frequency, amplitude and acceleration of the working member. Most often, the parameter of intensity is used to assess the efficiency of the vibration impact received by the mixture during the compaction process.Materials and Methods. A mixture that turns into concrete after compaction, setting and hardening was selected for the research. The various methods and parameters of concrete mixture compaction to obtain the high-quality concrete were studied. The impact of the various vibration modes on the process of compaction of concrete mixes was investigated, the criteria for the efficient selection and optimisation of the vibration impact parameters were revealed. The paper presents the criteria for assessing duration of the concrete mixture compaction and describes the changes in the parameters during compaction of the concrete mixes. The technical specifications of the vibrating tables of different types and sizes were also presented. The choice of the vibration compaction mode for the concrete mixture is a complicated task, which includes many parameters.Results. Upon the research, the data on the process of concrete mixture compaction under the impact of vibration forces has been obtained. The process results in the destruction of the structural bonds and weakening the bonds between the particles, which leads to the emergence of a variable stress-strain condition. It has also been found that under such settings, the mineral particles get transferred and a denser packing is formed.Discussion and Conclusion. The resulting denser packing of the concrete mixture particles ensures the undoubted economic advantage due to reduced consumption of a binder without loss of the strength properties of concrete.

Phased optimization of multi-thermal fluid flooding for enhanced oil recovery

Multi-thermal fluid flooding has become a critical technique in heavy oil reservoirs and is widely applied in China. Owing to the complexity of the displacement process and the involvement of multiple injection media, conventional optimization methods are unable to achieve economic and efficient development of heavy oil. In this study, an efficient phased parametric optimization method for multi-thermal fluid flooding was developed using horizontal wells in heavy oil reservoirs. This method utilizes particle swarm optimization (PSO), which targets the net present value for the phased optimization of multi-thermal fluid flooding. A comparison between the phased optimization and non-phased optimization shows that the cumulative oil production increased by 16.74% and the net present value increased by 39.66% with phased optimization. The effectiveness of the phased optimization method was verified using a field model. Optimal injection parameter charts were developed for each phase of the multi-thermal fluid flooding under different reservoir parameter conditions. The charts illustrate that the optimal gas injection rate and chemical mass concentration tend towards zero during the steam flooding initiation phase under varying reservoir parameter conditions. Conversely, all the media injection rates increased during the thermal fluid control and steam breakthrough phases. Additionally, with an increase in the crude oil viscosity, effective formation thickness, and original oil saturation, the optimal injection rate of the various media increases. However, with an increase in the permeability and reservoir depth, the optimal injection rate of the various media decreases.

A mechanics-based data-free Problem Independent Machine Learning (PIML) model for large-scale structural analysis and design optimization

Machine learning (ML) enhanced fast structural analysis and design recently attracted considerable attention. In most related works, however, the generalization ability of the ML model and the massive cost of dataset generation are the two most criticized aspects. This work combines the advantages of the universality of the substructure method and the superior predictive ability of the operator learning architecture. Specifically, using a novel mechanics-based loss function, lightweight neural network mapping from the material distribution inside a substructure and the corresponding continuous multiscale shape function is well-trained without preparing a dataset. In this manner, a problem machine learning model (PIML) that is generally applicable for efficient linear elastic analysis and design optimization of large-scale structures with arbitrary size and various boundary conditions is proposed. Several examples validate the effectiveness of the present work on efficiency improvement and different kinds of optimization problems. This PIML model-based design and optimization framework can be extended to large-scale multiphysics problems.

Particle Swarm Optimization for Efficiently Evolving Deep Convolutional Neural Networks Using an Autoencoder-Based Encoding Strategy

Deep convolutional neural networks (DCNNs) have achieved surpassing success in the field of computer vision, and a number of elaborately designed networks refresh the performance records in benchmark datasets. Recently, evolutionary neural architecture search (ENAS) has become an emerging area, which could employ an evolutionary computation (EC) technique to automatically construct promising network architectures without human intervention. However, existing algorithms still have limitations: most standard EC approaches cannot process flexible-length architecture representations directly, and most fitness evaluation processes suffer from the exhibitive computational cost and the unreliable prediction results. To overcome these shortcomings, we propose an efficient particle swarm optimization (PSO)-based neural architecture search algorithm to search for appropriate dense blocks on image classification tasks, named EAEPSO. EAEPSO addresses the first limitation by designing an autoencoder to encode variable-length network representations as fixed-length latent vectors, which converts the original search space to a latent space that can facilitate the downstream search. Besides, an efficient and effective hierarchical fitness evaluation method is designed to guide the search process to address the second limitation. The experimental results show that EAEPSO is a very competitive ENAS algorithm that achieves an error rate of 2.74% on CIFAR-10 and 16.17% on CIFAR-100, and reduces the computational cost from hundreds or thousands of GPU-days to only 2.2 and 4 GPU-days, respectively. Further analyses investigate the reduced training data’s effect and confirm the effectiveness of both the proposed autoencoder and the proposed hierarchical fitness evaluation method.

Global and local semantic enhancement of samples for cross-modal hashing

Hashing becomes popular in cross-modal retrieval due to its exceptional performance in both search and storage. However, existing cross-modal hashing (CMH) methods may (a) neglect to learn sufficient modal-specific information, and (b) fail to fully exploit sample semantics. To address these issues, we propose a method called Semantic Enhancement of Sample Hashing (SESH). First, SESH employs a global modal-specific learning strategy to draw overall shared information and global modal-specific information by factoring the mapping matrix. Second, SESH introduces manifold learning and a local modal-specific learning strategy to extract additional local modal-specific and modal-shared data under label guidance. Meanwhile, local modal-specific information is integrated with global modal-specific details to add rich modal-specific information. Third, SESH uses discrete maximum similarity and orthogonal constraint transformation to enhance both global and local semantic information, embedding more discriminative information into the Hamming space. Finally, an efficient discrete optimization algorithm is proposed to generate the hash codes directly. Experiments on three datasets demonstrate the superior performance of SESH. The source code will be available at https://github.com/kokorording/SESH.

Tail Bounds on the Runtime of Categorical Compact Genetic Algorithm.

The majority of theoretical analyses of evolutionary algorithms in the discrete domain focus on binary optimization algorithms, even though black-box optimization on the categorical domain has a lot of practical applications. In this paper, we consider a probabilistic model-based algorithm using the family of categorical distributions as its underlying distribution and set the sample size as two. We term this specific algorithm the categorical compact genetic algorithm (ccGA). The ccGA can be considered as an extension of the compact genetic algorithm (cGA), which is an efficient binary optimization algorithm. We theoretically analyze the dependency of the number of possible categories K, the number of dimensions D, and the learning rate η on the runtime. We investigate the tail bound of the runtime on two typical linear functions on the categorical domain: categorical OneMax (COM) and KVAL. We derive that the runtimes on COM and KVAL are O(Dln(DK)/η) and Θ(DlnK/η) with high probability, respectively. Our analysis is a generalization for that of the cGA on the binary domain.

Glutathione Therapy in Diseases: Challenges and Potential Solutions for Therapeutic Advancement.

An endogenous antioxidant, reduced glutathione (GSH), is found at high concentrations in nearly all typical cells. GSH synthesis is a controlled process, and any disruption in the process of GSH synthesis could result in GSH depletion. Cellular oxidative damage results from GSH depletion. Various pathological conditions such as aging, cardiovascular disease (CVD), psychiatric disorders, neurological disorders, liver disorders, and diabetes mellitus are more affected by this stress. There are various reasons for GSH reduction, but replenishing it can help to improve this condition. However, there are challenges in this field. Low bioavailability and poor stability of GSH limit its delivery to tissues, mainly brain tissue. Today, new approaches are used for the optimal amount and efficiency of drugs and alternative substances such as GSH. The use of nano-materials and liposomes are effective methods for improving the treatment effects of GSH. The difficulties of GSH decrease and its connection to the most important associated disorders are reviewed for the first time in this essay. The other major concerns are the molecular mechanisms involved in them; the impact of treatment with replacement GSH; the signaling pathways impacted; and the issues with alternative therapies. The utilization of nano-materials and liposomes as potential new approaches to solving these issues is being considered.

Computational Study of Moth-Eye Structures for Silicon Solar Cells Lights Harvesting Improvement

Abstract Recently Moth-Eye nanostructure is widely used in solar cell light harvesting enhancement, in this computational work, three different designs; rectangular, triangular, and semi-circular structures were introduced as anti-reflect structures placed above a silicon solar cell, anti-reflected nanostructured modeled and optimized for silicon ultra-thin film solar cells using the finite difference time domain method for optical, and Lumerical devise software for electrical properties study. The effect of the geometrical dimension of the three structures was investigated. It is found that light-harvesting and solar cell performance can be enhanced by choosing a suitable structure and dimension for the suggested structure. The optimum efficiency enhancement achieved was by a semi-circular structure with a radius of 200nm from 8.81% to 11.95%.

An Efficient Optimization Model and Tabu Search–Based Global Optimization Approach for the Continuous p-Dispersion Problem

Continuous p-dispersion problems with and without boundary constraints are NP-hard optimization problems with numerous real-world applications, notably in facility location and circle packing, which are widely studied in mathematics and operations research. In this work, we concentrate on general cases with a nonconvex multiply connected region that are rarely studied in the literature due to their intractability and the absence of an efficient optimization model. Using the penalty function approach, we design a unified and almost everywhere differentiable optimization model for these complex problems and propose a tabu search–based global optimization (TSGO) algorithm for solving them. Computational results over a variety of benchmark instances show that the proposed model works very well, allowing popular local optimization methods (e.g., the quasi-Newton methods and the conjugate gradient methods) to reach high-precision solutions due to the differentiability of the model. These results further demonstrate that the proposed TSGO algorithm is very efficient and significantly outperforms several popular global optimization algorithms in the literature, improving the best-known solutions for several existing instances in a short computational time. Experimental analyses are conducted to show the influence of several key ingredients of the algorithm on computational performance. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search & Approximation Algorithms. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72122006, 71821001, and 72471100] Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0089 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0089 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

3D Deployment and Energy Efficiency Optimization Based on DRL for RIS-Assisted Air-to-Ground Communications Networks

3D Deployment and Energy Efficiency Optimization Based on DRL for RIS-Assisted Air-to-Ground Communications Networks

Energy efficiency optimization by multi-scale approach

Energy efficiency optimization by multi-scale approach

Zeolite‐Based Materials for Catalytic Oxidation of Volatile Organic Compounds

AbstractOwing to the structural stability, modifiable acidity, and abundant pores/channels, zeolite‐based materials can act as catalysts or supports for effective conversion of volatile organic compounds (VOCs). In this review, a series of VOC oxidation processes catalyzed by zeolite‐based materials will be introduced, which include thermal oxidation, photocatalytic degradation, plasma‐driven removal, catalytic ozonation, microwave‐assisted degradation, and hybrid systems, where the specific roles, modification strategies, and structure–performance relationships of zeolites are discussed in depth. The following topics will also be covered, including the catalytic mechanisms of VOC oxidation, types of zeolite‐based materials in VOC catalytic oxidation, operation parameters in the processes, and reactor designs for efficiency optimization and cost control.

Torque distribution of distributed drive electric vehicle based on efficiency map of drive system

Abstract In order to optimize the system efficiency of distributed drive electric vehicles, based on the MAP of the efficiency of the drive motor system, the objective function of the optimal efficiency is established, and the problem of the no-load loss of the drive motor is considered. A torque distribution control strategy method considering the no-load loss of the motor is proposed. Then, AVL CRUISE and Simulink are used to establish a joint simulation platform, and a comparative simulation test analysis is carried out under ECE conditions. The simulation results show that the torque distribution strategy considering the loss makes the distributed drive motor work in a relatively high efficiency range. Compared with the torque distribution strategy without considering the loss, the power consumption is reduced by 3.4%, which effectively improves the vehicle economy. The research results can provide a reference for system efficiency optimization of distributed drive electric vehicles.

Rate Splitting Multiple Access: Optimal Beamforming Structure and Efficient Optimization Algorithms

Rate Splitting Multiple Access: Optimal Beamforming Structure and Efficient Optimization Algorithms

Innovative energy solutions: Evaluating reinforcement learning algorithms for battery storage optimization in residential settings

The implementation of BESS (battery energy storage systems) and the efficient optimization of their scheduling are crucial research challenges in effectively managing the intermittency and volatility of solar-PV (photovoltaic) systems. Nevertheless, an examination of the existing body of knowledge uncovers notable deficiencies in the ideal arrangement of energy systems' timetables. Most models primarily concentrate on a single aim, whereas only a few tackle the intricacies of multi-objective scenarios. This study examines homes connected to the power grid equipped with a BESS and a solar PV system. It leverages four distinct reinforcement learning (RL) algorithms, selected for their unique training methodologies, to develop effective scheduling models. The findings demonstrate that the RL model using Trust Region Policy Optimization (TRPO) effectively manages the BESS and PV system despite real-world uncertainties. This case study confirms the suitability and effectiveness of this approach. The TRPO-based RL framework surpasses previous models in decision-making by choosing the most optimal BESS scheduling strategies. The TRPO model exhibited the highest mean self-sufficiency rates compared to the A3C (Asynchronous Advantage Actor-Critic), DDPG (Deep Deterministic Policy Gradient), and TAC (Twin Actor Cretic) models, surpassing them by ∼ 3 %, 0.72 %, and 3.5 %, correspondingly. This results in enhanced autonomy and economic benefits by adapting to dynamic real-world conditions. Consequently, our approach was strategically designed to deliver an optimized outcome. This framework is primarily intended for seamless integration into an automated energy plant environment, facilitating regular electricity trading among multiple buildings. Backed by initiatives like the Renewable Energy Certificate weight, this technology is expected to play a crucial role in maintaining a balance between power generation and consumption. The MILP (Mixed Integer Linear Programming) architecture achieved a self-sufficiency rate of 29.12 %, surpassing the rates of A3C, TRPO, DDPG, and TAC by 2.48 %, 0.64 %, 2 %, and 3.04 %, correspondingly.