This paper presents an automated method for solving the initial structure of compact, high-zoom-ratio mid-wave infrared (MWIR) zoom lenses. Using differential analysis, the focal length variation process of zoom lense...
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This paper presents an automated method for solving the initial structure of compact, high-zoom-ratio mid-wave infrared (MWIR) zoom lenses. Using differential analysis, the focal length variation process of zoom lenses under paraxial conditions is investigated, and a model for the focal power distribution and relative motion of three movable lens groups is established. The particle swarm optimization (PSO) algorithm is introduced into the zooming process analysis, and a program is developed in MATLAB to solve for the initial structure. This algorithm integrates physical constraints from lens analysis and evaluates candidate solutions based on key design parameters, such as total lens length, zoom ratio, Petzval field curvature, and focal length at tele end. The results demonstrate that the proposed method can efficiently and accurately determine the initial structure of compact MWIR zoom lenses. Using this method, a mid-wave infrared zoom lens with a zoom ratio of 50x, a total length of less than 530 mm, and the ratio of focal length to total length approximately 2:1 was successfully designed. The design validates the effectiveness and practicality of this method in solving the initial structure of zoom lenses that meet complex design requirements.
The urban heat island (UHI) effect significantly impacts building energy consumption, but its effect on energy retrofit potential remains unclear. Here, we propose a data-driven surrogate optimization framework to ass...
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The urban heat island (UHI) effect significantly impacts building energy consumption, but its effect on energy retrofit potential remains unclear. Here, we propose a data-driven surrogate optimization framework to assess the energy-saving potential of improving building envelope thermal performance in 101 public buildings in Shenzhen. The framework integrates batch building energy modeling, ensemble learning surrogate model training, and multi-objective optimization. The potential changes in building loads resulting from different parameter combinations are defined as energy-saving potential. We use the Urban Weather Generator to adjust typical meteorological year data and account for UHI effects in energy modeling. The results indicate that neglecting the UHI effect in Shenzhen overestimates the potential for building energy retrofitting by approximately 25.2 % (office buildings:-8.96 %, commercial buildings: 60.7 %). Among the nine thermal retrofitting parameters, the heat transfer coefficient of windows contributes the most to the energy-saving potential. Considering the UHI effect or not leads to significant differences in retrofitting strategies and optimal retrofitting parameter configurations. These findings underscore the importance of considering the UHI effect and interactions between buildings in energy retrofit modeling and decision-making processes to formulate precise retrofit strategies and schemes.
In the process of author name disambiguation (AND), varying characteristics and noise of different blocks significantly impact disambiguation performance. In this paper, we propose a block-based adaptive hyperparamete...
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In the process of author name disambiguation (AND), varying characteristics and noise of different blocks significantly impact disambiguation performance. In this paper, we propose a block-based adaptive hyperparameter optimization method that assigns optimal hyperparameters to each block without altering the original AND model structure. Based on this, a random forest model is trained using the optimized results to fit the relationship between the block's data features and its optimal hyperparameters, thereby enabling the prediction of hyperparameters for new blocks. Empirical studies on 6 state-of-the-art AND algorithms, 11 public datasets, and a manually labeled dataset of China's information and communication technology (ICT) industry patents demonstrate that the proposed method significantly outperforms the original algorithms across multiple standard performance evaluation metrics (Cluster F1/Pairwise F1, B-Cubed F1, and K metrics). The results of the random forest regression indicate that the selected 16 features effectively predict the optimal hyperparameters. Further analysis reveals a power-law relationship between relative block size and both relative performance and relative optimized performance across all datasets and evaluation metrics, and the relative performance improvement of the adaptive hyperparameter optimization algorithm is particularly significant for smaller blocks. These findings provide theoretical support and practical guidance for the development of AND algorithms.
Anaerobic digestion (AD) is an important technology that can be engaged to produce renewable energy and valuable products from organic waste while reducing the net greenhouse gas emissions. Due to the AD process compl...
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Anaerobic digestion (AD) is an important technology that can be engaged to produce renewable energy and valuable products from organic waste while reducing the net greenhouse gas emissions. Due to the AD process complexity, further development of AD technology goes hand in hand with the advancement of underlying mathematical models and optimization techniques. This paper presents a comprehensive and critical review of current AD process modeling and optimization techniques as well as various aspects of further processing of AD products. The most important mechanistically inspired, kinetic, and phenomenological AD models and the most frequently used deterministic and stochastic methods for AD process optimization are addressed. The foundations, properties, and features of these models and methods are highlighted, discussed, and compared with respect to advantages, disadvantages, and various performance metrics;the models are also ranked with respect to adequately introduced criteria. Since AD process optimization affects heavily the required treatment and utilization of AD products, biogas and digestate utilization in the production of renewable energy and other valuable products is also addressed. Furthermore, special attention is devoted to the challenges and future research needs related to AD modeling and optimization, such are modeling issues related to foaming and microbial activities, AD model parameters calibration, CFD simulation challenges, availability of experimental data, and optimization of the AD process with respect to further biogas and digestate utilizations. As current research results indicate, further progress in these areas could notably improve AD modeling robustness and accuracy as well as AD optimization performance.
Virtual power plant (VPP) with a high percentage of flexibility resources has issues that need to be addressed, such as high source-load volatility and limited scope to participate in multi-market bids. Therefore, thi...
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Virtual power plant (VPP) with a high percentage of flexibility resources has issues that need to be addressed, such as high source-load volatility and limited scope to participate in multi-market bids. Therefore, this paper proposes a VPP standby capacity setting method based on normal distribution framework and Bayesian parameter optimization. Through the marginal revenue and expenditure of standby capacity analysis, this paper constructs a two-stage optimization strategy for VPP trading in multi-market considering double uncertainty, which is solved by the Improved Multi-Objective Squirrel Search Algorithm (IMSSA). Compared to the traditional program, the VPP's participation in the day-ahead spot bidding increased by 5.97% and 2.48%, respectively, total revenue increased by 17.41% and 12.97%, respectively, reliability increased by 0.21%, and overall energy efficiency increased by 10%. Compared to Squirrel Search Algorithm and Particle Swarm optimization Algorithm, IMSSA improves the optimal revenue by 1.03% and 1.91%, and the convergence speed by 24.24% and 38.01%, respectively.
This paper introduces the Enhanced Team-Oriented Swarm optimization (ETOSO) algorithm, a novel refinement of the Team-Oriented Swarm optimization (TOSO) algorithm aimed at addressing the stagnation problem commonly en...
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This paper introduces the Enhanced Team-Oriented Swarm optimization (ETOSO) algorithm, a novel refinement of the Team-Oriented Swarm optimization (TOSO) algorithm aimed at addressing the stagnation problem commonly encountered in nature-inspired optimization approaches. ETOSO enhances TOSO by integrating innovative strategies for exploration and exploitation, resulting in a simplified algorithm that demonstrates superior performance across a broad spectrum of benchmark functions, particularly in high-dimensional search spaces. A comprehensive comparative evaluation and statistical tests against 26 established nature-inspired optimization algorithms (NIOAs) across 15 benchmark functions and dimensions (D = 2, 5, 10, 30, 50, 100, 200) confirm ETOSO's superiority relative to solution accuracy, convergence speed, computational complexity, and consistency.
Integrated and sustainable river basin management requires accurate assessment and optimized allocation of available water resources, considering the impacts of climate and anthropogenic changes. It can benefit from c...
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Integrated and sustainable river basin management requires accurate assessment and optimized allocation of available water resources, considering the impacts of climate and anthropogenic changes. It can benefit from coupled modeling frameworks combining simulation models and optimization algorithms (OAs). This article reviews simulation-based optimization (SbO) techniques developed for integrated water resources management (IWRM) classified into four distinct categories: i) coupled Simulation-only Models (SM-o) and Simulation Models with water allocation skill (SM-wa);ii) coupled Simulation Models with water allocation skill (SM-wa) and OAs;iii) coupled SM-o and OAs, and iv) simultaneous coupling of SM-o, SM-wa, and OAs. These simulation-based optimization frameworks are constructed using various coupling strategies-isolated, loose, tight, and integrated-based on the required level of data exchange and interactions between model components. The first category (SM-o-SM-wa) is predominantly based on applying offline/isolated coupling methods, whereas subsequent categories witness the adoption of loose and tight coupling approaches. The findings of this review underscore the value of SbO frameworks in promoting comprehensive and sustainable management of water resources capable of addressing single-period and multi-period allocations, as well as optimizing reservoir operation policies within a single framework. Additionally, this review can be a valuable resource for researchers, modelers, and policymakers in selecting suitable simulation models, OAs, and coupling methods for effective decision-making in IWRM.
Sorptive bioprocesses are the basis for numerous biotechnological applications such as enzyme immobilization, biosensors, controlled drug delivery, water treatment, and molecular purification. Yet due to the complexit...
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Sorptive bioprocesses are the basis for numerous biotechnological applications such as enzyme immobilization, biosensors, controlled drug delivery, water treatment, and molecular purification. Yet due to the complexity of these processes, their optimization is still time, labor, and cost-intensive. This research presents a flexible self-driving laboratory (SDL) designed for the accelerated development and optimization of solid-phase extraction processes. As a use case, the SDL was used to optimize a DNA purification process using silica magnetic beads. Through the integration of robotics, machine learning, and data-driven experimentation, the SDL demonstrates a highly accelerated process optimization with minimal human intervention. In the multistep purification approach, the system is able to optimize buffer compositions for DNA extraction from complex samples, demonstrating effectiveness in both conventional chaotropic salt-based methods and innovative chaotropic salt-free buffers. The study highlights the SDL's capability to autonomously refine process parameters, achieving significant enhancements in yield and purity of the product. This blueprint for future self-driving optimization of bioprocess parameters showcases the potential of autonomous systems to revolutionize biochemical process development, offering insights into scalable, environmentally sustainable, and cost-effective solutions.
This research introduces and evaluates the Heuristic Particle Elimination optimization algorithm, a novel approach to structural design problems. The elimination strategy in this method distinguishes it from other met...
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This research introduces and evaluates the Heuristic Particle Elimination optimization algorithm, a novel approach to structural design problems. The elimination strategy in this method distinguishes it from other meta-heuristic algorithms. By systematically discarding solutions with higher objective values, the algorithm focuses its search on the most promising regions of the solution space. This targeted approach proves advantageous in discrete optimization problems, where the solution space consists of predefined discrete values. Focusing on optimizing cross-sectional areas of structural elements, the algorithm minimizes structural weight while adhering to constraints on deflections, stresses, and permissible sections. Five benchmark examples, including both planar and spatial truss structures with varying numbers of bars, serve as comprehensive test cases. Through rigorous comparative analyses with established optimization methods, the results highlight the algorithm's efficiency in achieving optimal solutions, emphasizing its convergence speed and solution accuracy. Its adaptability to both discrete and continuous variable scenarios makes it a promising tool for real-world structural design challenges.
作者：
Hou, RuijieYu, YangLi, XiuxianTongji University
Department of Control Science and Engineering College of Electronics and Information Engineering Shanghai201800 China Tongji University
Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai Institute of Intelligent Science and Technology Shanghai201210 China
This article focuses on online composite optimization over multiagent networks. In the distributed setting, each agent has its own local loss function, which consists of a convex, strongly convex or strongly convex an...
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