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Current strategy of non-model-based bioprocess optimizations with genetic algorithms in bioscience - A systematic review

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Computers in Biology and Medicine 2025年 192卷 110247-110247页

作者： Surma, Robert Wojcieszyńska, Danuta Mulla, Sikandar I. Guzik, Urszula Institute of Biology Biotechnology and Environmental Protection Faculty of Natural Sciences University of Silesia in Katowice Jagiellonska 28 Katowice40-032 Poland Department of Biochemistry School of Allied Health Sciences REVA University Bangalore India

This article describes genetic algorithms (GAs), a widely used group of nature-inspired metaheuristics, and presents examples of their application in model-free optimization of bioprocesses. This approach is mainly used to solve optimization problems expressed through mathematical models. However, there are many situations in which laboratory optimization with GAs can be performed. In many cases, GAs have been reported to be superior to other popular optimization methods. Hence, their use is particularly recommended when multiple variables need to be studied simultaneously, the search space is large, and/or little is known about the interactions between individual factors. Despite their usefulness and simplicity, the number of reported experimental examples of non-model-based optimization using GAs remains limited. Real-world experimental evaluations, as opposed to mathematical fitness functions, are neither classified nor explicitly defined in the literature. The authors propose the term "Reality-Based Genetic algorithms" and express hope for its widespread adoption. There is a significant need for both theoretical and empirical research on the parameter configurations of genetic algorithms for experimental optimization, and the authors anticipate that this gap will be addressed in the future. In the meantime, it is recommended to either use configurations that have been proven successful in similar studies or to experiment with different configurations to generate comparative data for future research. © 2025 Elsevier Ltd

关键词： optimization algorithms

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Riemannian Online Optimistic algorithms with Dynamic Regret

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IEEE Transactions on Automatic Control 2025年

作者： Wang, Xi Yuan, Deming Hong, Yiguang Hu, Zihao Wang, Lei Shi, Guodong University of New South Wales School of Electrical Engineering & Telecommunications SydneyNSW2052 Australia Nanjing University of Science and Technology School of Automation Nanjing210094 China Tongji University Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Hong Kong University of Science and Technology Department of Mathematics Hong Kong Zhejiang University College of Control Science and Engineering Zhejiang 310058 China The University of Sydney Australian Center for Robotics School of Aerospace Mechanical and Mechatronic Engineering NSW2006 Australia

In this paper, we consider Riemannian online convex optimization with dynamic regret, which involves minimizing the cumulative loss difference between a learner's decisions and a sequence of adaptive decisions across dynamic environments over time on Riemannian manifolds. First, we propose Riemannian Online Optimistic Gradient Descent (R-OOGD), which extends the classical optimistic algorithms to Riemannian manifolds, requiring one gradient inquiry per round. We then propose Riemannian Adaptive Online Optimistic Gradient Descent (R-AOOGD) by incorporating the meta-expert framework into R-OOGD. We analyze the dynamic regret of R-OOGD and R-AOOGD in terms of the regularity of the sequence of cost functions and comparators. The dynamic regret bound matches the results in Euclidean space and with a scale adjustment of O(1+κ D2/√1-KD2) related to the sectional curvature lower bound κ, sectional curvature upper bound and feasible set diameter. Finally, we provide numerical experiments to illustrate the performance of the proposed algorithms. © 1963-2012 IEEE.

关键词： optimization algorithms

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Bilevel Evolutionary Multi-objective Algorithm with Multiple Lower-level Search Modes

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IEEE Transactions on Evolutionary Computation 2025年

作者： Yang, Ning Liu, Hai-Lin Guangdong University of Technology School of Automation Ganzhou China Jiangxi University of Science and Technology Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control School of Information Engineering Ganzhou China Guangdong University of Technology Center for Mathematics and Interdisciplinary Sciences School of Mathematics and Statistics Guangzhou China

In bilevel optimization, the upper-level optimization problem (ULOP) requires to be solved under the constraint of the inner lower-level optimization problem (LLOP). However, it is computationally expensive to always consider the constraint caused by the LLOP in a higher priority because heavy evaluation budgets are required for validating the constraint satisfaction. From this aspect, this paper investigates bilevel evolutionary multi-objective optimization with multiple lower-level search modes (BLEMO-MLS). Assisted by self-learning and reinforcement, BLEMO-MLS could adaptively adjust the priority of considering more on the upper-level objective optimization or the constraint caused by the LLOP. In BLEMO-MLS, three lower-level search modes are designed to handle the constraint caused by the LLOP. The former two search modes have higher priorities on the satisfaction of the constraint caused by the LLOP, where lower-level decisions of solutions are optimized by a hybrid lower-level search method, while the last search mode has a higher priority on the upper-level objective optimization with the constraint caused by the LLOP temporarily ignored. Through self-learning and reinforcement, BLEMO-MLS dynamically selects proper lower-level search modes in the bilevel optimization process, aiming to obtain approximate bilevel Pareto-optimal solutions fulfilling the constraint caused by the LLOP as much as possible. Compared with five existing bilevel evolutionary algorithms, BLEMO-MLS could effectively solve bilevel multi-objective optimization problems with function evaluations saved in both levels. © 1997-2012 IEEE.

关键词： optimization algorithms

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Optimal allocation of renewable energy resources in distribution systems using meta-heuristic algorithms

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RESULTS IN ENGINEERING 2025年 25卷

作者： Nassar, Samira M. Saleh, A. A. Eisa, Ayman A. Abdallah, E. M. Nassar, Ibrahim A. Egyptian Atom Energy Author NCRRT Dept Nucl Safety & Radiol Emergencies Cairo Egypt Al Azhar Univ Fac Engn Dept Elect Engn Cairo Egypt

In recent decades, Distributed Generation (DG) has emerged as the most effective solution for Radial Distribution Systems (RDS) to reduce power losses, primarily due to the significant increase in energy consumption, while also improving the voltage profile. This paper discusses the application of two algorithms: the White Shark Optimizer (WSO) and the Exponential Distribution Optimizer (EDO). These algorithms are designed to determine the optimal distribution of Renewable Energy Resources (RERs) for Photovoltaic (PV) systems and Wind Turbine Generators (WTGs) within a distribution network. The main objectives are to minimize power losses and enhance the voltage profile of the distribution system. Simulations were conducted across various case studies, considering multiple scenarios to assess the impact of PV and WTG systems on power losses and the Voltage Deviation Index (VDI). The effectiveness of the proposed algorithms is demonstrated through a comprehensive performance analysis applied to the IEEE 33-bus system. Results indicate that, in the best scenario involving multi-objective functions, the WSO can reduce power losses and VDI by up to 90.7 % and 98.98 %, respectively, while improving the minimum voltage from 0.9131 to 0.9804 p.u. These findings were compared with other techniques, highlighting the superiority and effectiveness of the proposed algorithms. Overall, the results show that these algorithms effectively determine the ideal sizes and placements for PV and WTG units, leading to a significant reduction in active power loss and an improvement in the minimum bus voltage.

关键词： Renewable energy resources Solar photovoltaic Wind turbine generator optimization algorithms

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Research on Automatic Task Evaluation System of English-Assisted Teaching Based on Text Abstract Extraction

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International Journal of High Speed Electronics and Systems 2025年第0期

作者： Ren, Shiming Department of Foreign Languages Teaching Wuxi Institute of Technology Jiangsu Wuxi214121 China

With the continuous improvement and maturity of keyword extraction technology, its application scope continues to expand and has now penetrated into multiple fields. This study innovatively introduces the concept of word similarity and optimizes the TF-IDF algorithm. By comprehensively considering word similarity and inverse frequency, this study has developed a novel TF-IDF optimization algorithm that significantly improves the accuracy and relevance of keyword extraction, thereby more accurately reflecting students' learning status and needs. On this basis, this study further optimized the construction of the sentence matrix and proposed an improved initial point selection strategy to address the problem of traditional K-means algorithm being prone to local optima, thereby improving the global optimality and stability of clustering results. In the stage of text summarization extraction, this study fully considered the potential impact of Chinese writing habits and keywords on sentence importance, and customized and optimized the TextRank algorithm. With the help of machine learning technology, optimization algorithms can gain a deeper understanding of text structure and semantic associations, thereby more accurately identifying and extracting topic sentences that represent the main ideas of the article, and generating higher-quality text summaries. © 2025 World Scientific Publishing Company.

关键词： optimization algorithms

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Tighter Analysis for Decentralized Stochastic Gradient Method: Impact of Data Homogeneity

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IEEE Transactions on Automatic Control 2025年

作者： Li, Qiang Wai, Hoi-To The Chinese University of Hong Kong Dept. of System Engineering & Engineering Management Faculty of Engineering Shatin District Hong Kong Hong Kong

This paper studies the effect of data homogeneity on multi-agent stochastic optimization. We consider the decentralized stochastic gradient (DSGD) algorithm and perform a refined convergence analysis. Our analysis is explicit on the similarity between Hessian matrices of local objective functions which captures the degree of data homogeneity. We illustrate the impact of our analysis through studying the transient time, defined as the minimum number of iterations required for a distributed algorithm to achieve comparable performance as its centralized counterpart. When the local objective functions have similar Hessian, the transient time of DSGD can be as small as for smooth (possibly non-convex) objective functions, for strongly convex objective functions, where is the number of agents and is the spectral gap of graph. These findings provide a theoretical justification for the empirical success of DSGD. Our analysis relies on a novel observation with higher-order Taylor approximation for gradient maps that can be of independent interest. Numerical simulations validate our findings. © 2025 IEEE.

关键词： optimization algorithms

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Convex Partition: A Bayesian Regression Tree for Black-box Optimisation

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Pattern Recognition Letters 2025年

作者： de Paz, Erick G.G. Vaquera-Huerta, Humberto Albores-Velasco, Francisco Javier Bauer-Mengelberg, John R. Romero-Padilla, Juan Manuel Colegio de Postgraduados Carretera México-Texcoco Km. 36.5 Texcoco56230 Mexico Universidad Autónoma de Tlaxcala Calzada Apizaquito S/N Tlaxcala Apizaco90341 Mexico

Surrogate-based optimisation is a notable approach for problems, where evaluating the objective function is expensive. These methods construct a model of the objective function to guide the search for optimal solutions. A key challenge lies in efficiently exploring the search space to identify subsets that contain extreme values of the function. This paper introduces Convex Partition (CP), an algorithm based on Classification and Regression Trees (CARTs) and enhanced by a novel Bayesian criterion for selecting partitions during tree construction. This criterion maximises the probability of sampling extreme function values. CP is evaluated on a suite of benchmark functions in dimensions 30, 50 and 100, comparing its performance against black-box optimisation algorithms and existing tree-based surrogates methods such as: Genetic algorithms (GAs), Differential Evolution (DE), Treed Gaussian Processes (TGPs), Bayesian Treed Constants (BTC) and others. Experimental results show that CP achieves a competitive and robust performance. © 2025

关键词： optimization algorithms

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Synthesis of nonuniform linear antenna arrays based on subarray division

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Journal of Electromagnetic Waves and Applications 2025年

作者： Guo, Hua Ren, Chenxin Xiao, Yang Song, Peng Zhang, Lijian School of Electronics and Information Xi’an Polytechnic University Xi’an China

A novel hybrid optimization algorithm is proposed and applied to the subarray partitioning of linear non-uniform antenna arrays. The positions and excitations of the array elements are optimized using Invasive Weed optimization (IWO) and Convex optimization (CO), respectively. The proposed hybrid optimization algorithm can combine the capability of IWO to solve complex optimization problems and the superior global convergence properties of CO. To further reduce the design complexity and cost, the K-means clustering algorithm is applied to fulfill the subarray partitioning of the antenna array. A newly selection method of the initial cluster centers is introduced. To evaluate the effectiveness and superiority of the proposed algorithm, radiation patterns are synthesized under both contiguous and non-contiguous subarray partitioning conditions. Compared with relevant optimization approaches, the proposed hybrid optimization algorithm can effectively suppresses the peak sidelobe level (PSLL). © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： optimization algorithms

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A new smart charging electric vehicle and optimal DG placement in active distribution networks with optimal operation of batteries

RESULTS IN ENGINEERING

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RESULTS IN ENGINEERING 2025年 25卷

作者： Alhasnawi, Bilal Naji Zanker, Marek Bures, Vladimir Al Furat Al Awsat Tech Univ Petr & Energy Engn Tech Coll Dept Fuel & Energy Tech Engn Kufa 66001 Iran Univ Hradec Kralove Fac Informat & Management Hradec Kralove 50003 Czech Republic

The idea of Distribution Networks (DNs) is being developed to automate networks and better integrate renewable energy sources. To do this, the DNs integrate energy storage systems with Distributed Generating Units (DGs). This research report attempts to accomplish too many goals at once. In order to reduce MGs' reliance on the main grid, this study first proposes a smart charging method for PHEVs that maximizes the utilization of RERs and DERs while minimizing the amount of energy taken from the main grid. Second, the issue of how to best operate lithium-ion batteries to raise the technical, financial, and environmental indices of both independent and gridconnected distribution networks is addressed in this work. Thirdly, this paper proposes an optimization technique based on the Mountain Gazelle Optimizer (MGO), Improved Beluga Whale optimization (IBWO), and Arithmetic optimization Algorithm (AOA) for determining the optimal DGs in radial distribution systems. The effectiveness of the suggested framework is tested on IEEE 33-bus and IEEE 85-bus systems, and the findings demonstrate that, in spite of the complexity that arises from changing situations, the model offers an effective restoration solution. The proposed method finds reductions of about 6.83 % in power losses using AOA, reductions of about 17.92 % in power losses using IBWO, reductions of about 22.69 % in power losses and reductions of about 25.43 % in CO2 emissions using MGO, when compared to the benchmark case in the IEEE 33bus network. whereas the proposed method finds reductions of about 1.31 % in power losses using AOA, reductions of about 15.85 % in power losses using IBWO, reductions of about 19.48 % in power losses and reductions of about 23.27 % in CO2 emissions using MGO, when compared to the benchmark case in the IEEE 85bus network.

关键词： IEEE 85-bus distributed network IEEE 33-bus distribution network Batteries Electric vehicles optimization algorithms

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A GROUP-THEORETIC FRAMEWORK FOR MACHINE LEARNING IN HYPERBOLIC SPACES

arXiv

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arXiv 2025年

作者： Jaćimović, Vladimir Faculty of Natural Sciences and Mathematics University of Montenegro Cetinjski put bb. Podgorica81000 Montenegro

Embedding the data in hyperbolic spaces can preserve complex relationships in very few dimensions, thus enabling compact models and improving efficiency of machine learning (ML) algorithms. The underlying idea is that hyperbolic representations can prevent the loss of important structural information for certain ubiquitous types of data. However, further advances in hyperbolic ML require more principled mathematical approaches and adequate geometric methods. The present study aims at enhancing mathematical foundations of hyperbolic ML by combining group-theoretic and conformal-geometric arguments with optimization and statistical techniques. Precisely, we introduce the notion of the mean (barycenter) and the novel family of probability distributions on hyperbolic balls. We further propose efficient optimization algorithms for computation of the barycenter and for maximum likelihood estimation. One can build upon basic concepts presented here in order to design more demanding algorithms and implement hyperbolic deep learning *** Codes 51-08, 68530 © 2025, CC BY.

关键词： optimization algorithms

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