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Multi-phase constrained multi-objective optimization via heterogeneous transfer

INFORMATION SCIENCES 2025年 700卷

作者： Li, Huiting Jin, Yaochu Cheng, Ran Harbin Univ Sci & Technol Sch Automat Harbin Peoples R China Westlake Univ Sch Engn Hangzhou Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen Peoples R China

Despite the achievements of multi-objective evolutionary algorithms in solving Constrained Multi-Objective optimization Problems (CMOPs), their efficiency deteriorates when dealing with complex constraints. This deficiency is mainly due to the lack of diversity among the generated offspring, resulting from the singularity of the evolutionary operator. To address this issue, we introduce an Adaptive Multi-Phase Constrained Multi-objective optimization Algorithm using Heterogeneous Transfer (AMPHT). By using knowledge from unconstrained Multi-Objective Problems (MOPs) to identify beneficial solutions for CMOPs, AMPHT generates diverse offspring through traditional genetic and difference operators, as well as by leveraging a heterogeneous transfer learning method. To prevent computational resource strain from excessive offspring generation, AMPHT integrates an adaptive multi-phase optimization framework. This evaluates the efficiency of each offspring generation method and dynamically adjusts their implementation frequencies. Our comprehensive testing of AMPHT on sixty-six problems, benchmarked against twelve cutting-edge algorithms, validates its enhanced performance in solving complex CMOPs and handling a wider problem range.

关键词： optimization algorithms

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Sidelobe Level Suppression in Linear and Planar Phased Arrays Using Grey Wolf optimization With Local Search Refinements

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IEEE ACCESS 2025年 13卷 27818-27832页

作者： Adriansyah, Muhammad Athallah Wahdiyat, Aditya Inzani Fitri, Ismi Rosyiana Murad, Noor Asniza Apriono, Catur Univ Indonesia Fac Engn Dept Elect Engn Depok 16424 Indonesia Natl Res & Innovat Agcy Res Ctr Elect Jakarta 10340 Indonesia Univ Teknol Malaysia UTM Fac Elect Engn Adv RF & Microwave Res Grp ARFMRG Johor Baharu 81310 Johor Malaysia

Phased array antennas play a critical role in satellite communication systems, where minimizing peak sidelobe levels (PSLL) is essential to prevent interference and enhance signal quality. This paper presents a novel hybrid optimization approach, grey wolf optimizer with Nelder-Mead local search (GWO-NM), to suppress PSLL effectively in both linear antenna arrays (LAA) and planar antenna arrays (PAA). The proposed method combines the global search capability of grey wolf optimizer (GWO) with the Nelder-Mead local refinement to address the PSLL suppression challenge. The optimization process was applied to both 32-element and 100-element LAA, achieving PSLL reductions to -52.63 dB and -56.39 dB, respectively, and these results are also extended to the corresponding PAA configuration. The results were further validated through full-wave electromagnetic simulations in CST Microwave Studio 2024, considering practical effects such as mutual coupling and material losses. Also, this suppression comes with trade-offs, including a widened first null beamwidth from 18.34 degrees to 19.78 degrees for the 32-element arrays and from 6.47 degrees to 9.71 degrees for the 100-element arrays in both LAA and their corresponding PAA compared to the conventional algorithm, along with a marginal decrease of 0.5-1 dB in directivity observed in electromagnetic simulations. The GWO-NM algorithm outperformed other metaheuristic optimization techniques, including PSO, ACO, CSO, ABCO, and conventional GWO, demonstrating superior sidelobe suppression across different array configurations while maintaining an acceptable trade-off with other key radiation parameters.

关键词： Antenna arrays Linear antenna arrays Metaheuristics Geometry Genetic algorithms Spaceborne radar Phased arrays Radar antennas Antenna radiation patterns Space exploration electromagnetic simulation grey wolf optimizer Nelder-Mead simplex method optimization algorithms phased array antennas sidelobe level suppression

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Enhancing minority data generation through optimization in imbalanced datasets

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KNOWLEDGE AND INFORMATION SYSTEMS 2025年第5期67卷 4523-4547页

作者： Song, Jiuxiang Wang, Chuang Liu, Jizhong Nanchang Univ Sch Adv Mfg Nanchang 330031 Jiangxi Peoples R China Xiangyang Auto Vocat Tech Coll Intelligent Mfg Coll Xiangyang 441000 Hubei Peoples R China

The primary objective of research concerning class imbalance problems revolves around the generation of high-quality data for minority classes. Prior investigations have witnessed various approaches to synthesizing data, resulting in varying data quality. This study introduces a novel oversampling framework, termed the Optimal Oversampling Framework (OOF), which adopts a distinctive perspective. OOF uses optimization algorithms to guide the data generation process, ensuring that new samples are not only similar to the minority class but also exhibit sufficient diversity. Specifically, the method combines initialization and evolutionary strategies to refine the generated samples, while evaluating the similarity of the samples to the minority and majority classes through distance and cosine similarity measures. In addition, OOF prevents premature convergence and ensures that the generated samples maintain uniqueness through diversity judgments and fitness function settings. Finally, OOF selects the best quality samples for oversampling by optimizing the ranking. To demonstrate the effectiveness of OOF, we integrated the Particle Swarm optimization algorithm with OOF and conducted comparative experiments involving nine different oversampling methods across 21 datasets characterized by high class imbalance ratios. The experimental outcomes validate the success of the OOF approach.

关键词： Class imbalance Optimal oversampling framework optimization algorithms Minority class Particle swarm optimization

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Integrated Local Search Technique With Reptile Search Algorithm for Solving Large-Scale Bound Constrained Global optimization Problems

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OPTIMAL CONTROL APPLICATIONS & METHODS 2025年第2期46卷 775-788页

作者： Abu-Hashem, Muhannad A. Shehab, Mohammad Shambour, Mohd Khaled Abualigah, Laith King Abdulaziz Univ Architecture & Planning Fac Dept Geomat Jeddah Saudi Arabia Amman Arab Univ Coll Comp Sci & Informat Amman Jordan Middle East Univ Fac Engn & Design Dept Intelligent Syst Engn Amman Jordan Al Al Bayt Univ Comp Sci Dept Mafraq Jordan Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura Punjab India Univ Tabuk Artificial Intelligence & Sensing Technol AIST Res Tabuk Saudi Arabia Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan Sunway Univ Malaysia Sch Engn & Technol Petaling Jaya Malaysia

The Reptile Search Algorithm (RSA) is a powerful modern optimization technique that effectively solves intricate problems across various fields. Despite its notable success, the local search aspect of RSA requires enhancement to overcome issues such as limited solution variety, a pattern of falling into local optimal traps, and the possibility of early convergence. In response to these challenges, this research introduces an innovative paradigm that melds the robust and time-honoured local search technique, Simulated Annealing (SA), with RSA, christened henceforth as SARSA. This amalgamation aims to tackle the qualities of both strategies, synergistically improving their optimization capabilities. We utilize a broad and thorough assessment system to survey the viability and strength of SARSA. A comprehensive cluster of benchmark issues sourced from the CEC 2019 benchmark suite and an assorted set of real-world challenges drawn from the CEC 2011 store is utilized as the test bed. This fastidiously curated testbed guarantees an intensive examination of SARSA's execution over a wide range of issues and complexities. Our observational discoveries substantiate that SARSA beats the foundational RSA and a few related calculations reported within the existing body of writing, in this manner setting up SARSA as a critical progression in optimization calculations. The prevalent execution illustrated by SARSA highlights its potential for broad application and underscores its utility in handling complex optimization issues viably.

关键词： local search techniques optimization algorithms real-world problems reptile search algorithm simulated annealing

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Compact High-Zoom-Ratio Mid-Wavelength Infrared Zoom Lens Design Based on Particle Swarm optimization

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SENSORS 2025年第2期25卷 467-467页

作者： Liu, Zhenhao Zhang, Jipeng Huang, Yuqi Zhang, Xin Wu, Hongbo Zhang, Jianping Changchun Inst Opt Chinese Acad Sci Fine Mech & Phys Changchun 130033 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China State Key Lab Appl Opt Changchun 130033 Peoples R China Chinese Acad Sci Key Lab Opt Syst Adv Mfg Technol Changchun 130033 Peoples R China

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.

关键词： zoom lens design high zoom ratio particle swarm optimization optimization algorithms

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Adaptive hyperparameter optimization for author name disambiguation

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JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY 2025年第0期

作者： Lu, Shuo Zhou, Yong Xian Univ Architecture & Technol Sch Management 13 Middle SectYanta Rd Xian Shaanxi Peoples R China

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.

关键词： optimization algorithms

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Simulation-based optimization of Water Resource Systems: A Review of Limitations and Challenges

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WATER RESOURCES MANAGEMENT 2025年第2期39卷 579-602页

作者： Tekle, Shewandagn Lemma Bonaccorso, Brunella Naim, Mohamed Univ Messina Dipartimento Ingn Messina Italy Scuola Univ Super Pavia IUSS Pavia I-27100 Pavia Italy

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.

关键词： Water availability Climate change Simulation models Water allocation models optimization algorithms Model coupling

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Self-Driving Lab for Solid-Phase Extraction Process optimization and Application to Nucleic Acid Purification

ADVANCED INTELLIGENT SYSTEMS

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ADVANCED INTELLIGENT SYSTEMS 2025年第1期7卷

作者： Putz, Sebastian Doettling, Jonathan Ballweg, Tim Tschoepe, Andre Biniyaminov, Vitaly Franzreb, Matthias Karlsruhe Inst Technol KIT Inst Funct Interfaces IFG Dept Bioengn & Biosyst D-76344 Eggenstein Leopoldshafen Germany

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.

关键词： automation DNA purification magnetic beads optimization algorithms self-driving labs silica solid-phase extractions

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Dynamic Regret for Distributed Online Composite optimization

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IEEE Transactions on Automatic Control 2025年第5期70卷 3056-3071页

作者： Hou, Ruijie Yu, Yang Li, Xiuxian Tongji 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 and smooth function, and a time-varying nonsmooth regularizer. Two distributed online algorithms are proposed and corresponding dynamic regrets are analyzed. Two proposed algorithms are based on signs of relative states. The first algorithm obtains (Formula presented) dynamic regret bound when each local loss is a general convex composite function, where CT is the path variation. If CT can be estimated in advance for convex or strongly convex local loss with a time-varying nonsmooth regularizer, then dynamic regret bounds are, respectively, in the order of (Formula presented) and o (log T(1 + CT)). The second algorithm is based on the first one, especially for handling the local loss composed of a strongly convex and smooth function with a nonsmooth regularizer, and then obtains o (1 + CT) dynamic regret bound. In the end, numerical results are given to support the theoretical findings. © 1963-2012 IEEE.

关键词： optimization algorithms

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Enhanced Social Group optimization algorithm for Solving optimization Problems

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第1期49卷 151-176页

作者： Naik, Anima Department of CSE Raghu Engineering College Andhra Pradesh Visakhapatnam530003 India

In the last decades, the field of global optimization has experienced significant growth, leading to the development of various deterministic and stochastic algorithms designed to tackle a wide range of optimization problems. One notable member of this family is the Social Group optimization (SGO) algorithm. The Improving Phase and the Acquiring Phase are its two main fundamental phases. The two upgraded versions of SGO with a modified improvement phase are Enhanced Social Group optimization (ESGO) and Enhanced Modified Social Group optimization (EMSGO). The key enhancement in these variants focuses on honing, refining skills and abilities to achieve greater effectiveness. To assess the performance of ESGO and EMSGO, an extensive comparative analysis is conducted, involving twelve algorithms, including recently introduced, potent metaheuristic methods. Since both ESGO and EMSGO are modified algorithms, a comparison is conducted between these two algorithms and six recently introduced improved/hybrid algorithms. Subsequently, twenty-six real-world design problems from the mechanical and chemical engineering areas are addressed by applying both modified methods. The simulation results leave no doubt about the capability of ESGO and EMSGO to consistently achieve optimal solutions. Their robust performance, both in comparative evaluations and real-world applications, underscores their potential in solving challenging optimization tasks. © 2025 Slovene Society Informatika. All rights reserved.

关键词： optimization algorithms

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