Jie Liu, Jie Liu, Jiayu Chen et al. · International Journal of Numerical Modelling Electronic Networks Devices and Fields · 2024
Abstract This study presents a novel approach for optimizing the parameters of monolithic microwave integrated circuit (MMIC) functional units using machine‐learning techniques and multi‐objective optimization algorithms. We utilize advanced machine‐learning methods, including random forest, artificial neural networks (ANNs), and recurrent neural networks (RNNs), to construct highly accurate models that predict the performance of these units. These models are subsequently integrated with a multi‐objective optimization algorithm, specifically the multi‐objective particle swarm optimization (MOPSO), to generate inverse design solutions for both the geometric designs of the units and the fabrication parameters of the heterogeneous integration process. Our approach, which has been validated through chip fabrication and testing, has demonstrated its robustness as a tool for achieving optimal MMIC designs. It not only reduces the design time but also enhances the manufacturability of MMICs, thereby opening new avenues in microwave and RF circuit design.
Cited by 1
Changyu Lu, Jianmin Yang, Bernt J. Leira et al. · Journal of Marine Science and Engineering · 2023
Three-dimensional path planning is instrumental in path decision making and obstacle avoidance for deep-sea mining vehicles (DSMV). However, conventional particle swarm algorithms have been prone to trapping in local optima and have slow convergence rates when applied to underwater robot path planning. In order to secure a safe and economical three-dimensional path for the DSMV from the mining area to the storage base in connection with innovative mining system, this paper proposes a multi-objective optimization algorithm based on improved particle swarm optimization (IPSO) path planning. Firstly, we construct an unstructured seabed mining area terrain model with hazardous obstacles. Consequently, by considering optimization objectives such as the path length, terrain undulation, comprehensive energy consumption, and crawler slippage rate, we convert the path planning problem into a multi-objective optimization problem, constructing a multi-objective optimization mathematical model. Following that, we propose an IPSO algorithm to tackle the multi-objective non-linear optimization problem, which enables global optimization for DSMV path planning. Finally, we conduct a comprehensive set of experiments using the MATLAB simulation platform and compare the proposed method with existing advanced methods. Experimental results indicate that the path planned by the IPSO exhibits superior performance in terms of path length, terrain undulation, energy consumption, and safety.
Cited by 15Open access
Yang Ding · Architecture and the Built Environment · 2023
There are an increasing number of optimal-design paradigms used in architectural design nowadays. In these paradigms, a design task is formulated, or partially formulated, as an optimization problem. Multi-Disciplinary Optimization and Multi-Objective Optimization, as two important optimal-design paradigms, have shown their great potential in improving the performances of complex buildings in recent decades. Nevertheless, current paradigms for ill‑defined conceptual architectural design still lack ways to ensure the achievement of a reliable optimization problem, which hinders reliable design solutions despite the use of advanced optimization algorithms. To address this problem, it is necessary to shift the focus from Optimization Problem Solving to Optimization Problem Formulation. This research particularly focuses on knowledge‑supported, dynamic and interactive Optimization Problem Re-Formulation in order to construct a new Multi‑Objective and Multi-Disciplinary Optimization (MOMDO) method suitable for use in ill‑defined conceptual architectural design. The proposed method consists of two subtype methods: Non‑dynamic, Interactive Re-formulation method (Subtype-I) and Dynamic, Interactive Re‑formulation method (Subtype-II), which can be used to explore design space in a convergent and divergent manner respectively. To support the re-formulation, various kinds of information and knowledge need to be extracted by utilizing different computational techniques, such as advanced sampling algorithms, Self-Organizing Map, Hierarchical Clustering, Smoothing Spline Analysis of Variance, Two-Level Variable Structure and modular programming. Moreover, a software workflow that can provide these computational techniques is developed; it integrates McNeel’s Grasshopper, ESTECO's modeFRONTIER and simulation software tools Daysim, EnergyPlus and Karamba3D. With the support of this software workflow, the proposed method is demonstrated via two case studies concerning the conceptual design of indoor sports halls.
Cited by 0Open access
S. Dayana Priyadharshini, M. Arvindhan · 2026
The escalating global demand for renewable energy necessitates advanced optimization strategies for solar photovoltaic (PV) plant efficiency to reduce levelized cost of energy (LCOE) and maximize investment returns. This paper presents a comprehensive review of state-of-the-art optimization methodologies for solar PV systems, integrating advanced module technologies, intelligent control systems, and multi-objective optimization frameworks. The study evaluates eight primary optimization strategies: advanced PV modules including bifacial designs achieving up to 40% energy gain in high-albedo environments and TOPC on modules with commercial efficiencies of 23-24%; dynamic tracking systems where single-axis trackers deliver 15-25% annual yield improvements, extending to 40% when combined with bifacial modules; thermal management systems utilizing radiative cooling technologies reducing module temperatures by 13°C; power electronics optimization employing particle swarm optimization (PSO)-based maximum power point tracking (MPPT) algorithms achieving 10% efficiency gains under partial shading conditions; artificial intelligence frameworks incorporating machine learning-based forecasting that improves solar irradiance prediction accuracy by 18% compared to traditional statistical methods; hybrid renewable systems demonstrating 12% energy cost reduction through optimized dispatch strategies; dual-use land approaches including agrivoltaics and photovoltaics systems yielding 5-15% efficiency improvements; and lifecycle sustainability optimization through advanced materials and recycling frameworks. The methodology combines real-world industrial deployments, machine learning analytics, simulation studies, and multi-objective evolutionary algorithms. Results demonstrate that integrated optimization approaches can achieve cumulative efficiency improvements exceeding 50% compared to conventional fixed-tilt systems. The findings provide quantitative benchmarks and practical implementation guidelines for utility-scale solar PV installations, contributing to the advancement of renewable energy technologies and sustainable energy transition strategies.
Cited by 0
Bahman Shojaei, Heidar Dashti Naserabadi, Mohammad Javad Taheri Amiri · Qubahan Academic Journal · 2024
Effective allocation of human resources to project activities is crucial in optimizing project schedules and resource utilization. This paper addresses the challenge of competency-based workforce allocation in construction project scheduling by integrating multi-criteria decision-making with meta-heuristic optimization. A three-objective mathematical planning model aimed at minimizing project completion time, reducing implementation costs, and enhancing workforce competency is proposed. By leveraging expert opinions and multi-criteria decision-making techniques the relevant competency criteria are identified and prioritized. Our approach involves solving a sample problem precisely using GAMS software and developing two meta-heuristic algorithms—Non-Dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO). The algorithms' parameters were optimized using Taguchi design techniques to ensure robust performance. The effectiveness of our proposed methods is validated by comparing their results with exact solutions and conducting extensive tests on large-scale problems. The results demonstrate that both meta-heuristic algorithms effectively address the competency-based allocation challenge, with NSGA-II showing superior performance in achieving optimal solutions. This study highlights the potential of integrating competency-based approaches with advanced optimization techniques to enhance project management in construction.
Cited by 4Open access
Gerardo Ibarra-Vázquez, Hugo Terashima‐Marín, Carlos A. Coello Coello · 2024
In recent years, evolutionary computation has signif-icantly advanced in processes related to machine learning. How-ever, the reciprocal integration of machine learning techniques into evolutionary computation remains relatively unexplored. Machine learning can substantially enhance the understanding of processes within Multi-Objective Evolutionary Algorithms (MOEAs) by harnessing its proficiency in identifying patterns and employing data-driven approaches. Existing studies lack a comprehensive understanding of the intricate interaction between machine learning models and evolutionary algorithms, necessi-tating prioritized investigation to ensure the efficacy, reliabil-ity, and compatibility of integrated models within optimization frameworks. This paper addresses this gap by examining the behavior of using Generative Artificial Intelligence (AI) models as a population variation operator in Multi-objective Optimization Problems. Our experimental results reveal that Generative AI, particularly Distributional Adversarial Networks (DANs), sur-passes the performance of a traditional Generative Adversarial Network. Furthermore, DANs improve the population by gen-erating novel non-dominated solutions and augmenting overall performance and diversity. This study reveals the potential of the integration of Generative AI in evolutionary computation, presenting a pathway for advancements in addressing common challenges within multi-objective optimization problems.
Cited by 2
António Gaspar‐Cunha, João Melo, Tomás Marques et al. · Preprints.org · 2025
Plastic injection molding is a fundamental manufacturing process utilized in diverse areas, producing approximately 30% of global plastic products. This review examines the optimization methodologies in injection molding, emphasizing the integration of advanced modeling, surrogate models, and multi-objective optimization techniques to enhance efficiency, quality, and sustainability. Key phases such as plasticizing, filling, packing, cooling, and ejection are analyzed, each presenting unique optimization challenges. The review highlights the significance of cooling, which constitutes 50-80% of the cycle time, and explores innovative strategies like conformal cooling channels (CCC) to improve uniformity and reduce defects. Various computational tools, including Moldex3D and Autodesk Moldflow, are discussed due to their role in process simulation and optimization. Additionally, optimization algorithms such as evolutionary algorithms, simulated annealing, and multi-objective optimization methods are explored. Integration of surrogate models like Kriging, response surface methodology, and artificial neural networks has shown promise in addressing computational cost challenges. Future directions emphasize the need for adaptive machine learning and artificial intelligence techniques to optimize molds in real time, offering more innovative and sustainable manufacturing solutions. This review is a comprehensive guide for researchers and practitioners, bridging theoretical advancements with practical implementation in injection molding optimization.
Cited by 8Open access
Rounak Saha Niloy, Hemant Kumar Singh, Tapabrata Ray · UNSWorks (University of New South Wales, Sydney, Australia) · 2023
In the context of design, multi-concept optimization (MCO) refers to the task of concurrently identifying the best concept and the corresponding variable values to optimize certain objective(s). Despite its relevance in various practical domains such as engineering, transport, and product design, there have been limited studies on developing computationally efficient algorithms specialized for MCO problems. One of the contributing factors towards this gap is the lack of benchmark problems for MCO that offer diverse challenges for the systematic evaluation and development of advanced algorithms. In this paper, we conduct a brief review of some existing multi-objective test problems in the domain and discuss some of their shortcomings. The key aim is to highlight the need for the development of a more extensive set of benchmark problems that are flexible and tunable in terms of the challenges posed to the solution methodologies. In turn, we hope that this will encourage the development of more advanced algorithms to solve practical MCO problems in the future.
Cited by 4Open access
Namvar, Nima · AMS Degree Thesis (University of Bologna) · 2023
Abstract \nThis thesis investigates the integration of multi-objective optimization, space syntax, and graph analysis in the context of space planning for single-unit buildings. Focused on demonstrating the effectiveness of advanced techniques in generating diverse design options, the study emphasizes optimization based on specific criteria. Building regulations, client needs, and architectural considerations are central to achieving optimal solutions. \nThe research methodology involves a comprehensive exploration of design possibilities that adeptly balances conflicting elements. Utilizing space syntax and graph analysis, the study visualizes relationships between spaces and movements within the building. The integration of sophisticated plugins, Termite Nest and Wallacei, within the Grasshopper platform enhances the analysis. Termite Nest employs space syntax and graph analysis to generate design options, while Wallacei leverages multi-objective optimization, specifically through genetic algorithms, to harmonize conflicting design considerations. \nThis study contributes insights into the practical application of these advanced techniques in space planning, demonstrating their capacity to yield optimized design solutions aligned with specific criteria.
Cited by 0
SUN Liangxu, LI Linlin, LIU Guoli · SHILAP Revista de lepidopterología · 2025
In order to solve the problems of poor performance and low universality in solving MOPs with unconventional PF based on decomposed multi-objective evolutionary algorithms,a new Multi-Objective Evolution Algorithm based on Decomposition and Sub-problem Effectiveness Guidance(MOEA/D-SEG) is proposed.The algorithm expands the sub-problem structure and describes the behaviour of weight vectors in the evolutionary process.The weight vector adjustment is realized by fission “efficient” sub-problems,so that the algorithm can better adapt to the multi-objective optimization problems with different characte-ristics,ensure the convergence and diversity of the solution set,and improve the ability of the algorithm to solve various complex multi-objective optimization problems.Through a series of experiments,the effectiveness of the proposed algorithm in different feature testing problems is proved,and the superiority of the proposed algorithm is proved by comparative analysis with other advanced algorithms.The application of the proposed algorithm in steel-making and continuous casting scheduling problem further verifies the feasibility.
Cited by 0Open access
Guangzhou Xiao, Yuting Ma, Yunpeng Li · Robotica · 2025
Abstract This paper addresses the issue of energy-efficient trajectory optimization for planetary surface manipulators under kinematic and dynamic constraints. To mitigate the inefficiency of existing algorithms, an adaptive boundary adjustment strategy for the multi-dimensional decision space is proposed, which modifies the time intervals between neighboring configuration nodes, enabling precise adaptation of the decision space boundaries. Additionally, a complementary dual-archive guided boundary exploration strategy is introduced to connect the feasible and infeasible regions, allowing for the effective utilization of information from infeasible solutions near the constraint boundaries. This heuristic approach guides the particle swarm in efficiently exploring areas close to the constraints, significantly enhancing the evolutionary optimization capability of the swarm. Furthermore, a swarm optimal position updating strategy based on sparsity sorting is developed. This guides the particle swarm to concentrate on exploring positions where non-dominated solutions on the Pareto front are more sparsely distributed, ensuring uniformity and completeness in the final Pareto front. Finally, the aforementioned strategies are integrated into a heuristic multi-objective particle swarm optimization (HMOPSO) algorithm for the trajectory optimization of manipulators. Comparative experiments are conducted with HMOPSO and existing advanced algorithms in the field of multi-objective optimization. Experimental results demonstrate that HMOPSO exhibits superior evolutionary optimization capabilities and faster convergence rates. Moreover, performance metrics such as inverse generation distance and dominant area during the iterative process of HMOPSO significantly outperform those of existing optimization algorithms.
Cited by 1
Esteban Arteaga-Cabrera, César Ramírez‐Márquez, Eduardo Sánchez‐Ramírez et al. · Applied Sciences · 2025
Optimization has become an indispensable tool in the food industry, addressing critical challenges related to efficiency, sustainability, and product quality. Traditional approaches, such as one-factor-at-a-time analysis, have been supplanted by more advanced methodologies like response surface methodology (RSM), which models interactions between variables, identifies optimal operating conditions, and significantly reduces experimental requirements. However, the increasing complexity of modern food production systems has necessitated the adoption of multi-objective optimization techniques capable of balancing competing goals, such as minimizing production costs while maximizing energy efficiency and product quality. Advanced methods, including evolutionary algorithms and comprehensive modeling frameworks, enable the simultaneous optimization of multiple variables, offering robust solutions to complex challenges. In addition, artificial neural networks (ANNs) have transformed optimization practices by effectively modeling non-linear relationships within complex datasets and enhancing prediction accuracy and system adaptability. The integration of ANNs with Industry 4.0 technologies—such as the Internet of Things (IoT), big data analytics, and digital twins—has enabled real-time monitoring and optimization, further aligning production processes with sustainability and innovation goals. This paper provides a comprehensive review of the evolution of optimization methodologies in the food industry, tracing the transition from traditional univariate approaches to advanced, multi-objective techniques integrated with emerging technologies, and examining current challenges and future perspectives.
Cited by 13Open access
L. B. Abhang, Mital Patel, Vaibhav Harishchandra Bansode · International Journal of Advances in Signal and Image Sciences · 2026
Complex engineering systems increasingly demand robust mathematical modeling frameworks capable of addressing nonlinear dynamics, uncertainty, multi-scale interactions, and high-dimensional decision spaces. Traditional analytical techniques often fail to capture the coupled physical, economic, and operational constraints inherent in modern engineering challenges. This paper presents a comprehensive review and synthesis of advanced mathematical modeling and optimization techniques used to solve complex engineering problems across mechanical, civil, electrical, aerospace, and industrial domains. The study explores nonlinear differential equation modeling, stochastic processes, multi-objective optimization, metaheuristic algorithms, convex optimization, and hybrid AI-driven optimization frameworks. Emphasis is placed on practical implementation, computational trade-offs, convergence behavior, and scalability. The paper proposes an integrated modeling–optimization framework that combines deterministic, probabilistic, and data-driven approaches to enhance robustness and solution quality. Applications in structural design, energy systems, supply chain networks, and control engineering are discussed to illustrate real-world relevance. The findings highlight the critical role of advanced optimization in improving efficiency, sustainability, reliability, and cost-effectiveness in engineering systems.
Cited by 0Open access
Xu, Kangyu, Liu, Yuan, Li, Miqing et al. · DMU Open Research Archive (De Montfort University) · 2023
Many-objective optimization problems (MaOPs) widely exist in industrial and scientific fields, where there are more than 3 objectives that are conflicting with each other (i.e., the improvement of the performance in one objective may lead to the deterioration of the performance of some other objectives). Because of the conflict between objectives, there is no unique optimal solution for MaOPs, but a group of compromise solutions need to be obtained to balance between objectives. As a class of population-based optimization algorithms inspired by biological evolution principles evolutionary algorithms have been proved to be effective in solving MaOPs, and have become one of the research hot spots in the field of multi-objective optimization. In the past 20 years, the research on many-objective evolutionary algorithms (MaOEAs) has made great progress, and a large number of advanced evolutionary methods and evaluation systems have been proposed and improved. In this paper, the research progress of evolutionary many-objective optimization (EMaO) is comprehensively reviewed. Specifically, it includes: (1) Describing the relevant theoretical background of EMaO; (2) Analyzing the problems and challenges faced by evolutionary algorithms in solving MaOPs; (3) Discussing the development of MaOEAs in detail; (4) Summarizing MaOPs and performance indicators in detail; (5) Introducing the visualization tools for high-dimensional objective space; (6) Summarizing the application of MaOEAs in some fields, and (7) Providing suggestions for future research in the domain.
Cited by 1
Carlos Mauricio Bula, Muhammad Atiq Ur Rehman, Sergio Rivera et al. · WSEAS TRANSACTIONS ON POWER SYSTEMS · 2025
Networked microgrid operation and control is supported by fault-tolerant optimization. In networked microgrids, the microgrid failure or dysconnectivity from the network is obvious and must be rectified and restored in real-time. For this purpose, we need advanced algorithms for fault-tolerant optimization and its control in networked microgrids operation. We have introduced a multi-objective genetic algorithm (MOGA) to solve the multi-objective optimization problem. Genetic algorithms being meta-heuristic techniques are used to solve formulated complex optimization problems; fault-tolerant optimization problems (FTOP). A fault-tolerant optimization problem (FTOP) has the possibility of partial components of the system failing or generating errors during the operation of networked microgrids. For this problem, we have determined the best possible solution which is obtained even in the presence of failure or errors as well. We have minimized the total cost of the system and provision of a consistent supply of energy in case of failure of a microgrid in the networked microgrids to get stable and reliable energy. FTOP problems mostly occur in critical and uncertain systems like microgrids in which reliable power is the demand from the customers with continuous availability.
Cited by 0Open access
Yang, D. · Research Repository (Delft University of Technology) · 2022
There are an increasing number of optimal-design paradigms used in architectural design nowadays. In these paradigms, a design task is formulated, or partially formulated, as an optimization problem. Multi-Disciplinary Optimization and Multi-Objective Optimization, as two important optimal-design paradigms, have shown their great potential in improving the performances of complex buildings in recent decades. Nevertheless, current paradigms for ill‑defined conceptual architectural design still lack ways to ensure the achievement of a reliable optimization problem, which hinders reliable design solutions despite the use of advanced optimization algorithms.<br/><br/>To address this problem, it is necessary to shift the focus from Optimization Problem Solving to Optimization Problem Formulation. This research particularly focuses on knowledge‑supported, dynamic and interactive Optimization Problem Re-Formulation in order to construct a new Multi‑Objective and Multi-Disciplinary Optimization (MOMDO) method suitable for use in ill‑defined conceptual architectural design. The proposed method consists of two subtype methods: Non‑dynamic, Interactive Re-formulation method (Subtype-I) and Dynamic, Interactive Re‑formulation method (Subtype-II), which can be used to explore design space in a convergent and divergent manner respectively. To support the re-formulation, various kinds of information and knowledge need to be extracted by utilizing different computational techniques, such as advanced sampling algorithms, Self-Organizing Map, Hierarchical Clustering, Smoothing Spline Analysis of Variance, Two-Level Variable Structure and modular programming. Moreover, a software workflow that can provide these computational techniques is developed; it integrates McNeel’s Grasshopper, ESTECO's modeFRONTIER and simulation software tools Daysim, EnergyPlus and Karamba3D. With the support of this software workflow, the proposed method is demonstrated via two case studies concerning the conceptual design of indoor sports halls.
Cited by 0
Isaac Bastos Correia Reis · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · 2019
The broad field of engineering is facing a paradigm shift where advanced optimization methods and techniques are more often used to solve complex problems. Most of these problems either require the analysis of a large amount of data or the solving of complex calculations, or even both. This dissertation aims to develop an understanding of non-linear optimization algorithms applied to a complex engineering design problem: a multi-layer plate under a ballistic impact. To solve a complex design engineering problem, the most efficient way is to combine non-linear optimization algorithms with a software capable of simulating the model and event. Accordingly, the first part of this document focuses on developing a Python script of the simulation model system using Abaqus API. The usage of an Abaqus Python script to simulate the event allows to generate specific variables and post-processing outputs essential to its posterior integration with optimization algorithms. Nevertheless, the development of a model that simulates a ballistic impact is complex and, thus, a sounding understanding on the physics and mechanics behind such an event are properly discussed. These insights are then used to validate the dynamic response and equilibrium of the simulated model. Furthermore, several modeling strategies are considered and analyzed throughout the first part of this document. The second part of this dissertation aims to acquire a comprehensive understanding of three optimization algorithms: Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Simulated Annealing (SA). The performance and efficiency of each algorithm, as well as numerous programming and optimization strategies, are tested in four different benchmarks. Each benchmark increases in complexity regarding its precedent and they all use the Abaqus Python script previously developed. This dissertation culminates in a multi-objective optimization procedure that uses the most efficient algorithm out of the three algorithms tested in the previous benchmarks. This multi-objective procedure uses every single-objective formulation, variables and constraints from the previous benchmarks which results in a highly non-linear problem. The results from this complex optimization problem are analyzed using and discussed.
Cited by 0Open access
Hezhang Zhao, Hu He, Shifeng Li et al. · Electronics · 2025
This paper presents KlyH, a new 1D (one-dimensional) large-signal simulation software for klystrons, designed to deliver efficient and accurate simulation and optimization tools. KlyH integrates a Fortran-based dynamic link library (DLL) as its computational core, which employs high-performance numerical algorithms to rapidly compute critical parameters such as efficiency, gain, and bandwidth. Compared with traditional 1D simulation tools, which often lack open interfaces and extensibility, KlyH is built with a modular and open architecture that supports seamless integration with advanced optimization and intelligent design algorithms. KlyH incorporates multi-objective optimization frameworks, notably the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Optimized Multi-Objective Particle Swarm Optimization (OMOPSO), enabling automated parameter tuning for efficiency maximization and interaction length optimization. Its bandwidth-of-klystron-analysis module predicts gain and output power across operational bandwidths, with optimization algorithms further enhancing bandwidth performance. A Java-based graphical user interface (GUI) provides an intuitive workflow for parameter configuration and real-time visualization of simulation results. The open architecture also lays the foundation for future integration of artificial intelligence algorithms, promoting intelligent and automated klystron design workflows. The accuracy of KlyH and its potential for parameter optimization are confirmed by a case study on an X-band relativistic klystron amplifier. Discrepancies observed between 1D simulations and 3D PIC (three-dimensional particle-in-cell) simulation results are analyzed to identify model limitations, providing critical insights for advancing high-performance klystron designs.
Cited by 1Open access
Changyong Zhang, Chun‐Ting Zhang · 2023
Multi-objective evolutionary algorithms currently face scalability challenges and struggle to maintain a balance between convergence and distribution in addressing large-scale multi-objective optimization problems. To address this limitation and more comprehensively explore the exponentially growing decision space, we introduce a large-scale multi-objective evolutionary algorithm based on multi-group co-evolution. This algorithm initiates by creating two independent subpopulations. The first subpopulation employs a multi-strategy, multi-objective evolutionary algorithm grounded in dominance relationships to preserve diversity within the decision space. Simultaneously, the second subpopulation utilizes a decomposition-based multi-objective evolutionary algorithm with virtual target vectors to facilitate a rapid convergence toward the target solution. As the populations progress to a specific stage, they are merged, and outstanding individuals are selected based on an enhanced hypervolume parameter. Subsequently, the populations undergo multiple loops with a reset based on predefined rules. Throughout different evolutionary stages, the sizes of the two populations dynamically adjust to maintain a balance between population convergence and distribution. To validate the effectiveness of our proposed algorithm, we compare it with five advanced large-scale multi-objective evolutionary algorithms and evaluate its performance on benchmark test problems LSMOPI to LSMOP9. The experimental results clearly demonstrate its superiority in solving large-scale multi-objective problems.
Cited by 0
Qamariddinov Shohruh Akmal o'g'li · Zenodo (CERN European Organization for Nuclear Research) · 2025
This study examines techniques to enhance the efciency of mechatronic modules in wheat tempering throughvirtual prototyping (digital twin), advanced moisture sensors, and multi-objective optimization. The proposed frameworkleverages genetic algorithms and artifcial neural networks to improve moisture uniformity by ≥ 20% and reduce waterenergy consumption by ≥ 10%. Pilot-scale experiments validated the model with a determination coefcient of R² ≥ 0.95,demonstrating robust real-time control performance.
Cited by 0Open access