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A computational model for multiobjective optimization of multipolar stimulation in cochlear implants: An enhanced focusing approach

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 280卷

作者： Hernandez-Gil, Marcos Ramos-de-Miguel, Angel Greiner, David Benitez, Domingo Ramos-Macias, Angel Escobar, Jose M. Univ Las Palmas Gran Canaria Inst Intelligent Syst & Numer Applicat Engn SIANI Campus Univ Tafira Baja Las Palmas Gran Canaria 35017 Las Palmas Spain Complejo Hosp Univ Insular Materno Infantil Gran C Dept Otolaryngol Las Palmas Gran Canaria 35002 Las Palmas Spain

Multipolar stimulation has been demonstrated to improve auditory perception in individuals with cochlear implants by generating more focused electric fields through simultaneous activation of multiple electrodes. In this study, we propose a novel approach to multipolar stimulation that aims to achieve the narrowest possible pattern of current densities at target neurons. Our goal is to find the optimal profile of currents delivered by the electrodes that maximizes the focusing for a specific power consumption, or alternatively, which minimizes the power for a given focusing. To this end, we have designed two objective functions which are optimized through multiobjective evolutionary algorithms. These objective functions are evaluated using a patient-specific finite element volume conduction model that replicates the cochlear geometry and electrical behavior of the implant. Experimental results demonstrate that this approach achieves tighter current density focusing compared to phased-array stimulation, albeit with higher power consumption. Additionally, it is possible to reach non-dominated solutions that simultaneously improve the focusing and power consumption of both monopolar and phased-array stimulation.

关键词： Cochlear implant Multipolar stimulation Finite element method Evolutionary algorithms multiobjective optimization Optimum design

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An Efficient Neuro-Dynamic Network for Constructing the Pareto Front of Convex multiobjective optimization Problems

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INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 2025年第5期39卷 894-913页

作者： Abkhizi, M. Ghaznavi, M. Noori Skandari, M. H. Shahrood Univ Technol Fac Math Sci Shahrood Iran

This article introduces an effective neural network model for addressing convex multiobjective optimization problems, developed using the Karush-Kuhn-Tucker optimality conditions for multiobjective optimization problems. The proposed model is shown to be stable in the sense of Lyapunov and globally convergent to efficient solutions of the original problem. Additionally, a novel algorithm is presented to achieve a uniform approximation of the Pareto frontier. The approach's validity and effectiveness are demonstrated through experimental multiobjective problems. For a thorough comparison with other methods, four metrics including purity, uniformity, coverage, and spacing indicators are used, focusing on the positioning of the non-dominated points. Extensive numerical tests highlight the proposed algorithm's substantial advantages.

关键词： efficient solution multiobjective optimization neuro-dynamic network pareto front stability

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Remaining useful life prediction of equipment using a multiobjective optimization reinforced prognostic approach

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 204卷

作者： Zhuang, Jichao Ding, Xiaotong Zhang, Zilin Zhao, Xiaoli Li, Weigang Feng, Ke Guangzhou Univ Sch Mech & Elect Engn Guangzhou 510006 Peoples R China Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China Nanjing Univ Sci & Technol Sch Mech Engn Nanjing 210094 Peoples R China Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan 430081 Peoples R China

Data-driven methods have rapidly advanced equipment degradation monitoring and prognosis. However, traditional deep models rely on weak prior degradation knowledge and may not effectively incorporate degradation damage information. To address this limitation, a Deep multiobjective optimization Reinforced Prognostic (MORP) framework is proposed in this paper for equipment health prognosis. Specifically, a priori degradation knowledge and multi-source deep features are combined at both the feature and health indicator (HI) levels. They are then quantified into an unsupervised multi-objective optimization decision. Preceding this step, a multi-degradation criterion and HI generalizability are formulated as a multi-objective function, with the aim of enhancing the generalizability, monotonicity, tendency, and robustness of HIs. Comprehensive Health Indicators (CHIs) are then constructed while retaining the advantages of the Pareto frontier, using a reinforcement learning-guided swarm intelligence optimization method. To address anomalies within CHIs, a HI burr correction method featuring an interpolation-extrapolation term is introduced. Additionally, the prediction of remaining useful life is accomplished through a supervised prognostic scheme. Finally, the proposed methodology is applied to equipment datasets to validate its performance.

关键词： Remaining useful life prediction multiobjective optimization Information fusion Health indicator Reinforcement learning

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On Stationarity Conditions and Constraint Qualifications for multiobjective optimization Problems with Cardinality Constraints

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APPLIED MATHEMATICS AND optimization 2025年第1期91卷 1-26页

作者： Garmanjani, Rohollah Krulikovski, Evelin H. M. Ramos, Alberto NOVA Sch Sci & Technol NOVA FCT Ctr Math & Applicat NOVA Math Caparica Portugal Univ Fed Parana Dept Math Curitiba Brazil Univ Tarapaca Dept Matemat Casilla 7D Arica Chile

The purpose of this paper is to develop Pareto optimality conditions and constraint qualifications (CQs) for multiobjective Programs with Cardinality Constraints (MOPCaC). In general, such problems are difficult to solve, not only because they involve a cardinality constraint that is neither continuous nor convex, but also because there may be a potential conflict between the various objective functions. Thus, we reformulate the MOPCaC based on the problem with continuous variables, namely the relaxed problem. Furthermore, we consider different notions of optimality (weak/strong Pareto optimal solutions). Thereby, we define new stationarity conditions that extend the classical Karush-Kuhn-Tucker (KKT) conditions of the scalar case. Moreover, we also introduce new CQs, based on the recently defined multiobjective normal cone, to ensure compliance with such stationarity conditions. Important statements are illustrated by examples.

关键词： multiobjective optimization Cardinality constraints Nonlinear programming Sparse solutions Constraint qualifications Stationarity

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Cooperative Co-Evolutionary Dynamic multiobjective optimization for Burden Surface Profile Decision in Blast Furnace Ironmaking

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Li, Yanjiao Zhang, Yongjun Li, Qing Zhang, Jie Univ Sci & Technol Beijing Inst Engn Technol Beijing 100083 Peoples R China Univ Sci & Technol Beijing Natl Engn Res Ctr Adv Rolling Technol Beijing 100083 Peoples R China Univ Leeds Sch Elect & Elect Engn Leeds LS2 9JT England Univ Sci & Technol Beijing Sch Automat & Elect Engn Key Lab Knowledge Automat Ind Proc Minist Educ Beijing 100083 Peoples R China Queens Univ Belfast Ctr Wireless Innovat Sch Elect Elect Engn & Comp Sci Belfast BT3 9DT North Ireland

Burden surface profile (BSP) is crucial for energy utilization improvement and stable operation in the blast furnace (BF) ironmaking process. The BSP decision can be expressed as a multiobjective optimization problem. However, the production process has nonlinear dynamic characteristics, leading to certain defects in conventional stationary optimization. To address the aforementioned issue, this study presents a novel dynamic multiobjective decision scheme to identify the optimal BSP. In the proposed scheme, a dynamic multiobjective optimization problem with constraints is first constructed. Then, to get rid of the dilemma of deriving parameter dependence using the physics domain knowledge, we employ kernel extreme learning machine (K-ELM) to establish the multiobjective optimization models. Subsequently, aiming to solve this dynamic multiobjective problem (DMOP), a novel dynamic optimization framework based on environment sensitivities (CCE-D-NSGA-II) is developed, which embeds the parameter decomposition, change response strategy, and optimal solution selection, for adapting to the nonstationary characteristics of BF ironmaking process. Finally, experiments using the actual production data are carried out to verify the effectiveness and feasibility of the proposed scheme. The results demonstrate that the proposed scheme achieves a set of Pareto optimal solutions for the BSP optimization problem and outperforms the selected baseline methods in both convergence and population diversity.

关键词： optimization Production Kernel Iron Surface treatment Surface topography Raw materials Solids Electronic mail Training Blast furnace (BF) burden surface profile (BSP) cooperative co-evolutionary algorithm dynamic optimization multiobjective optimization

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Robust Calibration of Shaft and Base Resistance Factors for Piles Based on multiobjective optimization

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JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING 2025年第3期151卷

作者： Zhang, Yuting Huang, Jinsong Giacomini, Anna Xie, Jiawei Lu, Jianlin Univ Newcastle Res Ctr Geotech Sci & Engn Discipline Civil Surveying & Environm Engn Callaghan NSW 2308 Australia

Resistance factors are used to account for the uncertainties associated with pile resistance in load and resistance factor design (LRFD). Current design codes and most previous studies recommend a single resistance factor applied to the total pile resistance (shaft and base resistances). However, the uncertainties associated with shaft and base resistances are significantly different. Moreover, resistance factors are generally calibrated based on the statistics of resistance bias factors derived using all data collected from different sites, whereas the variability of the statistics between various sites (i.e., cross-site variability) has been ignored in the traditional calibration approaches, which may result in the designs based on the calibrated resistance factors violating safety requirements. In this paper, a robust calibration approach is proposed to calibrate shaft and base resistance factors, explicitly considering the cross-site variability in the statistics of resistance bias factors in the calibration process. To achieve that, the feasible robustness concept is adopted to describe the probability that the design remains able to achieve the target reliability index when the statistics of resistance bias factor exhibit cross-site variability. The calibration process is implemented through a multiobjective optimization, which leads to a Pareto front that describes the trade-off relationship between shaft and base resistance factors and feasible robustness. The optimal shaft and base resistance factors are determined using the minimum distance approach. The proposed approach is demonstrated and applied to calibrate shaft and base resistance factors for three design methods, the Vesic, Meyerhof, and Nordlund methods. Results show that resistance factors are significantly affected by design methods and the ratio of shaft and base resistances.

关键词： Load and resistance factor design (LRFD) Resistance factors Pile multiobjective optimization

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Physics-informed neural networks guided modelling and multiobjective optimization of a mAb production process

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2025年第3期103卷 1319-1334页

作者： Alam, Md Nasre Anurag, Anurag Gangwar, Neelesh Ramteke, Manojkumar Kodamana, Hariprasad Rathore, Anurag S. Indian Inst Technol Delhi Dept Chem Engn New Delhi 110016 India Indian Inst Technol Delhi Sch Interdisciplinary Res New Delhi India Indian Inst Technol Delhi Yardi Sch Artificial Intelligence New Delhi India

In this paper, we aim to correlate various process and product quality attributes of a mammalian cell culture process with process parameters. To achieve this, we employed physics-informed neural networks that solve the governing ordinary differential equations comprising independent variables (inputs- time, flow rates, and volume) and dependent variables (outputs- viable cell density, dead cell density, glucose concentration, lactate concentration, and monoclonal antibody concentration). The proposed model surpasses the prediction and accuracy capabilities of other commonly used modelling approaches, such as the multilayer perceptron model. It has higher R-squared (R-2), lower root mean square error, and lower mean absolute error than the multilayer perceptron model for all output variables (viable cell density, viability, glucose concentration, lactate concentration, and monoclonal antibody concentration). Furthermore, we incorporate a Bayesian optimization study to maximize viable cell density and monoclonal antibody concentration. Single objective optimization and weighted sum multiobjective optimization were carried out for viable cell density and monoclonal antibody concentration in separate (single objective optimization) and combined (multiobjective optimization) forms. An increment of 13.01% and 18.57% for viable cell density and monoclonal antibody concentration, respectively, were projected under single objective optimization, and 46.32% and 67.86%, respectively, for multiobjective optimization as compared to the base case. This study highlights the potential of the physics-informed neural networks-based modelling and optimization of upstream processing of mammalian cell-based monoclonal antibodies in biopharmaceutical operations.

关键词： bayesian optimization monoclonal antibodies multilayer perceptron multiobjective optimization ordinary differential equations physics-informed neural networks

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Proximal gradient method for convex multiobjective optimization problems without Lipschitz continuous gradients

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COMPUTATIONAL optimization AND APPLICATIONS 2025年第1期91卷 27-66页

作者： Zhao, Xiaopeng Raushan, Ravi Ghosh, Debdas Yao, Jen-Chih Qi, Min Tiangong Univ Sch Math Sci Tianjin 300387 Peoples R China Indian Inst Technol BHU Dept Math Sci Varanasi 221005 India China Med Univ China Med Univ Hosp Res Ctr Interneural Comp Taichung 40447 Taiwan Acad Romanian Scientists Bucharest 50044 Romania

This paper analyzes a proximal gradient method for nondifferentiable convex multiobjective optimization problems, where the components of the objective function are the sum of a proper lower semicontinuous function and a continuously differentiable function. By adopting a typical line search procedure, it is found that without a Lipschitz continuity of the gradients of the smooth part of the objective function, the proposed method is able to generate sequences that converge to weakly Pareto optimal points. The convergence rate of the method is found to be O(1/k)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathcal {O}(1/k)$$\end{document}. Further, if the smooth component in the objective function is strongly convex, then the convergence rate is O(rk)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathcal {O}(r<^>k)$$\end{document} for some r is an element of(0,1)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$r\in (0,1)$$\end{document}. Moreover, in the absence of a strong convexity assumption, we also consider the accelerated version of the proposed approach based on the Nesterov step strategy. We obtain the improved convergence rate of O(1/k2)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathcal {O}(1/k<^>2)$$\end{document}, which is measured

关键词： multiobjective optimization Proximal gradient method Pareto optimality Iteration complexity

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Universal nonmonotone line search method for nonconvex multiobjective optimization problems with convex constraints

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COMPUTATIONAL & APPLIED MATHEMATICS 2025年第1期44卷 1-22页

作者： Pinheiro, Maria Eduarda Grapiglia, Geovani Nunes Trier Univ Dept Math Univ ring 15 D-54296 Trier Germany Catholic Univ Louvain ICTEAM INMA Ave Georges Lemaitre 4 B-1348 Louvain La Neuve Belgium

In this work we propose a general nonmonotone line-search method for nonconvex multiobjective optimization problems with convex constraints. At the kth iteration, the degree of nonmonotonicity is controlled by a vector nu k\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\nu _{k}$$\end{document} with nonnegative components. Different choices for nu k\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\nu _{k}$$\end{document} lead to different nonmonotone step-size rules. Assuming that the sequence nu kk >= 0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left\{ \nu _{k}\right\} _{k\ge 0}$$\end{document} is summable, and that the ith objective function has H & ouml;lder continuous gradient with smoothness parameter theta i is an element of(0,1]\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\theta _i \in (0,1]$$\end{document}, we show that the proposed method takes no more than O & varepsilon;-1+1 theta min\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathcal {O}\left( \epsilon <^>{-\left( 1+\frac{1}{\theta _{\min }}\right) }\right) $$\end{document} iterations to find a & var

关键词： multiobjective optimization Projected gradient methods Nonmonotone line searches Worst-case complexity

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Insulation Structure Design for ±550-kV DC GIS Based on multiobjective optimization Algorithm

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IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION 2025年第2期32卷 1064-1073页

作者： Zhang, Boya Tao, Haifei Li, Yixuan Li, Xingwen Nan, Zhenle Luo, Wei Zheng, Yao Wang, Guoli Xi An Jiao Tong Univ State Key Lab Elect Insulat & Power Equipment Xian 710049 Peoples R China Xian XD Switchgear Elect Co Ltd Xian 710077 Peoples R China China Southern Power Grid Co Ltd Guangzhou 510080 Peoples R China China Southern Power Grid Elect Power Res Inst Guangzhou 510663 Peoples R China

In high-voltage direct current gas-insulated switchgear (HVdc GIS), the problem of surface charge accumulation on insulators has garnered significant attention, limiting the development of dc GIS at high voltage levels. This study proposes an insulation structure optimization method based on a multiobjective optimization method to balance the tangential and normal electric fields on the insulator surface and mitigate local electric stress, achieving efficient and precise GIS insulation structure design. The optimization objectives include the tangential electric field and surface charge density. The study utilizes nondominated sorting genetic algorithm II (NSGA-II) and response surface methodology (RSM) to obtain the optimal structure for the conical insulator and shield of +/- 550-kV dc GIS. In addition, the study investigates the impact of gap distance on the surface charge and electric field distribution of the insulator. Ultimately, following insulation optimization design criteria, the study achieves the optimal structure and gap distance for +/- 550-kV GIS, providing crucial insights for the insulation design of HVdc GIS.

关键词： Insulators Electric fields optimization Response surface methodology Shape Ions Switchgear IP networks High-voltage techniques HVDC transmission Conical insulator dc gas-insulated switchgear (GIS) multiobjective optimization surface charge

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