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A multiple-criteria sensor selection framework based on qualitative physical models

ADVANCED ENGINEERING INFORMATICS 2025年 65卷

作者： Diao, Xiaoxu Rownak, Md Ragib Olatubosun, Samuel Vaddi, Pavan Kumar Smidts, Carol Ohio State Univ Columbus OH 43210 USA

Sensor selection is critical for designing effective online monitoring systems for safety-critical applications. This paper proposes a novel sensor selection framework that utilizes qualitative system models to evaluate various sensor configurations based on multiple criteria. The criteria assess capabilities like fault diagnostics, risk reduction, observability, functionality, integrability, and cost. The framework uses the Integrated System Failure Analysis to generate signal features from qualitative system models. These features are used to evaluate sensor configurations against the selection criteria. The criteria can be applied as constraints or objectives for optimization. The Non-dominated Sorting Genetic Algorithm handles the multi-objective optimization to find Pareto optimal sensor deployment solutions. The framework is demonstrated on a reactor cavity cooling system case study, generating optimal configurations considering temperature, flow, pressure, density, and radiation sensors. The framework aids online monitoring system design by recommending sensor deployment configurations that balance critical capabilities. Qualitative models provide effective analysis despite the lack of operational data. The flexible criteria and multi-objective optimization enable extensive exploration of configurations in early development stages.

关键词： Sensor selection Qualitative modelling multiple objective optimization Fault detection and discrimination Safety critical system

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Coordinated optimization of control parameters for improving the stability of wind-PV hybrid power systems under improved pelican optimization algorithm

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ELECTRICAL ENGINEERING 2025年第4期107卷 5163-5186页

作者： Liu, Peng Wu, Yuchao Sun, Junwei Zhao, Junhong Zhengzhou Univ Light Ind Coll Elect & Informat Engn Dongfeng St Zhengzhou 450002 Henan Peoples R China Zhengzhou Univ Light Ind Coll Mat & Chem Engn Kexue St Zhengzhou 450000 Henan Peoples R China

This paper explores the coordinated optimization of the parameters of controllers, including power system stabilizer, unified power flow controller with power oscillation damping controller and the installation position of unified power flow controller, to enhance the stability of wind-PV hybrid power systems. An effective improved Pelican optimization algorithm is developed to optimize the parameters of these controllers. The proposed algorithm incorporates the L & eacute;vy flight strategy when updating the positions of search individuals and employs the average error function as an iterative termination condition, which overcomes the local convergence problem of the Pelican optimization algorithm. Simulation results from the hybrid power systems combining IEEE four-machine interconnected two-area system and wind-PV system substantiate the effectiveness of the proposed method and the advantage on mitigating low-frequency oscillations in renewable energy grid integration systems.

关键词： multiple objective optimization Wind-PV hybrid system IPOA UPFC-POD

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Enhancement of Bayesian optimal interval design by accounting for overdose and underdose errors trade-offs

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JOURNAL OF BIOPHARMACEUTICAL STATISTICS 2025年第1期35卷 1-20页

作者： Sadachi, Ryo Sato, Hiroyuki Fujiwara, Takeo Hirakawa, Akihiro Natl Canc Ctr Ctr Res Adm & Support Biostat Div Tokyo Japan Tokyo Med & Dent Univ Dept Global Hlth Promot Tokyo Japan Tokyo Med & Dent Univ Grad Sch Med & Dent Sci Dept Clin Biostat Tokyo Japan Tokyo Med & Dent Univ Grad Sch Med & Dent Sci Dept Clin Biostat 1-5-45 YushimaBunkyo ku Tokyo 1138510 Japan

Model-assisted designs, a new class of dose-finding designs for determining the maximum tolerated dose (MTD), model only the dose-limiting toxicity (DLT) data observed at the current dose based on a simple binomial model and offer the boundaries of DLT for the determination of dose escalation, retention, or de-escalation before beginning the trials. The boundaries for dose-escalation and de-escalation decisions are relevant to the operating characteristics of the design. The well-known model-assisted design, Bayesian Optimal Interval (BOIN), selects these boundaries to minimize the probability of incorrect decisions at each dose allocation but does not distinguish between overdose and underdose allocations caused by incorrect decisions when calculating the probability of incorrect decisions. Distinguishing between overdose and underdose based on the decision error in the BOIN design is expected to increase the accuracy of MTD determination. In this study, we extended the BOIN design to account for the decision probabilities of incorrect overdose and underdose allocations separately. To minimize the two probabilities simultaneously, we propose utilizing multiple objective optimizations and formulating an approach for determining the boundaries for dose escalation and de-escalation. Comprehensive simulation studies using fixed and randomly generated scenarios of DLT probability demonstrated that the proposed method is superior or comparable to existing interval designs, along with notably better operating characteristics of the proposed method.

关键词： Bayesian adaptive design dose finding model-assisted design maximum tolerated dose multiple objective optimization

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Group Decision Making in Multiobjective optimization: A Systematic Literature Review

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GROUP DECISION AND NEGOTIATION 2025年第2期34卷 329-371页

作者： Pajasmaa, Juuso Miettinen, Kaisa Silvennoinen, Johanna Univ Jyvaskyla Fac Informat Technol POB 35 Agora FI-40014 Jyvaskyla Finland

Group decision making has been studied from several viewpoints and a variety of methods has been proposed. However, in the literature on solving multiobjective optimization problems, the main focus has been on supporting a single decision maker. We conducted a systematic literature review to examine and synthesize the state-of-the-art of multiobjective optimization methods developed for group decision making. We analyze group decision making methods of multiobjective optimization according to how preferences of several decision makers are incorporated into the solution process, how to select the most preferred solution for the group, different types of decision makers, types of groups and how the group is to operate during the solution process. In addition, we identify the key issues in the literature that are required to be considered in further method development to increase the methods' applicability in solving real-world problems. Finally, we guide how to select a method for solving real-world multiobjective optimization problems with multiple decision makers and suggest future research directions.

关键词： multiple objective optimization Preference information Survey Several decision makers Consensus

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Layout-Enhanced Topology optimization (LETO): A modular approach for adaptable and manufacturable designs

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ADVANCES IN ENGINEERING SOFTWARE 2025年 205卷

作者： Yoshida, Tatsuhito Yamada, Takayuki Univ Tokyo Grad Sch Engn Dept Mech Engn Yayoi 2-11-16Bunkyo Ku Tokyo 1138656 Japan Univ Tokyo Inst Engn Innovat Grad Sch Engn Dept Strateg Studies Yayoi 2-11-16Bunkyo Ku Tokyo 1138656 Japan

This paper introduces Layout-Enhanced Topology optimization (LETO), a modular topology optimization method that extends the potential of the conventional topology optimization. In this approach, a single module shape is optimized to enable adaptation to multiple design objectives by changing the layout through geometrical manipulations applied to the modules. The optimization algorithm simultaneously selects the best layout for each design objective and optimizes the module shape. Numerical examples of a steady-state heat conduction problem are demonstrated to confirm the effectiveness of this method. The results show that, with improved thermal conduction efficiency, the optimized module can be rearranged to suit multiple design objectives, enhancing the adaptability of the conventional topology optimization. LETO has potential applications in mass-production cases, addressing the demand for versatile and manufacturable designs.

关键词： Topology optimization Level set method multiple objective optimization Modular design Additive manufacturing

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Prediction and multi-objective optimization of high-strength concrete parameters via soft computing approaches

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EXPERT SYSTEMS WITH APPLICATIONS 2009年第3期36卷 6145-6155页

作者： Baykasoglu, Adil Oztas, Ahmet Ozbay, Erdogan Gaziantep Univ Dept Ind Engn TR-27310 Gaziantep Turkey Gaziantep Univ Dept Civil Engn TR-27310 Gaziantep Turkey

The optimization of composite materials such as concrete deals with the problem of selecting the values of several variables which determine composition, compressive stress, workability and cost etc. This study presents multi-objective optimization (MOO) of high-strength concretes (HSCs). One of the main problems in the optimization of HSCs is to obtain mathematical equations that represents concrete characteristic in terms of its constitutions. In order to solve this problem, a two step approach is used in this study. In the first step, the prediction of HSCs parameters is performed by using regression analysis, neural networks and Gen Expression Programming (GEP). The output of the first step is the equations that can be used to predict HSCs properties (i.e. compressive stress, cost and workability). In order to derive these equations the data set which contains many different mix proportions of HSCs is gathered from the literature. In the second step, a MOO model is developed by making use of the equations developed in the first step. The resulting MOO model is solved by using a Genetic Algorithm (GA). GA employs weighted and hierarchical method in order to handle multiple objectives. The performances of the prediction and optimization methods are also compared in the paper. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： multiple objective optimization Meta-heuristics Prediction High-strength concrete

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Multi-objective optimization model for a downstream oil and gas supply chain

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APPLIED MATHEMATICAL MODELLING 2017年 52卷 689-708页

作者： Ghaithan, Ahmed M. Attia, Ahmed Duffuaa, Salih O. King Fand Univ Petr & Minerals Dept Syst Engn Dhahran Saudi Arabia

Oil and gas companies play an important role in the global economy since they supply a large portion of the necessary energy to the world. The optimal production of oil and gas should be performed in an integrated fashion for the whole supply chain. The downstream oil and gas supply chain (OGSC) has attracted the interest of many researchers due to its central role in the world economy. This paper develops an integrated multi-objective OGSC model for medium-term tactical decision making for the OGSC downstream segment. The selected objectives related to downstream activities are the following: minimize the total cost, maximize the total revenue, and maximize the service level. The model includes multi-period and multi-product inputs. The model is verified and solved using an improved augmented 8-constraint algorithm to generate Pareto optimal solutions. The model assists in assessing various trade-offs among different objectives and guides decision makers for the effective management of the downstream OGSC. The utility of the proposed model is demonstrated using a real case from a Saudi Arabian downstream OGSC. Sensitivity analysis is conducted to investigate the effects of input parameters on the set of Pareto optimal solutions. The model is expected to have a positive impact on the future management of this important component of the energy sector. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Energy multiple objective optimization Oil and gas supply chain Tactical planning Pareto optimal

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Global maximization of UTA functions in multi-objective optimization

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2013年第2期228卷 397-404页

作者： Duy Van Nguyen Univ Trier Dept Math D-54286 Trier Germany

The UTAs (UTilite Additives) type methods for constructing nondecreasing additive utility functions were first proposed by Jacquet-Lagreze and Siskos in 1982 for handling decision problems of multicriteria ranking. In this article, by UTA functions, we mean functions which are constructed by the UTA type methods. Our purpose is to propose an algorithm for globally maximizing UTA functions of a class of linear/convex multiple objective programming problems. The algorithm is established based on a branch and bound scheme, in which the branching procedure is performed by a so-called I-rectangular bisection in the objective (outcome) space, and the bounding procedure by some convex or linear programs. Preliminary computational experiments show that this algorithm can work well for the case where the number of objective functions in the multiple objective optimization problem under consideration is much smaller than the number of variables. (C) 2012 Elsevier B.V. All rights reserved.

关键词： multiple objective optimization Utility function program UTA type methods UTA functions Global optimization Branch-and-bound algorithm

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A coverage-based Box-Algorithm to compute a representation for optimization problems with three objective functions

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JOURNAL OF GLOBAL optimization 2017年第3期67卷 581-600页

作者： Kuhn, Tobias Ruzika, Stefan Tech Univ Kaiserslautern Fachbereich Math Kaiserslautern Germany Univ Koblenz Landau Math Inst Fachbereich Math Nat Wissensch Koblenz Germany

In this paper, a method for computing a substitute of the nondominated set, a so-called representation system, is proposed for tricriteria optimization problems. Our algorithm is easy to implement, it is flexibly applicable to a wide range of problems, and it takes several quality measures which have been proposed in the literature into account. The resulting representation system satisfies provably certain quality levels and, thus, it may be utilized to make proper and confident decisions.

关键词： multiple objective optimization Efficient solution Approximation Representation systems Box-Algorithm Coverage error

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Multi-objective genetic optimization of the fuel cell hybrid vehicle supervisory system: Fuzzy logic and operating mode control strategies

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2015年第36期40卷 12512-12521页

作者： Ahmadi, Saman Bathaee, S. M. T. KN Toosi Univ Technol Dept Elect Engn Hybrid Elect Vehicle Res Ctr Tehran Iran

A power train of the fuel cell vehicles includes the battery as a secondary power source to improve the vehicle's performance. In this composition, the vehicle's performance depends largely on quality of energy management strategies (EMSs). In this paper, two EMSs are introduced to control the energy in the fuel cell hybrid vehicle (FCHV). These are: fuzzy logic control (FLC) and operating mode control (OMC). Genetic algorithm (GA) is implemented to optimize these strategies by off-line simulation through a combined driving cycle. Comparison of these control methods with other approaches like EMS of advanced vehicle simulator (ADVISOR) software and non-optimized control strategies, demonstrate the superiority of optimized strategies regarding to equivalent energy consumption, energy efficiency, and state-of-charge (SOC) variation. Copyright (C) 2015, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Energy management strategy Fuel cell hybrid vehicle Fuzzy logic control Genetic algorithm multiple objective optimization Operating mode control

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