Wyniki wyszukiwania - BazTech

1

Review, design, stabilization and synchronization of fractional-order energy resources demand-supply hyperchaotic systems using fractional-order PD-based feedback control scheme

Soukkou Ammar, Soukkou Yassine, Haddad Sofiane, Benghanem Mohamed, Rabhi Abdelhamid

Archives of Control Sciences

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2023

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Vol. 33, No. 3

539--563

EN

This paper introduces a fractional-order PD approach (F-oPD) designed to control a large class of dynamical systems known as fractional-order chaotic systems (F-oCSs). The design process involves formulating an optimization problem to determine the parameters of the developed controller while satisfying the desired performance criteria. The stability of the control loop is initially assessed using the Lyapunov’s direct method and the latest stability assumptions for fractional-order systems. Additionally, an optimization algorithm inspired by the flight skills and foraging behavior of hummingbirds, known as the Artificial Hummingbird Algorithm (AHA), is employed as a tool for optimization. To evaluate the effectiveness of the proposed design approach, the fractional-order energy resources demand-supply (Fo-ERDS) hyperchaotic system is utilized as an illustrative example.

2

Neutrosophic compromise programming approach for multiobjective nonlinear transportation problem with supply and demand following the exponential distribution

Adhami Ahmad Yusuf, Faizan Mohd, M Anas

Operations Research and Decisions

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2022

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Vol. 32, No. 3

1--15

EN

Decision-making is a tedious and complex process. In the present competitive scenario, any incorrect decision may excessively harm an organization. Therefore, the parameters involved in the decision-making process should be looked into carefully as they may not always be of a deterministic nature. In the present study, a multiobjective nonlinear transportation problem is formulated, wherein the parameters involved in the objective functions are assumed to be fuzzy and both supply and demand are stochastic. Supply and demand are assumed to follow the exponential distribution. After converting the problem into an equivalent deterministic form, the multiobjective problem is solved using a neutrosophic compromise programming approach. A comparative study indicates that the proposed approach provides the best compromise solution, which is significantly better than the one obtained using the fuzzy programming approach.

3

An improved proximal method with quasi-distance for nonconvex multiobjective optimization problem

Amir Fouzia, Farajzadeh Ali, Alzabut Jehad

Journal of Applied Analysis

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2022

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Vol. 28, nr 2

333--340

EN

Multiobjective optimization is the optimization with several conflicting objective functions. However, it is generally tough to find an optimal solution that satisfies all objectives from a mathematical frame of reference. The main objective of this article is to present an improved proximal method involving quasidistance for constrained multiobjective optimization problems under the locally Lipschitz condition of the cost function. An instigation to study the proximal method with quasi distances is due to its widespread applications of the quasi distances in computer theory. To study the convergence result, Fritz John’s necessary optimality condition for weak Pareto solution is used. The suitable conditions to guarantee that the cluster points of the generated sequences are Pareto-Clarke critical points are provided.

4

Multiobjective optimiaztion of microstructure parameters in a thermoelastic porous material by means of differential evolution and elements of game theory

Długosz Adam, Schlieter Tomasz

Computer Methods in Materials Science

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2022

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Vol. 22, No. 3

117--124

EN

The paper is devoted to the optimization of the microstructure parameters of a porous medium under thermo-mechanical loading. Four different criteria related to the properties of the porous material have been proposed and numerically implemented. To solve a multiobjective problem, a novel method based on the coupling of differential evolution and elements of game theory is used. The proposed algorithm features an appropriate balance between exploration and exploitation of objective space, which is necessary for the successful optimization of these types of tasks with the use of numerical simulations. The model of the thermo-elastic porous material is composed of two-scale direct analysis based on a numerical homogenization. Direct thermoelastic analysis with representative volume element (RVE) and finite element method (FEM) is performed. Numerical example of the optimization illustrating the usefulness of the proposed method is included.

5

Static and dynamic impact performance optimization of CFRP/Al bolt joint coupling structure-lay-up-connection parameters

Xu Wenchao, Wang Dengfeng

Archives of Civil and Mechanical Engineering

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2022

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Vol. 22, no. 3

art. no. e118

EN

Static shearing, drawing, and dynamic impact test schemes of carbon fber reinforced polymer (CFRP)/aluminum alloy (Al) bolt joint were designed. The fnite element model of the CFRP/Al bolt joint was established, and the failure modes of the joints under the static and dynamic impact conditions were analyzed. The structure, lay-up, and connection parameters of the joint were defned as design variables, and the static and dynamic impact performance indicators of the joint and the lay-up numbers of the CFRP sheet were defned as optimization objectives. Integrated multiobjective optimization was conducted for joints, employing the radial basis function neural network (RBFNN) surrogate model, elitist nondominated sorting genetic (NSGA-II) algorithm, and entropy-technique for order preference by similarity to ideal solution (E-TOPSIS) decision method. The best trade-of solution was obtained, and the optimal design variables were determined. The optimized joint was fabricated, and static and dynamic impact tests were carried out. The test and simulation results were compared to verify the efectiveness of simulation and optimization.

6

Multiobjective optimization of fluphenazine nanocomposite formulation using NSGA-II method

Abu Sharar Ahmed Adnan, Ramadan Saleem Z., Hussein-Al-Ali Samer Hasan

Materials Science Poland

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2021

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Vol. 39, No. 4

517--544

EN

The World Health Organization (WHO, 2019) reports that schizophrenia affects approximately 20 million people worldwide, and the annual number of new cases is estimated at 1.5%/10,000 people. As a result, there is a demand for making the relevant medicines work better. Using a fluphenazine (FZN) drug delivery system that has been optimized using nanoparticles (NPs) technology is an important alternative treatment option for noncompliant patients with schizophrenia. Compared to the conventional delivery system, the NPs delivery system provides a controlled-release treatment, minimizes drug levels reaching the blood, and has fewer side effects as well. As a result of using the NPs delivery system, patients can obtain the benefits of reduced daily dosing and improved compliance. In this context, this study was performed to develop a mathematical model for FZN to optimize its nanocomposite delivery system using a mixture-process DoE and multiobjective optimization (MOO) approaches. The influences of NPs input fabrication parameters [i.e., FZN percentage, chitosan (CS) percentage, sodium tripolyphosphate (TPP) percentage, and pH] were investigated by mixture-designed experiments and analyzed by analysis of variance (ANOVA); subsequently, based on the results of the analysis, three regression models were built for size, zeta potential (ZP), and drug loading efficiency (LE%); and thereafter, these models were validated using 26 experiments with three replicates. The MOO approach was employed using a non-dominated sorting genetic algorithm (NSGA-II) to provide the optimal fitness value of each objective function by minimizing NPs size, maximizing ZP, and maximizing LE%. Test of hypotheses showed no statistical differences between the average observed values and the corresponding predicted values calculated by the regression models (126.6 nm, 18.7 mV, and 91.6%, respectively). As there is no benchmark available for the optimal NPs input fabrication parameters in the literature, the optimized formulation was further characterized using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), polydispersity index (PdI), and differential scanning calorimetry (DSC). Those tests indicated that FZN was successfully encapsulated into the final nanocomposite. Furthermore, an in-vitro drug release study was carried out and showed that at least 5 days would be needed for FZN to be fully released from its nanocomposite in a sustained-release pattern. The nanocomposite could serve as a model for the controlled and extended delivery of many drugs.

7

Multiobjective design of permanent magnet generator with dimensionality reduction in criteria space

Woźniak Piotr

Przegląd Elektrotechniczny

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2020

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R. 96, nr 7

86--91

EN

This paper addresses the problem of dimensionality reduction while preserving the characteristics of the Pareto set approximation in multiobjective optimization. The real-life engineering design problem for permanent magnet generator is considered. The Pareto front approximations with constraints, ranging from the five objectives to the set of two, are presented and compared.

PL

W artykule przedstawiono rozwiązanie problemu optymalizacji wielokryterialnej poprzez redukcję wymiarów w przestrzeni kryteriów. Rozważono właściwości zbioru Pareto w zadaniu projektowania generatora z magnesami trwałymi. Zaprezentowano i porównano aproksymacje frontu Pareto przy optymalizacji z ograniczeniami przy redukcji z pięciu dwóch kryteriów.

8

Multiobjective global optimization of mechanical systems with cracks

Beluch Witold, Dlugosz Adam

Journal of Theoretical and Applied Mechanics

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2020

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Vol. 58 nr 2

553--564

EN

The paper is devoted to the multiobjective shape optimization of cracked structures. The two main goals are: reduction of the negative crack influence of identified cracks and optimal design of structural elements to reduce the risk of crack occurrence and growth. NURBS (Non-Uniform Rational B-Splines) curves are used to model the structure boundaries. Global optimization methods in the form of evolutionary algorithms are employed. As different optimization criteria are considered simultaneously, the efficient multiobjective optimization method are applied. An in-house multiobjective evolutionary algorithm is proposed as an efficient optimization tool. The dual boundary element method is used to solve the boundaryvalue problem.

9

A practical pretreatment planning method of multiple puncturing for thermal ablation surgery

Li Jing, Xu Yuanqi, Shen Nanyan, Feng Lanyun, Ran Zhuang, Deng Zongqian

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 4

1469--1485

EN

Thermal ablation surgery serves as one of the main approaches to treat liver tumors. The pretreatment planning, which highly demands the experience and ability of the physician, plays a vital role in thermal ablation surgery. The planning of multiple puncturing is necessary for avoiding the possible interference, destroying the tumor thoroughly and minimizing the damage to healthy tissue. A GPU-independent pretreatment planning method is proposed based on multi-objective optimization, which takes the most comprehensive constraints into consideration. An adaptive decision method of closing kernel size based on Jenks Natural Breaks is utilized to describe the final feasible region more accurately. It should be noted that the reasonable procedure of solving the feasible region and the use of KD tree based high dimensional search approach are used to enhance the computational efficiency. Seven constraints are handled within 7 s without GPU acceleration. The Pareto front points of nine puncturing tests are obtained in 5 s by using the NSGA-II algorithm. To evaluate the maximum difference and similarity between the planning results and the puncturing points recommended by the physician, Hausdorff distance and overlap rate are respectively developed, the Hausdorff distances are within 30 mm in seven out of nine tests and the average value of overlap rate is 73.0% for all the tests. The puncturing paths of high safety and clinical-practice compliance can be provided by the proposed method, based on which the pretreatment planning software developed can apply to the interns' training and ability evaluating for thermal ablation surgery.

10

Wybrane przykłady zadań optymalizzacji wielokryterialnej w planowaniu sieci WLAN

Pieprzycki Adam, Ludwin Wiesław

Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne

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2019

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nr 7

699-704, CD

PL

Celem artykułu była analiza wielokryterialnego podejścia do planowania sieci łączności bezprzewodowej WLAN (Wireless Local Area Network) IEEE 802.11b/g z wykorzystaniem wybranych rojowych algorytmów optymalizacji. W procesie poszukiwania ekstremów wybranych dwóch i więcej funkcji kryterialnych zastosowano dwa algorytmy rojowe: kukułki MOCS (Multi Objective Cuckoo Search) oraz optymalizacji rojem cząstek MOPSO (Multi Objective Particle Swarm Optimisation). Dodatkowo, zaproponowano wykorzystanie oceny globalnej uzyskanych rozwiązań z zastosowaniem Metody Unitaryzacji Zerowanej MUZ.

EN

The aim of the article is analyze the multicriteria approach to IEEE 802.11b/g wireless LAN planning using selected swarm optimization methods. For this purpose, in the search extremes for two and three objective functions that applied two swarm algorithms: MOCS and MOPSO. In addition, it was proposed to perform a global assessment of solutions using the zero unitarisation method MUZ, the best results were furtheranalysed with performance metric (PM).

11

Racjonalne i optymalne rozmieszczenie infrastruktury zielonej na terenach zurbanizowanych

McGarity Arthur, Dąbrowski Wojciech

Instal

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2019

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nr 12

44--47

PL

Wielokryterialna optymalizacja pozwala na takie rozlokowanie różnych rodzajów infrastruktury zielonej, aby uzyskać najkorzystniejsze relacje pomiędzy kosztami i wszystkimi rozpatrywanymi korzyściami. Takie zadania są obecnie formułowane i rozwiązywane w różnych miejscach na świecie. Opisano tutaj model opracowany przez Agencję Ochrony Środowiska USA i nazywany SUSTAIN i kolejny przez grupę badawczą składającą się głównie z pracowników Swarthmore College oraz Uniwersytetu Johna Hopkinsa i zastosowany w projekcie infrastruktury zielonej obejmującym część miasta Filadelfia. Głównym powodem wybudowania tej infrastruktury była potrzeba zmniejszenia ilości ścieków i ładunków zanieczyszczeń zrzucanych przez ogólnospławny przelew burzowy.

EN

Multiobjective optimization methods are being used to predict the most efficient allocation of financial sources in order to achieve the best trade-off solution that balances costs and the various benefits. These methods have been applied in several locations around the world. The approach developed by the U. S. EPA, called SUSTAIN, which uses heuristic evolutionary optimization, is briefly described here. Also, a simplified multiobjective approach, called StormWISE, which uses linear programming, has been applied to reduce combined sewer overflows (CSO) in Philadelphia. Both approaches are briefly described.

12

Efficient Approaches for Solving a Multiobjective Energy-aware Job Shop Scheduling Problem

González Miguel A., Oddi Angelo, Rasconi Riccardo

Fundamenta Informaticae

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2019

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Vol. 167, nr 1-2

93--132

EN

One of the most recent and interesting trends in intelligent scheduling is trying to reduce the energy consumption in order to obtain lower production costs and smaller carbon footprint. In this work we consider the energy-aware job shop scheduling problem, where we have to minimize at the same time an efficiency-based objective, as is the total weighted tardiness, and also the overall energy consumption. We experimentally show that we can reduce the energy consumption of a given schedule by delaying some operations, and to this end we design a heuristic procedure to improve a given schedule. As the problem is computationally complex, we design three approaches to solve it: a Pareto-based multiobjective evolutionary algorithm, which is hybridized with a multiobjective local search method and a linear programming step, a decomposition-based multiobjective evolutionary algorithm hybridized with a single-objective local search method, and finally a constraint programming approach. We perform an extensive experimental study to analyze our algorithms and to compare them with the state of the art.

13

Multi-criteria analysis on spatial data of the landscape

Jarosz Paweł, Łabędź Paweł, Ozimek Paweł

Computer Assisted Methods in Engineering and Science

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2019

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Vol. 26, no. 2

113--121

EN

The method presented in this paper allows the visualization of spatial relations that could not be obtainedusing existing methods. Depending on the component maps that represent the analysis criteria, it ispossible to indicate certain ways to develop good or bad areas. These maps may contain visibility dataas well as other types of spatial impact. Thanks to this, spatial decision criteria can be broad and multi-branch.

14

Gene selection from large-scale gene expression data based on fuzzy interactive multi-objective binary optimization for medical diagnosis

Shahbeig S., Rahideh A., Helfroush M. S., Kazemi K.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 2

313--328

EN

An efficient fuzzy interactive multi-objective optimization method is proposed to select the sub-optimal subset of genes from large-scale gene expression data. It is based on the binary particle swarm optimization (BPSO) algorithm tuned by a chaotic method. The proposed method is able to select the sub-optimal subset of genes with the least number of features that can accurately distinguish between the two classes, e.g. the normal and cancerous samples. The proposed method is evaluated on several publicly available microarray and RNA-sequencing gene expression datasets such as leukemia, colon cancer, central nervous system, lung cancer, ovarian cancer, prostate cancer and RNA-seq lung disease. The results indicate that the proposed method can identify the minimum number of genes to achieve the most accuracy, sensitivity and specificity in the classification process. Achieving 100% accuracy in six out of the seven datasets investigated in this study, demonstrates the high capacity of the proposed algorithm to find the sub-optimal subset of genes. This approach is useful in clinical applications to extract the most influential genes on a disease and to find the treatment procedure for the disease.

15

Stochastic fractal based multiobjective fruit fly optimization

Zuo C., Wu L., Zeng Z. F., Wei H. L.

International Journal of Applied Mathematics and Computer Science

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2017

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Vol. 27, no. 2

417--433

EN

The fruit fly optimization algorithm (FOA) is a global optimization algorithm inspired by the foraging behavior of a fruit fly swarm. In this study, a novel stochastic fractal model based fruit fly optimization algorithm is proposed for multiobjective optimization. A food source generating method based on a stochastic fractal with an adaptive parameter updating strategy is introduced to improve the convergence performance of the fruit fly optimization algorithm. To deal with multiobjective optimization problems, the Pareto domination concept is integrated into the selection process of fruit fly optimization and a novel multiobjective fruit fly optimization algorithm is then developed. Similarly to most of other multiobjective evolutionary algorithms (MOEAs), an external elitist archive is utilized to preserve the nondominated solutions found so far during the evolution, and a normalized nearest neighbor distance based density estimation strategy is adopted to keep the diversity of the external elitist archive. Eighteen benchmarks are used to test the performance of the stochastic fractal based multiobjective fruit fly optimization algorithm (SFMOFOA). Numerical results show that the SFMOFOA is able to well converge to the Pareto fronts of the test benchmarks with good distributions. Compared with four state-of-the-art methods, namely, the non-dominated sorting generic algorithm (NSGA-II), the strength Pareto evolutionary algorithm (SPEA2), multi-objective particle swarm optimization (MOPSO), and multiobjective self-adaptive differential evolution (MOSADE), the proposed SFMOFOA has better or competitive multiobjective optimization performance.

16

Multi-objective binary DE algorithm for optimizing the performance of Devanagari script-based P300 speller

Chaurasiya R. K., Londhe N. D., Ghosh S.

Biocybernetics and Biomedical Engineering

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2017

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Vol. 37, no. 3

422--431

EN

P300 speller-based brain-computer interface (BCI) allows a person to communicate with a computer using only brain signals. In order to achieve better reliability and user continence, it is desirable to have a system capable of providing accurate classification with as few EEG channels as possible. This article proposes an approach based on multi-objective binary differential evolution (MOBDE) algorithm to optimize the system accuracy and number of EEG channels used for classification. The algorithm on convergence provides a set of pareto-optimal solutions by solving the trade-off between the classification accuracy and the number of channels for Devanagari script (DS)-based P300 speller system. The proposed method is evaluated on EEG data acquired from 9 subjects using a 64 channel EEG acquisition device. The statistical analysis carried out in the article, suggests that the proposed method not only increases the classification accuracy but also increases the over-all system reliabil-ity in terms of improved user-convenience and information transfer rate (ITR) by reducing the EEG channels. It was also revealed that the proposed system with only 16 channels was able to achieve higher classification accuracy than a system which uses all 64 channel's data for feature extraction and classification.

17

New Design Method for the Formed Suction Intake in Axial-Flow Pumps with a Vertical Axis

Błaszczyk A., Papierski A., Susik M.

Mechanics and Mechanical Engineering

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2016

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Vol. 20, nr 4

553--568

EN

A concept of the formed suction intake design obtained with an algorithm for vertical axial{ ow pumps is presented. The design methodology is a part of works conducted within project no. N N513 460240 supported by the Polish National Science Center. The proposed procedure is used to optimize intakes. The results of steady ow numerical computations in the suction intake as well as applications of the design optimization in the aspect of ful lling two objective functions are discussed. The objective functions given by the authors concern the optimal in ow of the uid into the impeller.

18

Multiobjective and multiscale optimization of composite materials by means of evolutionary computations

Beluch W., Długosz A.

Journal of Theoretical and Applied Mechanics

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2016

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Vol. 54 nr 2

397--409

EN

The paper deals with the multiobjective and multiscale optimization of heterogeneous structures by means of computational intelligence methods. The aim of the paper is to find optimal properties of composite structures in a macro scale modifying their microstructure. At least two contradictory optimization criteria are considered simultaneously. A numerical homogenization concept with a representative volume element is applied to obtain equivalent macro-scale elastic constants. An in-house multiobjective evolutionary algorithm MOOPTIM is applied to solve the considered optimization tasks. The finite element method is used to solve the boundary-value problem in both scales. A numerical example is attached.

19

Multiobjective optimization in two-scale thermoelastic problems for porous solids

Długosz A., Schlieter T.

Engineering Transactions

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2016

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Vol. 64, iss. 4

449--456

EN

The multiobjective optimization of a two-scale thermoelastic problem is considered in this paper. To compute the solutions, direct thermoelastic analysis with the representative volume element (RVE) and the finite element method (FEM) analysis are performed. Evolutionary algorithms (EAs) are used to find a set of Pareto-optimal solutions. The design variables of the optimization problem are defined so as to describe the microstructure of a porous solid, whereas the optimization criteria are defined on the basis of macro-scale thermal and mechanical quantities. A numerical example of optimization is included.

20

A Nash equilibrium approach for multiobjective optimal control problems with elliptic partial diﬀerential equations

Dreves A.

Control and Cybernetics

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2016

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Vol. 45, no. 4

457--482

EN

We consider the generalized Nash equilibrium as a solution concept for multiobjective optimal control problems governed by elliptic partial diﬀerential equations with constraints not only for the control but also for the state variables. In the ﬁrst part, we present a constructive proof of the existence of a generalized Nash equilibrium via an approximating sequence of suitable ﬁnite dimensional discretizations. In the second part, we propose a variant of a potential reduction algorithm for the numerical solution of these discretized problems. In contrast to the existing numerical approaches ours does not require the computation of the control–to–state mapping. Instead we introduce diﬀerent state variables and guarantee that they become equal at a solution. We prove suﬃcient conditions for the convergence of our algorithm to a solution. Furthermore, some numerical results showing the applicability are provided.