Wyniki wyszukiwania - BazTech

1

Quantum inspired chaotic salps warm optimization for dynamic optimization

Pathak Sanjai, Mani Ashish, Sharma Mayank, Chatterjee Amlan

Computer Science

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2024

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T. 25 (2)

301--326

EN

Many real-world problems are dynamic optimization problems that are un-known before hand. In practice, unpredict able events such as the arrival of new jobs, due date changes, and reservation cancellations, changes in parameters or constraints make the search environment dynamic. Many algorithms are designed to deal with stationary optimization problems, but these algorithms do not face dynamic optimization problems or manage them correctly. Although some optimization algorithms are proposed to deal with the changes in dynamic environments differently, there are still areas of improvement in existing algorithms due to limitations or drawbacks, especially in terms of locating and following the previously identified optima. With this in mind, westudied a variant of SSA known as QSSO, which integrates the principles of quantum computing. An attempt is made to improve the overall performance of standard SSA to deal with the dynamic environment effectively by locating and tracking the global optima for DOPs. This work is an extension of the proposed new algorithm QSSO, known as the Quantum-inspired Chaotic SalpSwarm Optimization (QCSSO) Algorithm, which details the various approaches considered while solving DOPs. A chaotic operator is employed with quantum computing to respond to change and guarantee to increase individual searcha-bility by improving population diversity and the speed at which the algorithm converges. We experimented by evaluating QCSSO on a well-known generalized dynamic benchmark problem (GDBG) provided for CEC 2009, followed by a comparative numerical study with well-regarded algorithms. As promised, the introduced QCSSO is discovered as the rival algorithm for DOPs.

2

Improving the insulin therapy for diabetic patients using optimal impulsive disturbance rejection: Continuous time approach

Dodek Martin, Miklovičová Eva, Halás Miroslav

Biocybernetics and Biomedical Engineering

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2024

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Vol. 44, no. 2

414--430

EN

The paper proposes a new model-based optimization approach to improve the clinical efficiency of compensatory insulin bolus treatment in diabetic patients, aiming to mitigate the consequences of diabetes. The most important contribution of this paper is a novel methodology for determining the optimal parameters of insulin treatment, namely the size and timing of insulin boluses, to effectively compensate for carbohydrate intake. This concept can be seen as the so-called optimal model-based bolus calculator. The presented theoretical framework deals with the problem of optimal disturbance rejection in impulsive systems by minimizing an integral quadratic cost function. The methodology considers a personalized empirical transfer function model with static gains and time constants as the only parameters assumed to be known, making the bolus calculator more straightforward to implement in clinical practice. Contrary to other techniques, the proposed methodology considers impulsive insulin administration in the form of boluses, which is more feasible than continuous infusion. In contrast to the conventional bolus calculator, the proposed algorithm allows for maximizing therapy performance by optimizing the relative time of insulin bolus administration with respect to carbohydrate intake. Another feature to highlight is that the solution of the optimization problem can be obtained analytically, hence no numerical iterative solvers are required. Additionally, the continuous-time domain approach allows for a much finer adjustments of the insulin administration timing compared to discrete-time models. The proposed approach was validated in an in-silico study, which demonstrated the importance of systematically determined insulin-carbohydrate ratio and the relative delay between disturbance and its compensation. The results showed that the proposed optimal bolus calculator outperforms the traditional suboptimal formula.

3

Application of dynamic optimization for autonomous vehicle motion control

Brzozowski Michał, Drąg Łukasz

Transport Problems

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2023

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T. 18, z. 2

209--222

EN

This article presents a procedure algorithm and vehicle dynamics models that can be applied to planning and controlling the motion of an autonomous car. The simulation results obtained using a simplified bicycle model with three degrees of freedom and a spatial model with 10 degrees of freedom were compared. The numerical efficiency of both models was evaluated. The task of dynamic optimization was formulated, the solution to which enables the implementation of lane change and overtaking maneuvers. The task was solved using the bicycle model, and the results (implementation of the intended maneuver) were validated using the spatial model.

4

Travel management optimization based on air pollution condition using Markov decision process and genetic algorithm (case study: Shiraz city)

Bagheri Mohammad, Ghafourian Hossein, Kashefiolasl Morteza, Pour Mohammad Taghi Sadati, Rabbani Mohammad

Archives of Transport

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2020

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Vol. 53, iss. 1

89--102

EN

Currently, air pollution and energy consumption are the main issues in the transportation area in large urban cities. In these cities, most people choose their transportation mode according to corresponding utility including traveller's and trip’s characteristics. Also, there is no effective solution in terms of population growth, urban space, and transportation demands, so it is essential to optimize systematically travel demands in the real network of roads in urban areas, especially in congested areas. Travel Demand Management (TDM) is one of the well-known ways to solve these problems. TDM defined as a strategy that aims to maximize the efficiency of the urban transport system by granting certain privileges for public transportation modes, Enforcement on the private car traffic prohibition in specific places or times, increase in the cost of using certain facilities like parking in congested areas. Network pricing is one of the most effective methods of managing transportation demands for reducing traffic and controlling air pollution especially in the crowded parts of downtown. A little paper may exist that optimize urban transportations in busy parts of cities with combined Markov decision making processes with reward and evolutionary-based algorithms and simultaneously considering customers’ and trip’s characteristics. Therefore, we present a new network traffic management for urban cities that optimizes a multi-objective function that related to the expected value of the Markov decision system’s reward using the Genetic Algorithm. The planned Shiraz city is taken as a benchmark for evaluating the performance of the proposed approach. At first, an analysis is also performed on the impact of the toll levels on the variation of the user and operator cost components, respectively. After choosing suitable values for the network parameters, simulation of the Markov decision process and GA is dynamically performed, then the optimal decision for the Markov decision process in terms of total reward is obtained. The results illustrate that the proposed cordon pricing has significant improvement in performance for all seasons including spring, autumn, and winter.

5

Heuristic Adaptive Control of Waste Gas Exhausting in Coke Making

Kačur Ján, Flegner Patrik, Durdán Milan, Laciak Marek

Advances in Science and Technology. Research Journal

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2020

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Vol. 14, no. 4

195--212

EN

Stabilization of the carbon monoxide (CO) in the waste gas is a common technical problem in many industrial plants. Stabilization can be performed continuously by regulating the fuel input or by regulating the exhaust gas draught. This paper proposes an adaptive control system for CO stabilization in waste gases based on a discrete controller. Heuristic adaptation of a discrete controller is based on continuous optimization of controller parameters. The advantage of this solution is that the control system does not need to perform the identification of the controlled system repeatedly. The parameters of the controller are dynamically optimized during the production process. By regulating the under-pressure, we change the amount of air supplied to the combustion chambers, which affects the combustion of gaseous fuel and also the concentration of CO in the waste flue gas. The control algorithm was verified for the combustion process in coke making. The proposed control achieved good stabilization quality when verified in simulation and also in an industry operation. The CO level at which the waste gas temperature was highest was selected as the setpoint. It was found that the stabilization of CO in waste gas to lower values is possible to achieve higher waste gas temperature and by that, higher temperatures in heating chambers.

6

Driver comfort improvement by a selection of optimal springing of a seat

Tengler S., Warwas K.

Technical Transactions

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2017

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Vol. 5(114)

217--235

EN

An optimal way to select parameters of a driver’s seat of a special vehicle, which contributes directly to an increase in driving comfort is presented in the work. A mathematical model of the vehicle was formulated by using of joint coordinates and homogenous transformations. A maneuver of driving over obstacles in a form of a speed bump of different heights and lengths is presented. A subject of the investigations was to select such damping parameters and driver’s seat stiffness to minimize amplitudes of vibrations present in this subassembly. An influence of a form of an objective function and a selection of decisive variables on quality of the optimization results obtained was analyzed.

PL

W pracy przedstawiono sposób doboru optymalnych parametrów fotela kierowcy pojazdu specjalnego, który bezpośrednio przyczynia się do zwiększenia komfortu jazdy. Model matematyczny pojazdu sformułowano korzystając ze współrzędnych złączowych i przekształceń jednorodnych. Przedstawiono manewr przejazdu przez przeszkody w postaci progów zwalniających o różnej wysokości i długości. Przedmiotem badań było dobranie takich parametrów tłumienia i sztywności fotela kierowcy, aby zminimalizować amplitudy drgań występujące w tym podzespole. W pracy skoncentrowano się na analizie wpływu postaci funkcji celu oraz doboru zmiennych decyzyjnych na jakość uzyskanych wyników optymalizacyjnych.

7

Evolutionary multi-agent systems in non-stationary environments

Kisiel-Dorohinicki M.

Computer Science

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2013

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Vol. 14 (4)

563--575

EN

In this article, the performance of an evolutionary multi-agent system in dynamic optimization is evaluated in comparison to classical evolutionary algorithms. The starting point is a general introduction describing the background, structure and behavior of EMAS against classical evolutionary techniques. Then, the properties of energy-based selection are investigated to show how they may influence the diversity of the population in EMAS. The considerations are illustrated by experimental results based on the dynamic version of the well-known, high-dimensional Rastrigin function benchmark.

8

Optimisation of operating conditions in fed-batch baker’s yeast fermentation

Atasoy I., Yuceer M., Berber R.

Chemical and Process Engineering

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2013

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Vol. 34, nr 1

175--186

EN

Saccharamyces cerevisia known as baker’s yeast is a product used in various food industries. Worldwide economic competition makes it a necessity that industrial processes be operated in optimum conditions, thus maximisation of biomass in production of saccharamyces cerevisia in fedbatch reactors has gained importance. The facts that the dynamic fermentation model must be considered as a constraint in the optimisation problem, and dynamics involved are complicated, make optimisation of fed-batch processes more difficult. In this work, the amount of biomass in the production of baker’s yeast in fed-batch fermenters was intended to be maximised while minimising unwanted alcohol formation, by regulating substrate and air feed rates. This multiobjective problem has been tackled earlier only from the point of view of finding optimum substrate rate, but no account of air feed rate profiles has been provided. Control vector parameterisation approach was applied the original dynamic optimisation problem which was converted into a NLP problem. Then SQP was used for solving the dynamic optimisation problem. The results demonstrate that optimum substrate and air feeding profiles can be obtained by the proposed optimisation algorithm to achieve the two conflicting goals of maximising biomass and minimising alcohol formation.

9

Planar arm movement trajectory formation: an optimization based simulation study

Zadravec M., Matjačić Z.

Biocybernetics and Biomedical Engineering

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2013

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Vol. 33, no. 2

106--117

EN

Rehabilitation of post stroke patients with upper extremity motor deficits is typically focused on relearning of motor abilities and functionalities requiring interaction with physiotherapists and/or rehabilitation robots. In a point-to-point movement training, the trajectories are usually arbitrarily determined without considering the motor impairment of the individual. In this paper, we used an optimal control model based on arm dynamics enabling also incorporation of muscle functioning constraints (i.e. simulation of muscle tightness) to find the optimal trajectories for planar arm reaching movements. First, we tested ability of the minimum joint torque cost function to replicate the trajectories obtained in previously published experimental trials done by neurologically intact subjects, and second, we predicted the optimal trajectories when muscle constraints were modeled. The resulting optimal trajectories show considerable similarity as compared to the experimental data, while on the other hand, the muscle constraints play a major role in determination of the optimal trajectories for stroke rehabilitation.

10

Self-Adaptive Differential Evolution with Hybrid Rules of Perturbation for Dynamic Optimization

Trojanowski K., Raciborski M., Kaczyński P.

Journal of Telecommunications and Information Technology

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2011

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nr 4

20-30

EN

In this paper an adaptive differential evolution approach for dynamic optimization problems is studied. A new benchmark suite Syringa is also presented. The suite allows to generate test-cases from a multiple number of dynamic optimization classes. Two dynamic benchmarks: Generalized Dynamic Benchmark Generator (GDBG) and Moving Peaks Benchmark (MPB) have been simulated in Syringa and in the presented research they were subject of the experimental research. Two versions of adaptive differential evolution approach, namely the jDE algorithm have been heavily tested: the pure version of jDE and jDE equipped with solutions mutated with a new operator. The operator uses a symmetric ?-stable distribution variate for modification of the solution coordinates.

11

Multi-swarm that learns

Trojanowski K.

Control and Cybernetics

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2010

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Vol. 39, no. 2

359-375

EN

This paper studies particle swarm optimization approach enriched by two versions of an extension aimed at gathering information during the optimization process. Application of these extensions, called memory mechanisms, increases computational cost, but it is spent to a benefit by incorporating the knowledge about the problem into the algorithm and this way improving its search abilities. The first mechanism is based on the idea of storing explicit solutions while the second one applies one-pass clustering algorithm to build clusters containing search experiences. The main disadvantage of the former mechanism is lack of good rules for identification of outdated solutions among the remembered ones and as a consequence unlimited growth of the memory structures as the optimization process goes. The latter mechanism uses other form of knowledge representation and thus allows us to control the amount of allocated resources more efficiently than the former one. Both mechanisms have been experimentally verified and their advantages and disadvantages in application for different types of optimized environments are discussed.

12

Multi-Swarm That Learn

Trojanowski K.

Prace Instytutu Podstaw Informatyki Polskiej Akademii Nauk

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2009

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Nr 1015

1-24

EN

In this paper a dynamic optimization with particle swarm approach using two different memory mechanisms is studied. One of them is based on the idea of storing explicit solutions in memory structures while the other applies one-pass clustering algorithm to build clusters containing search experiences. Both mechanisms have been experimentally verified and their advantages and disadvantages in application for different types of testing environments have been discussed.

PL

Artykuł zawiera wyniki badań dwóch mechanizmów pamięciowych stosowanych w roju cząsteczek do optymalizacji dynamicznej. Jeden z nich jest oparty na zasadzie gromadzenie gotowych rozwiązań w strukturach pamięci, natomiast drugi stosuje jednoprzejściowy algorytm do budowy klastrów, w których mogłyby być przechowywane doświadczenia zdobywane w trakcie procesu szukania. Obydwa mechanizmy zostały zweryfikowane w badaniach eksperymentalnych a ich wady i zalety objawiające się w zastosowaniach do różnych typów zadań zostały omówione.

13

Object library of algorithms for dynamic optimization problems; Benchmarking SQP and nonlinear interior point methods

Błaszczyk J., Karbowski A., Malinowski K.

International Journal of Applied Mathematics and Computer Science

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2007

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Vol. 17, no. 4

515-537

EN

The main purpose of this paper is to describe the design, implementation and possibilities of our object-oriented library of algorithms for dynamic optimization problems. We briefly present library classes for the formulation and manipulation of dynamic optimization problems, and give a general survey of solver classes for unconstrained and constrained optimization. We also demonstrate methods of derivative evaluation that we used, in particular automatic differentiation. Further, we briefly formulate and characterize the class of problems solved by our optimization classes. The solution of dynamic optimization problems with general constraints is performed by transformation into structured large-scale nonlinear programming problems and applying methods for nonlinear optimization. Two main algorithms of solvers for constrained dynamic optimization are presented in detail: the sequential quadratic programming (SQP) exploring the multistage structure of the dynamic optimization problem during the solution of a sequence of quadratic subproblems, and the nonlinear interior-point method implemented in a general-purpose large-scale optimizer IPOPT. At the end, we include a typical numerical example of the application of the constrained solvers to a large-scale discrete-time optimal control problem and we use the performance profiles methodology to compare the efficiency and robustness of different solvers or different options of the same solver. In conclusions, we summarize our experience gathered during the library development.

14

Metody numeryczne w optymalizacji dynamicznej kosztów produkcji

Kwiatkowska-Murzyn A., Galicki M.

Management

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2004

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Vol. 8, no. 2

175-186

PL

Głównym problemem prezentowanym w na łamach danego artykułu jest ustalenie strategii decyzyjnej przedsiębiorstwa produkcyjnego ze względu na kryterium optymalnych kosztów, w oparciu o metody zapożyczone z teorii sterowania i analizy matematycznej, tj. zasadę maksimum Pontriagina i metodę strzałów. Rozwiązanie przedstawionego zagadnienia wymagało przede wszystkim sformułowania modelu matematycznego procesu produkcyjnego, w postaci układu równań różniczkowych zmiennych stanu, co w danym zadaniu określają: stan magazynu x1 (t) i stan zobowiązań x2 (t), które to zmienne powiązano odpowiednio ze zmiennymi sterującymi reprezentowanymi przez wielkość produkcji u1 (t) i kwotę spłaty zobowiązań u2 (t). Natomiast parametry stałe budowanego modelu, zostały wyznaczone w oparciu o dynamiczną sieć neuronową, dla której procedura optymalizacyjna funkcji błędu ze względu na wektor optymalnych wag (parametrów stałych) została zaprogramowana w oparciu o alorytm Levenberga-Marquardta. Zastosowanie pierwszej z zaproponowanych metod - zasady maksimum Pontraiagina wymagało, obok sformułowania modelu, wyznaczenia również funkcji celu ukazującej kosztowe powiązania między zmiennymi oraz uwzględnienia ograniczeń, które jednak pominięto ze względu na przyjęte w pracy założenia upraszczające. Ideę ww. metody sprowadzono zatem do sprawdzenia warunku koniecznego maksymalizacji Hamiltonianu, którego efekt w postaci równań kierunkowych zmiennych sterujących posłużył do wyznaczenia swoistej, zamkniętej postaci układu równań różniczkowych. Ostateczne rozwiązanie problemu w postaci optymalnych ścieżek zmiennych sterujących, nastąpiło w oparciu o metodę strzałów, w efekcie rozwiązania n-razy zagadnienia początkowego z uwzględnieniem specyficznych dla problemów brzegowych warunków transwersalności. Pracę kończy interpretacja ekonomiczna wyników uzyskanych na podstawie sekwencji ww. metod wraz ze wskazaniem kierunków ich ewentualnej poprawy.

EN

The main problem presented in the publication is to establish parameters of the production control to optimize total production expenses with the use of theory of control methods. To solve the problem a mathematical model for the production process has been created and the occurring phenomena were described by mathematical equations. The model comprises variables which can be divided into variables describing the state of the production (state variables) and the ones that control the process (governing dynamics). Constant parameters of the equations have been established on the basis of a dynamic neural network, where the optimal weighs have been established with the use of the error function minimization procedure basing on Levenberg-Marquardt algorithm. Applying Pontriagin's optimization method has also required the target function to comprise all of the model variabies. Whereas, limitations have been determined only for informational purposes. The closed form of the differential equation system, which is the base for the subsequent calculations, has been determined by suitable substitution of the function of governing dynamics into differential equations for state variables and corresponding equations for movement of conjugated variables. The mathematical solution to the problem has consisted in determination of optimal paths of state variables, which has been performed with the use of the shot method. The paper is folIowed by conclusions and final remarks referring to the obtained results together with the economic interpretation and determination of directions of improvements of the optimizing procedure.

15

Optymalizacja dynamiczna w identyfikacji sił mięśniowych podczas chodu

Michnik R.

Zeszyty Naukowe Katedry Mechaniki Stosowanej / Politechnika Śląska

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2002

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z. 19

105-110

PL

W pracy opracowano model matematyczny opisujący ruch oraz mechanizmy sterowania ruchem ciała człowieka podczas realizowania podstawowych form lokomocji. Ciało człowieka zamodelowano jako układ siedmiu członów sztywnych, połączonych przegubowo stawami i poruszających się ruchem płaskim. Opracowany model matematyczny układu szkieletowo-mięśniowego ciała składa się z trzech podstawowych elementów: modelu układu szkieletowego, modelu kontaktu stopy z podłożem, modelu mięśni. W procesie identyfikacji sformułowano zadanie optymalizacji dynamicznej, które rozwiązano za pomocą algorytmów genetycznych.

EN

A model describing movement and systems of control of this movement is presented in this paper. The human body is modelled as a system of seven rigid segments connected with each other by joints and moves in one plane. The mathematical model of human body consists of three elements: model of skeletal system, model of ground reactions and model of muscles. The human body is modelled as a system of seven rigid segments connected with each other by joints and moves in one plane. The optimisation task was formulated in the identification process and solved with the help of genetic algorithms.