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EN
Reducing contaminant emissions is an important task of any industry, included the maritime one. In fact, in April 2018, IMO (International Maritime Organization) adopted an Initial Strategy on reduction of Greenhouse gas (GHG) emissions from ships. An essential part responsible for producing these emissions is the diesel engine. For that reason vessels include separation systems for heavy fuel oils. The purpose of this work is to improve the predictive maintenance techniques incorporating new intelligent approaches. An analysis of vibrations of this separation system was made and their characteristics were used in a Genetic Neuro-Fuzzy System in order to design an intelligent maintenance based on condition monitoring. The achieved results show that the proposed method provides an improvement since it indicates if a maintenance operation is necessary before the schedule one or if it could be possible extend the next maintenance service.
EN
The use of wind energy in water pumping is an economically viable and sustainable solution to rural communities without access to the electricity grid. The aim of this paper is to present a detailed modeling of the wind-powered pumping system, propose and compare some control schemes to optimize the performance of the system and enhance the quality of the generated power. The wind energy system used in this paper consists of a permanent magnet synchronous generator (PMSG) and static converters directly coupled to an asynchronous motor that drives a centrifugal pump. A typical control is applied to the proposed configuration for the purpose of controlling the generator to extract maximum wind power. Furthermore, four types of controllers (PI and conventional RST polynomials, adaptive RST-fuzzy and genetic algorithm are designed for the wind energy system and tested under various operating conditions.
PL
Wykorzystanie energii wiatru w pompowaniu wody jest opłacalnym i zrównoważonym rozwiązaniem dla społeczności wiejskich bez dostępu do sieci elektrycznej. Celem tego artykułu jest przedstawienie szczegółowego modelowania systemu pompowania napędzanego wiatrem, zaproponowanie i porównanie niektórych schematów sterowania, aby zoptymalizować wydajność systemu i poprawić jakość generowanej mocy. System energii wiatrowej zastosowany w tym artykule składa się z synchronicznego generatora z magnesami trwałymi (PMSG) i przekształtników statycznych bezpośrednio sprzężonych z silnikiem asynchronicznym, który napędza pompę odśrodkową. Typowe sterowanie jest stosowane do proponowanej konfiguracji w celu sterowania generatorem w celu wydobycia maksymalnej energii wiatru. Ponadto cztery typy sterowników (PI i konwencjonalne wielomiany RST, adaptacyjny algorytm rozmytego RST i genetyczny) są zaprojektowane dla systemu energii wiatrowej i testowane w różnych warunkach pracy).
3
EN
The paper presents the optimal design of the electric circuit of a Wireless Power Transfer Systems used to recharge the battery of an electric car. A field-circuit model is developed for the purpose of analysis, while a Pareto-like approach – based on SA-MNSGA-III and µ-BiMO, two nature-inspired algorithms - is used for synthesis. An excellent correspondence between the results obtained with the two methods was found. Then, the optimization algorithms could be applied successfully even in more complicated cases, such as WPTSs design.
PL
W artykule zaprezentowano projekt I optymalizację obwodu do bezprzewodowego transferu energii przeznaczonego do ładowania baterii samochodu elektrycznego. Wykorzystano algorytm Pareto. Uzyskano bardzo dobrą zgodność modelu z wynikami eksperymentu.
EN
This paper proposes a novel hybrid software/hardware system to automatically create filters for image processing based on genetic algorithms and mathematical morphology. Experimental results show that the hybrid system, implemented using a combination of a NIOS-II processor and a custom hardware accelerator in an Altera FPGA device, is able to generate solutions that are equivalent to the software version in terms of quality in approximately one third of the time.
PL
W artykule zaproponowano nowe hybrydowe oprogramowanie do automatycznego tworzenia filtrów grafiki bazuj ˛acych na algorytmach genetycznych i morfologii matematycznej. Eksperymenty wykazały ˙ze proponowany system wykorzystuj ˛acy procesor NIOS-II i Altera FPGA jest w stanie generowa´c rozwi ˛azanie niemal trzy razy szybciej ni˙z dotychczas stosowane systemy.
EN
Cabin placement layout is an important part of ship cabin layout design. A good cabin placement layout can improve the efficiency of the ship’s cabin arrangement. However, optimisation of the layout of cabin placement is not widely studied and more often relies on the experience of the staff. Thus, a novel methodology combining systematic layout planning and a genetic algorithm to optimise the cabin placement is presented in this paper. First key elements are converted by a systematic planning method that is often applied in factory layout, and a preliminary cabin placement layout model is established according to these key elements. Then the circulation strength and adjacency strength are taken as sub-objectives to establish a mathematical model, and an improved genetic algorithm is used to optimise the model. The result of the optimisation is compared with the initial schemes to verify the validity of the algorithm. Finally, the human factors are introduced according to the actual situation. The AHP method is used to select the layout scheme of the cabin that is most likely to be applied in the actual cabin layout.
EN
Scheduling of multiobjective problems has gained the interest of the researchers. Past many decades, various classical techniques have been developed to address the multiobjective problems, but evolutionary optimizations such as genetic algorithm, particle swarm, tabu search method and many more are being successfully used. Researchers have reported that hybrid of these algorithms has increased the efficiency and effectiveness of the solution. Genetic algorithms in conjunction with Pareto optimization are used to find the best solution for bi-criteria objectives. Numbers of applications involve many objective functions, and application of the Pareto front method may have a large number of potential solutions. Selecting a feasible solution from such a large set is difficult to arrive the right solution for the decision maker. In this paper Pareto front ranking method is proposed to select the best parents for producing offspring’s necessary to generate the new populations sets in genetic algorithms. The bi-criteria objectives minimizing the machine idleness and penalty cost for scheduling process is solved using genetic algorithm based Pareto front ranking method. The algorithm is coded in Matlab, and simulations were carried out for the crossover probability of 0.6, 0.7, 0.8, and 0.9. The results obtained from the simulations are encouraging and consistent for a crossover probability of 0.6.
EN
Background: Under conditions of digital transformation, the effective decision-making process should involve the usage of different mathematical models and methods, one of which is the transportation problem. The transportation problem, as the problem of resource allocation, is applicable in such domains as manufacturing, information technologies, etc. To get more precise solutions, the multi-index transportation problem can be applied, which allows taking into account several variables. Methods: This paper develops an approach for applying the genetic algorithm for solving four-index transportation problems. Results: The steps of the genetic algorithm for solving four-index transportation problems are outlined. The research has proved the steps of the genetic algorithm to be the same for all four-index transportation problem types, except for the first step (initialization), which is described for every type of transportation problem separately. Based on the theoretical results, the program implementation of the genetic algorithm for solving four-index symmetric transportation problems has been developed with the open-source programming language typescript. Conclusions: The paper promotes the application of the genetic algorithm for solving multi-index transportation problems. The investigated problem requires comprehensive studies, specifically, on the influence of change different parameters of the genetic algorithm (population size, the mutation, and crossover rates, etc.) on the efficiency of the algorithm in solving four-index transportation problems.
PL
Wstęp: W warunkach komputerowej transformacji, efektywny proces podejmowania decyzji powinien obejmować wykorzystania modeli metod matematycznych. Przykładem takiej sytuacji jest problem transportowy, który jest problemem alokacji zasobów, występujący w takich obszarach jak produkcji, technologie informatyczne, itp. W celu uzyskania precyzyjniejszych rozwiązań, można zastosować wieloczynnikowy problem transportowy, który umożliwia uwzględnienie wielu zmiennych. Metody: W pracy zastosowano algorytm genetyczny dla rozwiązania czteroczynnikowych problemów transportowych. Wyniki: Wyszczególniono kroki algorytmu genetycznego dla czteroczynnikowego problem transportowego. Udowodnione, że kroki algorytmu genetycznego są takie same dla wszystkich typów czteroczynnikowych problemów transportowych, z wyjątkiem pierwszego kroku (inicjalizacji), który został opisany osobno dla każdego z typów problemu transportowego. W oparciu o wyniki teoretyczne, utworzono programowanie dla algorytmu genetycznego dla rozwiązywania czteroczynnikowych problemów transportowych przy użyciu opensourcowego języka typescript. Wnioski: W pracy zaproponowano zastosowanie algorytmu genetycznego dla rozwiązywania wieloczynnikowych problemów transportowych. Analizowany problem wymaga dalszych badań, szczególnie w zakresie wpływu zmian poszczególnych parametrów algorytmu genetycznego (wielkości populacji, mutacji, współczynnika podziału, itp.) na efektywność algorytmu w rozwiązywaniu czteroczynnikowych problemów transportowych.
EN
A lot of uncertainties and complexities exist in real life problem. Unfortunately, the world approaches such intricate realistic life problems using traditional methods which has failed to offer robust solutions. In recent times, researchers look beyond classical techniques. There is a model shift from the use of classical techniques to the use of standardized intelligent biological systems or evolutionary biology. Genetic Algorithm (GA) has been recognized as a prospective technique capable of handling uncertainties and providing optimized solutions in diverse area, especially in homes, offices, stores and industrial operations. This research is focused on the appraisal of GA and its application in real life problem. The scenario considered is the application of GA in 0-1 knapsack problem. From the solution of the GA model, it was observed that there is no combination that would give the exact weight or capacity the 35 kg bag can carry but the possible range from the solution model is 34 kg and 36 kg. Since the weight of the bag is 35 kg, the feasible or near optimal solution weight of items the bag can carry would be 34 kg at benefit of 16. Additional load beyond 34 kg could lead to warping of the bag.
EN
Computational Intelligence (CI) is a computer science discipline encompassing the theory, design, development and application of biologically and linguistically derived computational paradigms. Traditionally, the main elements of CI are Evolutionary Computation, Swarm Intelligence, Fuzzy Logic, and Neural Networks. CI aims at proposing new algorithms able to solve complex computational problems by taking inspiration from natural phenomena. In an intriguing turn of events, these nature-inspired methods have been widely adopted to investigate a plethora of problems related to nature itself. In this paper we present a variety of CI methods applied to three problems in life sciences, highlighting their effectiveness: we describe how protein folding can be faced by exploiting Genetic Programming, the inference of haplotypes can be tackled using Genetic Algorithms, and the estimation of biochemical kinetic parameters can be performed by means of Swarm Intelligence. We show that CI methods can generate very high quality solutions, providing a sound methodology to solve complex optimization problems in life sciences.
EN
Improving application efficiency is crucial for both the economic and environmental aspects of plant protection. Mathematical models can help in understanding the relationships between spray application parameters and efficiency, and reducing the negative impact on the environment. The effect of nozzle type, spray pressure, driving speed and spray angle on spray coverage on an artificial plant was studied. Artificial intelligence techniques were used for modeling and the optimization of application process efficiency. The experiments showed a significant effect of droplet size on the percent area coverage of the sprayed surfaces. A high value of the vertical transverse approach surface coverage results from coarse droplets, high driving speed, and nozzles angled forward. Increasing the vertical transverse leaving surface coverage, as well as the coverage of the sum of all sprayed surfaces, requires fine droplets, low driving speed, and nozzles angled backwards. The maximum coverage of the upper level surface is obtained with coarse droplets, low driving speed, and a spray angle perpendicular to the direction of movement. The choice of appropriate nozzle type and spray pressure is an important aspect of chemical crop protection. Higher upper level surface coverage is obtained when single flat fan nozzles are used, while twin nozzles produce better coverage of vertical surfaces. Adequate neural models and evolutionary algorithms can be used for pesticide application process efficiency optimization.
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.
PL
W artykule przedstawiono nowe narzędzie optymalizacyjne wspierające zarządzanie łańcuchem dostaw w aspekcie wielokryterialnym. To narzędzie zostało wdrożone w systemie EPLOS (Europejski Portal Usług Logistycznych). System EPLOS to zintegrowany system informatyczny wspierający proces tworzenia sieci dostaw i dystrybucji w łańcuchach dostaw. Ten system składa się z wielu modułów, np. moduł optymalizacji odpowiedzialny za przetwarzanie danych, generowanie wyników, moduł danych wejściowych, moduł kalibracji parametrów algorytmu optymalizacyjnego. Głównym celem badań było opracowanie systemu do określania parametrów łańcucha dostaw, które wpływają na jego efektywność w procesie zarządzania przepływem towarów między poszczególnymi ogniwami łańcucha. Parametry te zostały uwzględnione w modelu matematycznym jako zmienne decyzyjne w celu ustalenia ich w procesie optymalizacji. W modelu matematycznym zdefiniowano dane wejściowe adekwatne do analizowanego problemu, przedstawiono główne ograniczenia związane z wyznaczaniem efektywnego sposobu zarządzania łańcuchem dostaw oraz opisano funkcje kryterium. Problem zarządzania przepływem towarów w łańcuchu dostaw został przedstawiony w ujęciu wielokryterialnym. Ocenę efektywności zarządzania łańcuchem dostaw przeprowadzono na podstawie globalnej funkcji kryterium składającej się z częściowych funkcji kryteriów opisanych w modelu matematycznym. Główne funkcje kryteriów na podstawie których wyznaczane jest końcowe rozwiązane to współczynnik wykorzystania wewnętrznych środków transportu, współczynnik wykorzystania zewnętrznych środków transportu, koszty pracy środków transportu wewnętrznego i personelu, całkowity koszt realizacji zadań transportowych, współczynnik wykorzystania czasu zaangażowania pojazdów, całkowity czas poświęcony na wykonanie zadań, czy liczba pojazdów. Punktem wyjścia do badania było założenie, że o skuteczności zarządzania łańcuchem decydują dwa problemy decyzyjne ważne dla menedżerów w procesie zarządzania łańcuchem dostaw, tj. problem przydziału pojazdów do zadań i problem lokalizacji obiektów logistycznych w łańcuchu dostaw. Aby rozwiązać badany problem, zaproponowano innowacyjne podejście w postaci opracowania algorytmu genetycznego, który został dostosowane do przedstawionego modelu matematycznego. W pracy szczegółowo opisano poszczególne kroki konstruowania algorytmu. Zaproponowana struktura przetwarzana przez algorytm jest strukturą macierzową, dzięki której wyznaczane są optymalne parametry łańcucha dostaw. Procesy krzyżowania i mutacji zostały opracowane adekwatnie do przyjętej struktury macierzowej. W procesie kalibracji algorytmu wyznaczono takie wartości parametrów algorytmu tj. prawdopodobieństwo krzyżowania czy mutacji, które generują optymalne rozwiązanie. Poprawność algorytmu genetycznego oraz efektywność zaproponowanego narzędzia wspomagającego proces zarządzania łańcuchem dostaw została potwierdzona w procesie jego weryfikacji.
EN
The article presents a new optimization tool supporting supply chain management in the multi-criteria aspect. This tool was implemented in the EPLOS system (European Logistics Services Portal system). The EPLOS system is an integrated IT system supporting the process of creating a supply and distribution network in supply chains. This system consists of many modules e.g. optimization module which are responsible for data processing, generating results. The main objective of the research was to develop a system to determine the parameters of the supply chain, which affect its efficiency in the process of managing the goods flow between individual links in the chain. These parameters were taken into account in the mathematical model as decision variables in order to determine them in the optimization process. The assessment of supply chain management effectiveness was carried out on the basis of the global function of the criterion consisting of partial functions of the criteria described in the mathematical model. The starting point for the study was the assumption that the effectiveness of chain management is determined by two important decision-making problems that are important for managers in the supply chain management process, i.e. the problem of assigning vehicles to tasks and the problem of locating logistics facilities in the supply chain. In order to solve the problem, an innovative approach to the genetic algorithm was proposed, which was adapted to the developed mathematical model. The correctness of the genetic algorithm has been confirmed in the process of its verification.
EN
In this paper, a procedure for MEMS accelerometer static calibration using a genetic algorithm, considering non-orthogonality was presented. The results of simulations and real accelerometer calibration are obtained showing high accuracy of parameters estimation.
EN
In phononic quasi one-dimensional structures, there is a phenomenon of a phononic bandgap (PhBG), which means that waves of a given frequency do not propagate in the structure. The location and size of PhBG depend on the thickness of the layers, the type of materials used and their distribution in space. The theoretical study examined the transmission properties of quasi one-dimensional structures designed using a genetic algorithm (GA). The objective function minimized the transmission integral and integral of the absolute value of the transmission functions derivative (to eliminate high transmission peaks with a small half width) in a given frequency range. The paper shows the minimization of transmission in various frequency bands for a 40-layer structure. The distribution of multilayer structure transmission was obtained through the Transfer Matrix Method (TMM) algorithm. Structures surrounded by water were analyzed and built of layers of glass and epoxy resin.
EN
This paper presents the problem of the identifying parameters for use in mathematical models of induction motors with the use of a genetic algorithm (GA). The effect of arithmetical crossover and the generation of new populations on identification results is analysed. The identified parameters of the model were determined as a result of the minimisation of the performance index defined as the mean-square error of stator current and angular velocity. The experiments were performed for the low power induction motor. The steady-state genetic algorithm with regard to convergence and accuracy of the identification process and calculation time is analysed.
PL
W artykule przedstawiono problem identyfikacji parametrów modeli matematycznych silników indukcyjnych z zastosowaniem algorytmu genetycznego (AG). Analizowano wpływ krzyżowania arytmetycznego i generowania potomków na wyniki identyfikacji. Identyfikowane parametry modelu wyznaczono w rezultacie minimalizacji wskaźnika jakości zdefiniowanego jako błąd średniokwadratowy prądu stojana i prędkości kątowej. Badania eksperymentalne przeprowadzono dla silnika indukcyjnego małej mocy. Algorytm genetyczny z częściową wymianą populacji analizowano ze względu na zbieżność i dokładność procesu identyfikacji i czas obliczeń numerycznych.
EN
A genetic algorithm is proposed to solve the weight minimization problem of spatial truss structures considering size and shape design variables. A very recently developed metaheuristic method called JAYA algorithm (JA) is implemented in this study for optimization of truss structures. The main feature of JA is that it does not require setting algorithm specific parameters. The algorithm has a very simple formulation where the basic idea is to approach the best solution and escape from the worst solution. Analyses of structures are performed by a finite element code in MATLAB. The effectiveness of JA algorithm is demonstrated through benchmark spatial truss 39-bar, and compare with results in references.
EN
The article deals with the issue of quality improvement of a gear transmission by optimizing its geometry with the use of genetic algorithms. The optimization method is focused on increasing productivity and efficiency of the pump and reducing its pulsation. The best results are tested on mathematical model and automatically modelled in 3D be means of PTC Creo Software. The developed solution proved to be an effective tool in the search for better results, which greatly improved parameters of pump especially reduced flow pulsation.
18
Content available remote The algorithm of multi-objective optimization of PM synchronous motors
EN
This paper presents multi-objective algorithm for optimal designing of permanent magnet synchronous motors. The special attention is paid on the formulation the optimization problem, especially on the correct selection of the partial criteria which constitute multi-objective function and constraints. It is pointed out that connection of multimodal parameter (cogging torque) and unimodal parameter (electromagnetic torque) in one multi-objective compromise function can lead to erroneous operation of optimization algorithm. Therefore, decomposition of the optimization task into two-level is proposed. The optimization calculation has been executed for permanent magnet synchronous motor structure with hybrid excitation system.
PL
W artykule przedstawiono algorytm do optymalizacji magnetoelektrycznych silników synchronicznych. Przedstawiono rozważania dotyczące poprawnego formułowania kompromisowych funkcji celu, w szczególności odpowiedniego doboru kryteriów cząstkowych. Wykazano, że włączenie do kompromisowej funkcji jednocześnie członu reprezentującego elektromagnetyczny moment użyteczny i moment zaczepowy może prowadzić do błędnego działania algorytmu optymalizacji. Zaproponowano dekompozycję zadania optymalizacji na dwa etapy. Przedstawiono i omówiono wybrane wyniki obliczeń optymalizacyjnych dla magnetoelektrycznego silnika synchronicznego z hybrydowym układem wzbudzenia.
EN
Although the multitude benefit of wind power, the randomness of wind speed and the fluctuations of wind power are the most disadvantages of wind energy. So, for more efficiency and better performances, wind rotor must be driven at specific optimal rotational speed under each particular wind speed. Therefore, to extract the maximum power from wind turbine, a Maximum Power Point Tracking (MPPT) controller is required. In this paper, modeling of wind energy conversion system WECS using tip speed ratio (TSR) MPPT controller using PID controller tuned by genetic algorithm is investigated. The wind energy conversion is based on a doubly-fed induction generator (DFIG), which it is controlled by robust sliding mode control technique using a generator of 3.6 MW . The obtained results are presented and analyzed, where the performances of both proposed control strategies (MPPT based PID-GA, sliding mode control) have been shown.
PL
W pracy przedstawiono system energii wiatrowej wykorzystujący sterownik śledzący szcztową prędkość . W sterowniku zastosowano regulator PID strojony z wykorzystaniem algorytmu generycznego. Jako generator wykorzystano układ DFIG sterowany za pośrednictwem sterownika ślizgowego.
20
Content available remote Genetic algorithm optimization of a SAPF based on the fuzzy- DPC concept
EN
This article presents a study on the use of the concept of direct power control (DPC) based on intelligent techniques in the control of a shunt active power filter (SAPF). In order to improve harmonic mitigation and reactive power compensation capabilities, the conventional switching table is replaced by a fuzzy inference system to generate the switching sequences of the shunt active power filter. To ensure an active power exchange stable and efficient, the DC voltage of the SAPF in controlled using an integrated proportional controller (PI) optimized by a heuristic optimization technique based on genetic algorithms (GA). The combination of two intelligent techniques in this proposed control strategy makes it possible to reduce ripples in different variables of the SAPF, to maintain the direct voltage at their reference value and to improve the THD of the grid current. The numerical simulation results obtained under Matlab / Simulink confirm the importance of the SAPF's proposed control technique
PL
W artykule opisano wykorzystanie metody DPC (direct power control) do poprawy parametrów bocznikowego filtru aktywnego SAPF. Konwencjonalna tabela przełączeń jest zastąpiona przez system logiki rozmytej. Do optymalizacji filtru wykorzystano też algorytm genetyczny.
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