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EN
The paper presents the results of analyses concerning a new approach to approximating trajectory of mining-induced horizontal displacements. Analyses aimed at finding the most effective method of fitting data to the trajectory of mining-induced horizontal displacements. Two variants were made. In the first, the direct least square fitting (DLSF) method was applied based on the minimization of the objective function defined in the form of an algebraic distance. In the second, the effectiveness of differential-free optimization methods (DFO) was verified. As part of this study, the following methods were tested: genetic algorithms (GA), differential evolution (DE) and particle swarm optimization (PSO). The data for the analysis were measurements of on the ground surface caused by the mining progressive work at face no. 698 of the German Prospel-Haniel mine. The results obtained were compared in terms of the fitting quality, the stability of the results and the time needed to carry out the calculations. Finally, it was found that the direct least square fitting (DLSF) approach is the most effective for the analyzed registration data base. In the authors’ opinion, this is dictated by the angular range in which the measurements within a given measuring point oscillated.
2
Content available Archipelag sztucznej inteligencji. Część I
PL
Tytuł tego artykułu może budzić wątpliwości Czytelników. Sztuczna inteligencja? Wiadomo! Ale jakiś archipelag? Już wyjaśniam. Otóż sztuczna inteligencja tylko z nazwy jest dziedziną integralną, jak – nawiązując do tytułu miesięcznika – napędy albo sterowanie. W istocie sztuczna inteligencja to zbiór bardzo różnych metod, które ludzie wymyślili w tym celu, żeby maszyny lepiej zaspokajały ich potrzeby. Te metody w większości nie mają ze sobą nawzajem absolutnie nic wspólnego. Są od siebie odległe i nie ma łatwego sposobu przejścia od jednej z nich do innej. Pozwoliłem sobie porównać tę sytuację do archipelagu wysp.
EN
In general, this paper focuses on finding the best configuration for PSO and GA, using the different migration blocks, as well as the different sets of the fuzzy systems rules. To achieve this goal, two optimization algorithms were configured in parallel to be able to integrate a migration block that allow us to generate diversity within the subpopulations used in each algorithm, which are: the particle swarm optimization (PSO) and the genetic algorithm (GA). Dynamic parameter adjustment was also performed with a fuzzy system for the parameters within the PSO algorithm, which are the following: cognitive, social and inertial weight parameter. In the GA case, only the crossover parameter was modified.
EN
Given an undirected connected graph G = (V, E), a subset of vertices S is a maximum 2-packing set if the number of edges in the shortest path between any pair of vertices in S is at least 3 and S has the maximum cardinality. In this paper, we present a genetic algorithm for the maximum 2-packing set problem on arbitrary graphs, which is an NP-hard problem. To the best of our knowledge, this work is a pioneering effort to tackle this problem for arbitrary graphs. For comparison, we extended and outperformed a well-known genetic algorithm originally designed for the maximum independent set problem. We also compared our genetic algorithm with a polynomial-time one for the maximum 2-packing set problem on cactus graphs. Empirical results show that our genetic algorithm is capable of finding 2-packing sets with a cardinality relatively close (or equal) to that of the maximum 2-packing sets. Moreover, the cardinality of the 2-packing sets found by our genetic algorithm increases linearly with the number of vertices and with a larger population and a larger number of generations. Furthermore, we provide a theoretical proof demonstrating that our genetic algorithm increases the fitness for each candidate solution when certain conditions are met.
EN
Objectives: Optimization of Brain-Computer Interface by detecting the minimal number of morphological features of signal that maximize accuracy. Methods: System of signal processing and morphological features extractor was designed, then the genetic algorithm was used to select such characteristics that maximize the accuracy of the signal’s frequency recognition in offline Brain-Computer Interface (BCI). Results: The designed system provides higher accuracy results than a previously developed system that uses the same preprocessing methods, however, different results were achieved for various subjects. Conclusions: It is possible to enhance the previously developed BCI by combining it with morphological features extraction, however, it’s performance is dependent on subject variability.
EN
This paper deals with genetic algorithms as an optimisation method and its use for optimisation of the machining process in the CAM system. Tool path verification and optimisation are two best ways of dramatically improving manufacturing operations while saving money with relatively little work. Genetic algorithms can be used for improvement of these operations and considerably reduce length of tool paths leading to the reduction of machine times and optimisation of cutting parameters. Provides the software application created to optimise processes of boring and local milling (Incomplete sentence; what or who provides).
EN
The study involves the development of multi-objective optimization model for turning machining process. This model was developed using a GA - based weighted-sum of minimum production cost and time criteria of multipass turning machining process subject to relevant technological/practical constraints. The results of the single-objective machining process optimization models for the multipass turning machining process when compared with those of multi-objective machining process model yielded the minimum production cost and minimum production time as $5.775 and 8.320 min respectively (and the corresponding production time and production cost as 12.996 min and $6.992, respectively), while those of the multi-objective machining process optimization model were $5.841and 9.097 min. Thus, the multi-objective machining process optimization model performed better than each of the single-objective model for the two criteria of minimum production cost and minimum production time respectively. The results also show that minimum production time model performs better than the minimum production cost model. For the example considered, the multi-objective model gave a lower production time of 30.0% than the corresponding production time obtained from the minimum production cost model, while it gave a lower production cost of 16.46% than the corresponding cost obtained by the minimum production time model.
EN
Minimum Production Time model of the machining process optimization problem comprising seven lathe machining operations were developed using Genetic Algorithms solution method. The various cost and time components involved in the minimum production cost and minimum production time criteria respectively, as well as all relevant technological/practical constraints were determined. An interactive, user-friendly computer package was then developed in Microsoft Visual Basic.Net environment to implement the developed models. The package was used to determine optimal machining parameters of cutting speed, feed rate and depth of cut for the seven machining operations with twenty-three technological constraints in the conversion of a cylindrical metal bar stock into a finished machined profile. The result of the single-objective machining process optimization models shows that the minimum production time is 21.84 min.
EN
In 2010 Durda, Caron, and Buchanan published a paper in INFOR: Information systems and Operational Research, entitled: An application of operational research to computational linguistics: Word ambiguity. In this article the authors developed “a new measure of word ambiguity (e.g., homonymy and polysemy) for use in psycholinguistic research”. In our work we propose some modification of their algorithm.
EN
This paper presents results of evolutionary minimisation of peak-to-peak value of a?multi-tone signal. The signal is the sum of multiple tones (channels) with constant amplitudes and frequencies combined with variable phases. An exemplary application is emergency broadcasting using widely used analogue broadcasting techniques: citizens band (CB) or VHF FM commercial broadcasting. The work presented illustrates a?relatively simple problem, which, however, is characterised by large combinatorial complexity, so direct (exhaustive) search becomes completely impractical. The process of minimisation is based on genetic algorithm (GA), which proves its usability for given problem. The final result is a?significant reduction of peak-to-peak level of given multi-tone signal, demonstrated by three real-life examples.
EN
A Travelling Salesman Problem (TSP) is an NP-hard combinatorial problem that is very important for many real-world applications. In this paper, it is shown, that proposed approach solves multi-objective TSP (mTSP) more effectively than other investigated methods, i.e. Non-dominated Sorting Genetic Algorithm II (NSGA-II). The proposed methods use rank and crowding distance (well-known from NSGA-II), combining those mechanisms in a novel, unique way: competing and co-evolving in the evolution process. The proposed modifications are investigated and verified by the benchmark mTSP instances, and results are compared to other methods.
12
Content available remote Customized genetic algorithm for facility allocation using p-median
EN
The p-median problem is classified as a NP-hard problem, which demands a long time for solution. To increase the use of the method in public management, commercial, military and industrial applications, several heuristic methods has been proposed in literature. In this work, we propose a customized Genetic Algorithm for solving the p-median problem, and we present its evaluation using benchmark problems of OR-library. The customized method combines parameters used in previous studies and introduces the evolution of solutions in stationary mode for solving PMP problems. The proposed Genetic Algorithm found the optimum solution in 37 of 40 instances of p-median problem. The mean deviation from the optimal solution was 0.002% and the mean processing time using CPU core i7 was 17.7s.
13
Content available remote A Specialized evolutionary approach to the bi-objective travelling thief problem
EN
In the recent years, it has been shown that real world-problems are often comprised of two, interdependent subproblems. Often, solving them independently does not lead to the solution to the entire problem. In this article, a Travelling Thief Problem is considered, which combines a Travelling Salesman Problem with a Knapsack Problem. A Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is investigated, along with its recent modification - a Non-Dominated Tournament Genetic Algorithm (NTGA). Each method is investigated in two configurations. One, with generic representation, and genetic operators. The other, specialized to the given problem, to show how the specialization of genetic operators leads to better results. The impact of the modifications introduced by NTGA is verified. A set of Quality Measures is used to verify the convergence, and diversity of the resulting PF approximations, and efficiency of the method. A set of experiments is carried out. It is shown that both methods work almost the same when generic representation is used. However, NTGA outperforms classical NSGA-II in the specialized results.
PL
Celem artykułu jest prezentacja metody wyznaczania tras pojazdów dystrybucyjnych i ocena wpływu zastosowanego sposobu wyznaczania ścieżek między węzłami w sieci transportowej.Realizacja celu wymagała sformułowania modelu matematycznego odwzorowującego system dystrybucji ładunków i zadania optymalizacyjnego.Przedstawiono metodę optymalizacyjną opartą o algorytmy genetyczne i modyfikację algorytmu A-star do wyznaczania ścieżek.W artykule porównano wyznaczanie marszrut dla pojazdów dystrybucyjnych z punktu widzenia zastosowanego podejścia do wyznaczania ścieżek.
EN
The aim of the article is to present themethodfordetermining routes of distribution vehicles and to assess the impact of the method used to determine the pathbetween nodes in the transport network. The implementation of the goal required the formulation of a mathematical model of the cargo distribution system and the optimization task. An optimization method based on genetic algorithms as well as modification of A-star forpathfindingwere presented. The articles compare the vehicle routing problem solution from the point of view of the approach used to determine paths.
EN
In the study described here model calibration was performed employing the inverse analysis using genetic algorithms (GA). The objective of analysis is to determine value of the coefficient of hydraulic conductivity, k. The commonly used method for the determination of coefficient of hydraulic conductivity based on Terzaghi consolidation leads to an underestimation of the value of k as the Terzaghi model does not take into account the deformation of soil skeleton. Here, an alternative methodology based on genetic algorithms is presented for the determination of the basic parameters of Biot consolidation model. It has been demonstrated that genetic algorithms are a highly effective tool enabling automatic calibration based on simple rules. The values of the coefficient of hydraulic conductivity obtained with GA are of at least one order smaller than values obtained with the Terzaghi model.
PL
Celem artykułu jest przedstawienie istotności zastosowania procesu selekcji przy diagnozowaniu turbin parowych. Możliwość zastosowania algorytmów genetycznych w diagnostyce cieplno-przepływowej wiąże się z zastosowaniem selekcji występujących parametrów. Proces selekcji jest najtrudniejszy ze względu na liczbę parametrów. Liczba degradujących się parametrów może być różna, tzn. możemy mówić o degradacji jednokrotnej lub wielokrotnej. Degradacje wielokrotne przysparzają najwięcej trudności podczas procesu wyboru odpowiedniego modelu selekcji.
EN
This article is intended to demonstrate the relevance of the selection process in diagnosing steam turbines. The applicability of genetic algorithms in thermal-flow diagnostics is associated with the use of selection, which is the most difficult process due to the number of parameters. The number of degradable parameters can be different, i.e. we can talk about single or multiple degradation. Multiple degradations cause the most difficulties during the process of choosing an appropriate selection model.
17
Content available Algorytmy rozwiązywania problemu kolorowania grafu
PL
Głównym celem pracy było zbadanie algorytmów rozwiązujących problem kolorowania grafu, kolejno: algorytmów zachłannych LF (ang. Largest First) i SFL (ang. Saturated Largest First), algorytmu genetycznego sekwencyjnego oraz równoległego. Ponadto, zaimplementowana została aplikacja działającej w środowisku przeglądarki internetowej pozwalająca na wizualizacje 3D procesu kolorowania grafu wraz z regulacją parametrów grafu (takich jak liczba wierzchołków i gęstość grafu) oraz obserwację uzyskanych wyników (czasu wykonywania algorytmu i liczby dobranych kolorów).
EN
The main aim of the study was to examine four algorithms concerning the graph colouring problem, respectively: LF (Largest First), SFL (Saturated Largest First), genetic algorithm, both sequential and multithreaded. Additionally, an application in a web browser environment was created to 3D visualisation of the graph colouring process allowing adjustment of graph parameters (such as number of vertices and graph density) and observation of the obtained results (execution time and number of colours).
PL
W artykule przedstawiony został nowoczesny system obliczeń rozproszonych, umożliwiający łatwe wykorzystanie dostępnych zasobów obliczeniowych przedsiębiorstwa. Opracowany system pozwala na przygotowanie planów produkcji w oparciu o różne modele matematyczne. Do rozwiązania problemów został wykorzystany rozproszony algorytm genetyczny z różnymi reprezentacjami chromosomu oraz operatorami genetycznymi, dostosowanymi do specyfiki danego problemu. W ten sposób wykazana została uniwersalność zaproponowanego systemu i jego zdolność do rozwiązywania rzeczywistych problemów zarządzania produkcją.
EN
The article presents a modern system of distributed computing, allowing easy use of available computational resources of the company. The developed system allows for the generation of production plans based on various mathematical models. A distributed genetic algorithm with different solution representations and different genetic operators tailored to the specific problem is used to solve the problems. In this way, the universality of the proposed system and its ability to solve real problems of production management were demonstrated.
19
Content available remote Analiza aerodynamiczna przedniego skrzydła bolidu Formuły Student
PL
Studenckie Koło Naukowe PolSl Racing w sezonie 2018 zaprezentuje nowy samochód wyścigowy z pakietem aerodynamicznym rozbudowanym o przednie oraz tylne skrzydło. Przedstawiono proces projektowania i optymalizacji geometrii przedniego skrzydła nowego bolidu. W analizach użyto narzędzia obliczeniowej mechaniki płynów (CFD). Analiza dwuwymiarowa skrzydła złożonego z dwóch profili pozwoliła na odrzucenie niespełniających wymagań wariantów geometrii. Jej optymalizacji dokonano z użyciem narzędzi optymalizacji algorytmami genetycznymi dostępnych w programie Ansys 18.1. Następnym krokiem było dodanie do geometrii drugiego profilu dodatkowego, zamodelowanie i zoptymalizowanie geometrii dwuwymiarowej skrzydła składającego się z trzech płatów. Analizę trójwymiarową przeprowadzono dla jednego, najlepszego ustawienia profili (wybranego w poprzednim kroku), do którego dodano obracające się koło. Jej celem było sprawdzenie zachowania strugi na całej rozpiętości skrzydła, dobranie odpowiedniej geometrii płyty końcowej oraz zamodelowanie całej geometrii przodu bolidu.
EN
SKN PolSl Racing in the season of 2018 will present a new racing car. One of the changes, comparing to the old car, is development of the aerodynamics and adding front and rear wing. In the herein engineering project is presented the design process of front wing for the car. The analysis was performed with CFD process. Two-dimensional analysis of wing geometry consisting of two airfoils allowed us to reject few variants of geometry which haven’t fulfill the requirements. The optimization of this analysis was performed by using genetic algorithm methods from Ansys 18.1 software. Next step was a two-dimensional analysis and optimization of geometry consisting of three airfoils. The three-dimensional analysis were performed for the best airfoil geometry with added rotating wheel to it selected in previous step. They were processed to examine air stream behavior alongside the wing and for selecting optimal endplate and modelling the geometry of car’s front.
EN
The selection of a proper set of views to materialize plays an important role in database performance. There are many methods of view selection that use different techniques and frameworks to select an efficient set of views for materialization. In this paper, we present a new efficient scalable method for view selection under the given storage constraints using a tree mining approach and evolutionary optimization. The tree mining algorithm is designed to determine the exact frequency of (sub)queries in the historical SQL dataset. The Query Cost model achieves the objective of maximizing the performance benefits from the final view set that is derived from the frequent view set given by the tree mining algorithm. The performance benefit of a query is defined as a function of query frequency, query creation cost, and query maintenance cost. The experimental results show that the proposed method is successful in recommending a solution that is fairly close to an optimal solution.
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