Wszystkie pola: population-based algorithm - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: On-line signature partitioning using a population based algorithm
Autorzy:: Zalasiński, Marcin
Łapa, Krystian
Cpałka, Krzysztof
Przybyszewski, Krzysztof
Yen, Gary G.
Powiązania:: https://bibliotekanauki.pl/articles/91729.pdf
Data publikacji:: 2020
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: on-line signature
biometrics
signature partitioning
population-based algorithm
podpis on-line
biometria
partycjonowanie sygnatur
algorytm populacyjny
Opis:: The on-line signature is a biometric attribute which can be used for identity verification. It is a very useful characteristic because it is commonly accepted in societies across the world. However, the verification process using this particular biometric feature is a rather difficult one. Researchers working on identity verification involving the on-line signature might face various problems, including the different discriminative power of signature descriptors, the problem of a large number of descriptors, the problem of descriptor generation, etc. However, population-based algorithms (PBAs) can prove very useful when resolving these problems. Hence, we propose a new method for on-line signature partitioning using a PBA in order to improve the verification process effectiveness. Our method uses the Differential Evolution algorithm with a properly defined evaluation function for creating the most characteristic partitions of the dynamic signature. We present simulation results of the proposed method for the BioSecure DS2 database distributed by the BioSecure Association.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 1; 5-13
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Multi-population-based algorithm with an exchange of training plans based on population evaluation
Autorzy:: Łapa, Krystian
Cpałka, Krzysztof
Kisiel-Dorohinicki, Marek
Paszkowski, Józef
Dębski, Maciej
Le, Van-Hung
Powiązania:: https://bibliotekanauki.pl/articles/2147148.pdf
Data publikacji:: 2022
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: population-based algorithm
multi-population algorithm
hybrid algorithm
island algorithm
subpopulation evaluation
training plan
Opis:: Population Based Algorithms (PBAs) are excellent search tools that allow searching space of parameters defined by problems under consideration. They are especially useful when it is difficult to define a differentiable evaluation criterion. This applies, for example, to problems that are a combination of continuous and discrete (combinatorial) problems. In such problems, it is often necessary to select a certain structure of the solution (e.g. a neural network or other systems with a structure usually selected by the trial and error method) and to determine the parameters of such structure. As PBAs have great application possibilities, the aim is to develop more and more effective search formulas used in them. An interesting approach is to use multiple populations and process them with separate PBAs (in a different way). In this paper, we propose a new multi-population-based algorithm with: (a) subpopulation evaluation and (b) replacement of the associated PBAs subpopulation formulas used for their processing. In the simulations, we used a set of typical CEC2013 benchmark functions. The obtained results confirm the validity of the proposed concept.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2022, 12, 4; 239--253
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Gold rush optimizer : a new population-based metaheuristic algorithm
Autorzy:: Zolf, Kamran
Powiązania:: https://bibliotekanauki.pl/articles/2204102.pdf
Data publikacji:: 2023
Wydawca:: Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:: gold rush optimizer
metaheuristic
global optimization
population-based algorithm
Opis:: Today’s world is characterised by competitive environments, optimal resource utilization, and cost reduction, which has resulted in an increasing role for metaheuristic algorithms in solving complex modern problems. As a result, this paper introduces the gold rush optimizer (GRO), a population-based metaheuristic algorithm that simulates how gold-seekers prospected for gold during the Gold Rush Era using three key concepts of gold prospecting: migration, collaboration, and panning. The GRO algorithm is compared to twelve well-known metaheuristic algorithms on 29 benchmark test cases to assess the proposed approach’s performance. For scientific evaluation, the Friedman and Wilcoxon signed-rank tests are used. In addition to these test cases, the GRO algorithm is evaluated using three real-world engineering problems. The results indicated that the proposed algorithm was more capable than other algorithms in proposing qualitative and competitive solutions.
Źródło:: Operations Research and Decisions; 2023, 33, 1; 113--150
2081-8858
2391-6060
Pojawia się w:: Operations Research and Decisions
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Evolutionary algorithm with a configurable search mechanism
Autorzy:: Łapa, Krystian
Cpałka, Krzysztof
Laskowski, Łukasz
Cader, Andrzej
Zeng, Zhigang
Powiązania:: https://bibliotekanauki.pl/articles/1837536.pdf
Data publikacji:: 2020
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: evolutionary algorithm
population-based algorithm
optimization
operator pool
operator selection
individual selection
Opis:: In this paper, we propose a new population-based evolutionary algorithm that automatically configures the used search mechanism during its operation, which consists in choosing for each individual of the population a single evolutionary operator from the pool. The pool of operators comes from various evolutionary algorithms. With this idea, a flexible balance between exploration and exploitation of the problem domain can be achieved. The approach proposed in this paper might offer an inspirational alternative in creating evolutionary algorithms and their modifications. Moreover, different strategies for mutating those parts of individuals that encode the used search operators are also taken into account. The effectiveness of the proposed algorithm has been tested using typical benchmarks used to test evolutionary algorithms.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 3; 151-171
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Wykorzystanie algorytmu populacyjnego do poprawy oddziaływania na sieć układów przekształtnikowych stosowanych w trakcji elektrycznej
Application of population based algorithm for improving the impact of converter devices used in electric traction on power network
Autorzy:: Kulesz, B.
Sikora, A.
Zielonka, A.
Powiązania:: https://bibliotekanauki.pl/articles/1197505.pdf
Data publikacji:: 2018
Wydawca:: Sieć Badawcza Łukasiewicz - Instytut Napędów i Maszyn Elektrycznych Komel
Tematy:: trakcja elektryczna
transformator prostownikowy
nierównowaga napięć
optymalizacja
algorytmy populacyjne
napięcie odkształcone
electric traction
rectifier transformer
voltage unbalance
optimization
population-based algorithms
distorted voltage
Opis:: Optimization procedures are nowadays a standard approach in overall transformer design, they also may be utilized for some detailed issues related toconstruction and operation of transformers. An overview of most useful procedures together with examples of their use is given in the paper. Example of applicati on of particle swarm optimization algorithm, namely Ant Colony Optimization is presented. We have discussed energy transformation circuit containing multi-phase transformer and diode rectifiers; this circuit is often used in tram traction substation in Poland. In this circuit, when supply voltage is distorted, pulsation of dc (rectified) voltage is increased and additional voltage higher harmonics start to appear. The concept of improving (i.e. lessening) pulsation in dc voltage is based upon used of on-load tap changer applied to one of transformer’s secondary windings in order to adjust voltage unbalance of secondary delta and wye windings. To calculate settings of tap changer under specified supply conditions we used ACO algorithm. Comparison of harmonic spectra in dc voltage and supply current for several different supply voltage waveforms is given
Procedury optymalizacyjne są obecnie standardowo wykorzystywane w projektowaniu transformatorów, jak również do pewnych szczegółowych zagadnień związanych z konstrukcją i eksploatacją transformatorów. W artykule podano krótki przegląd najczęściej używanych procedur wraz z możliwościami ich zastosowania. Zaprezentowano również konkretny przykład zastosowania algorytmu z grupy populacyjnych (ACO-Ant Colony Optimization). Rozważano układ przetwarzania energii z transformatorem wielofazowym i prostownikami diodowymi, stosowany często w trakcji elektrycznej tramwajowej w Polsce. W układzie takim, przy zasilaniu transformatora napięciem odkształconym, rośnie pulsacja napięcia wyprostowanego i pojawiają się dodatkowe harmoniczne tego napięcia. Idea poprawy pulsacji bazuje na zastosowaniu podobciążeniowego przełącznika zaczepów na jednym z wtórnych uzwojeń transformatora dla ustawienia pewnego balansu napięć obu uzwojeń wtórnych. Do obliczenia nastawy przełącznika w konkretnych warunkach zasilania użyto algorytmu mrówkowego. Zaprezentowano porównanie spektrum harmonicznych w napięciu wyprostowanym i prądzie zasilania transformatora dla kilku różnych kształtów napięcia zasilania.
Źródło:: Maszyny Elektryczne: zeszyty problemowe; 2018, 3, 119; 107-112
0239-3646
2084-5618
Pojawia się w:: Maszyny Elektryczne: zeszyty problemowe
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 6.

Tytuł:: Parallel PBIL applied to power system controller design
Autorzy:: Folly, K.
Powiązania:: https://bibliotekanauki.pl/articles/91747.pdf
Data publikacji:: 2013
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: Population-Based Incremental Learning algorithm
PBIL algorithm
Opis:: Population-Based Incremental Learning (PBIL) algorithm is a combination of evolutionary optimization and competitive learning derived from artificial neural networks. PBIL has recently received increasing attention in various engineering fields due to its effectiveness, easy implementation and robustness. Despite these strengths, it was reported in the last few years that PBIL suffers from issues of loss of diversity in the population. To deal with this shortcoming, this paper uses parallel PBIL based on multi-population. In parallel PBIL, two populations are used where both probability vectors (PVs) are initialized to 0.5. It is believed that by introducing two populations, the diversity in the population can be increased and better results can be obtained. The approach is applied to power system controller design. Simulations results show that the parallel PBIL approach performs better than the standard PBIL and is as effective as another diversity increasing PBIL called adaptive PBIL.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2013, 3, 3; 215-223
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 7.

Tytuł:: Improving Population-Based Algorithms with Fitness Deterioration
Autorzy:: Wolny, A.
Schaefer, R.
Powiązania:: https://bibliotekanauki.pl/articles/308437.pdf
Data publikacji:: 2011
Wydawca:: Instytut Łączności - Państwowy Instytut Badawczy
Tematy:: basin of attraction
clustering
fitness deterioration
genetic algorithm
optics
sequential niching
Opis:: This work presents a new hybrid approach for supporting sequential niching strategies called Cluster Supported Fitness Deterioration (CSFD). Sequential niching is one of the most promising evolutionary strategies for analyzing multimodal global optimization problems in the continuous domains embedded in the vector metric spaces. In each iteration CSFD performs the clustering of the random sample by OPTICS algorithm and then deteriorates the fitness on the area occupied by clusters. The selection pressure pushes away the next-step sample (population) from the basins of attraction of minimizers already recognized, speeding up finding the new ones. The main advantages of CSFD are low memory an computational complexity even in case of large dimensional problems and high accuracy of deterioration obtained by the flexible cluster definition delivered by OPTICS. The paper contains the broad discussion of niching strategies, detailed definition of CSFD and the series of the simple comparative tests.
Źródło:: Journal of Telecommunications and Information Technology; 2011, 4; 31-44
1509-4553
1899-8852
Pojawia się w:: Journal of Telecommunications and Information Technology
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 8.

Tytuł:: Swarm intelligence algorithm based on competitive predators with dynamic virtual teams
Autorzy:: Yang, S.
Sato, Y.
Powiązania:: https://bibliotekanauki.pl/articles/91592.pdf
Data publikacji:: 2017
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: swarm intelligence
sitness predator optimizer
dynamic virtual team
population diversity
Opis:: In our previous work, Fitness Predator Optimizer (FPO) is proposed to avoid premature convergence for multimodal problems. In FPO, all of the particles are seen as predators. Only the competitive, powerful predator that are selected as an elite could achieve the limited opportunity to update. The elite generation with roulette wheel selection could increase individual independence and reduce rapid social collaboration. Experimental results show that FPO is able to provide excellent performance of global exploration and local minima avoidance simultaneously. However, to the higher dimensionality of multimodal problem, the slow convergence speed becomes the bottleneck of FPO. A dynamic team model is utilized in FPO, named DFPO to accelerate the early convergence rate. In this paper, DFPO is more precisely described and its variant, DFPO-r is proposed to improve the performance of DFPO. A method of team size selection is proposed in DFPO-r to increase population diversity. The population diversity is one of the most important factors that determines the performance of the optimization algorithm. A higher degree of population diversity is able to help DFPO-r alleviate a premature convergence. The strategy of selection is to choose team size according to the higher degree of population diversity. Ten well-known multimodal benchmark functions are used to evaluate the solution capability of DFPO and DFPO-r. Six benchmark functions are extensively set to 100 dimensions to investigate the performance of DFPO and DFPO-r compared with LBest PSO, Dolphin Partner Optimization and FPO. Experimental results show that both DFPO and DFPO-r could demonstrate the desirable performance. Furthermore, DFPO-r shows better robustness performance compared with DFPO in experimental study.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2017, 7, 2; 87-101
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 9.

Tytuł:: On the efficiency of population-based optimization in finding best parameters for RGB-D visual odometry
Autorzy:: Kostusiak, Aleksander
Skrzypczyński, Piotr
Powiązania:: https://bibliotekanauki.pl/articles/384397.pdf
Data publikacji:: 2019
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: particle swarm optimization (PSO)
evolutionary algorithm
visual odometry
RGB-D
Opis:: Visual odometry estimates the transformations between consecutive frames of a video stream in order to recover the camera’s trajectory. As this approach does not require to build a map of the observed environment, it is fast and simple to implement. In the last decade RGBD cameras proliferated in roboTIcs, being also the sensors of choice for many practical visual odometry systems. Although RGB-D cameras provide readily available depth images, that greatly simplify the frame-to-frame transformations computaTIon, the number of numerical parameters that have to be set properly in a visual odometry system to obtain an accurate trajectory estimate remains high. Whereas seƫng them by hand is certainly possible, it is a tedious try-and-error task. Therefore, in this article we make an assessment of two population-based approaches to parameter opTImizaTIon, that are for long time applied in various areas of robotics, as means to find best parameters of a simple RGB-D visual odometry system. The optimization algorithms investigated here are particle swarm optimization and an evolutionary algorithm variant. We focus on the optimization methods themselves, rather than on the visual odometry algorithm, seeking an efficient procedure to find parameters that minimize the estimated trajectory errors. From the experimental results we draw conclusions as to both the efficiency of the optimization methods, and the role of particular parameters in the visual odometry system.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2019, 13, 2; 5-14
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 10.

Tytuł:: Application of multi-objective fruit fly optimisation algorithm based on population Manhattan distance in distribution network reconfiguration
Autorzy:: Tang, Minan
Zhang, Kaiyue
Wang, Qianqian
Cheng, Haipeng
Yang, Shangmei
Du, Hanxiao
Powiązania:: https://bibliotekanauki.pl/articles/1841286.pdf
Data publikacji:: 2021
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: Chebyshev chaotic mapping
distributed generation
distribution network reconfiguration
fuzzy decision method
Pareto optimal
pmdMOFOA
population Manhattan distance
Opis:: In order to optimise the operation state of the distribution network in the presence of distributed generation (DG), to reduce network loss, balance load and improve power quality in the distribution system, a multi-objective fruit fly optimisation algorithm based on population Manhattan distance (pmdMOFOA) is presented. Firstly, the global and local exploration abilities of a fruit fly optimisation algorithm (FOA) are balanced by combining population Manhattan distance (PMD) and the dynamic step adjustment strategy to solve the problems of its weak local exploration ability and proneness to premature convergence. At the same time, Chebyshev chaotic mapping is introduced during position update of the fruit fly population to improve ability of fruit flies to escape the local optimum and avoid premature convergence. In addition, the external archive selection strategy is introduced to select the best individual in history to save in external archives according to the dominant relationship amongst individuals. The leader selection strategy, external archive update and maintenance strategy are proposed to generate a Pareto optimal solution set iteratively. Lastly, an optimal reconstruction scheme is determined by the fuzzy decision method. Compared with the standard FOA, the average convergence algebra of a pmdMOFOA is reduced by 44.58%. The distribution performance of non-dominated solutions of a pmdMOFOA, MOFOA, NSGA-III and MOPSO on the Pareto front is tested, and the results show that the pmdMOFOA has better diversity. Through the simulation and analysis of a typical IEEE 33-bus system with DG, load balance and voltage offset after reconfiguration are increased by 23.77% and 40.58%, respectively, and network loss is reduced by 57.22%, which verifies the effectiveness and efficiency of the proposed method.
Źródło:: Archives of Electrical Engineering; 2021, 70, 2; 307-323
1427-4221
2300-2506
Pojawia się w:: Archives of Electrical Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 11.

Tytuł:: Probabilistic model-building algorithms as tool to find optimum of a function
Algorytmy z modelem probabilistycznym jako narzędzie optymalizacji funkcji
Autorzy:: Reichel, A.
Nowak, I.
Powiązania:: https://bibliotekanauki.pl/articles/87296.pdf
Data publikacji:: 2015
Wydawca:: Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
Tematy:: algorytm PBIL
algorytm cGA
metody heurystyczne
optymalizacja
population-based incremental learning
compact genetic algorithm
heuristic methods
optimization
Opis:: The aim of this paper is to present the probabilistic modelbuilding heuristics which is a modification of an evolutionary algorithm. the Probabilistic-Based Incremental Learning (PBIL) and the compact Genetic Algorithm (cGA) is presented as a example of the probabilistic model building algorithms dedicated to the binary problems. Both heuristics are tested on three functions that allow to investigate the advantages, disadvantages and limitations of methods under consideration.
Celem niniejszego artykułu jest przedstawienie heurystyk wieloagentowych wykorzystujących model probabilistyczny. W artykule omówiono dwie metody: the Probabilistic-Based Incremental Learning (PBIL) oraz the compact Genetic Algorithm (cGA), będące przykładami heurystyk z modelem probabilistycznym. Obie metody są przeznaczone do rozwiązywania problemów binarnych. W ramach pracy metody te testowano na trzech funkcjach zdefiniowanych w przestrzeni ciągów binarnych. Testy miały zbadać zalety, wady oraz ograniczenia obu prezentowanych heurystyk populacyjnych.
Źródło:: Zeszyty Naukowe. Matematyka Stosowana / Politechnika Śląska; 2015, 5; 79-97
2084-073X
Pojawia się w:: Zeszyty Naukowe. Matematyka Stosowana / Politechnika Śląska
Dostawca treści:: Biblioteka Nauki

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