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Tytuł:
A machine learning method for soil conditioning automated decision-making of EPBM : hybrid GBDT and Random Forest Algorithm
Autorzy:
Lin, Lin
Guo, Hao
Lv, Yancheng
Liu, Jie
Tong, Changsheng
Yang, Shuqin
Powiązania:
https://bibliotekanauki.pl/articles/2087007.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
soil conditioning
automated decision-making
hybrid algorithm
geological parameters
drive parameters
feature selection
Opis:
There lacks an automated decision-making method for soil conditioning of EPBM with high accuracy and efficiency that is applicable to changeable geological conditions and takes drive parameters into consideration. A hybrid method of Gradient Boosting Decision Tree (GBDT) and random forest algorithm to make decisions on soil conditioning using foam is proposed in this paper to realize automated decision-making. Relevant parameters include decision parameters (geological parameters and drive parameters) and target parameters (dosage of foam). GBDT, an efficient algorithm based on decision tree, is used to determine the weights of geological parameters, forming 3 parameters sets. Then 3 decision-making models are established using random forest, an algorithm with high accuracy based on decision tree. The optimal model is obtained by Bayesian optimization. It proves that the model has obvious advantages in accuracy compared with other methods. The model can realize real-time decision-making with high accuracy under changeable geological conditions and reduce the experiment cost.
Źródło:
Eksploatacja i Niezawodność; 2022, 24, 2; 237--247
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A new auto adaptive fuzzy hybrid particle swarm optimization and genetic algorithm
Autorzy:
Dziwiński, Piotr
Bartczuk, Łukasz
Paszkowski, Józef
Powiązania:
https://bibliotekanauki.pl/articles/1837533.pdf
Data publikacji:
2020
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
hybrid methods
Particle Swarm Optimization
Genetic Algorithm
fuzzy systems
multimodal function
Opis:
The social learning mechanism used in the Particle Swarm Optimization algorithm allows this method to converge quickly. However, it can lead to catching the swarm in the local optimum. The solution to this issue may be the use of genetic operators whose random nature allows them to leave this point. The degree of use of these operators can be controlled using a neuro-fuzzy system. Previous studies have shown that the form of fuzzy rules should be adapted to the fitness landscape of the problem. This may suggest that in the case of complex optimization problems, the use of different systems at different stages of the algorithm will allow to achieve better results. In this paper, we introduce an auto adaptation mechanism that allows to change the form of fuzzy rules when solving the optimization problem. The proposed mechanism has been tested on benchmark functions widely adapted in the literature. The results verify the effectiveness and efficiency of this solution.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 2; 95-111
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A novel hybrid cuckoo search algorithm for optimization of a line-start PM synchronous motor
Autorzy:
Knypiński, Łukasz
Powiązania:
https://bibliotekanauki.pl/articles/2204509.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
hybrid cuckoo search algorithm
heuristic algorithms
multi-objective optimization
permanent magnet synchronous motor
PMSM
algorytm kukułki hybrydowy
algorytm Cuckoo
algorytm heurystyczny
optymalizacja wielocelowa
silnik synchroniczny z magnesem trwałym
Opis:
The paper presents a novel hybrid cuckoo search (CS) algorithm for the optimization of the line-start permanent magnet synchronous motor (LSPMSM). The hybrid optimization algorithm developed is a merger of the heuristic algorithm with the deterministic Hooke–Jeeves method. The hybrid optimization procedure developed was tested on analytical benchmark functions and the results were compared with the classical cuckoo search algorithm, genetic algorithm, particle swarm algorithm and bat algorithm. The optimization script containing a hybrid algorithm was developed in Delphi Tiburón. The results presented show that the modified method is characterized by better accuracy. The optimization procedure developed is related to a mathematical model of the LSPMSM. The multi-objective compromise function was applied as an optimality criterion. Selected results were presented and discussed.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2023, 71, 1; art. no. e144586
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A performance analysis of a hybrid golden section search methodology and a nature-inspired algorithm for MPPT in a solar PV system
Autorzy:
Mostafa, Hazem H.
Ibrahim, Amr M.
Anis, Wagdi R.
Powiązania:
https://bibliotekanauki.pl/articles/141645.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
hybrid optimization
golden sections search
multi-verse optimization algorithm
maximum power point tracking
perturb and observe
photovoltaic (PV)
Opis:
This research presents a comparative study for maximum power point tracking (MPPT) methodologies for a photovoltaic (PV) system. A novel hybrid algorithm golden section search assisted perturb and observe (GSS-PO) is proposed to solve the problems of the conventional PO (CPO). The aim of this new methodology is to boost the efficiency of the CPO. The new algorithm has a very low convergence time and a very high efficiency. GSS-PO is compared with the intelligent nature-inspired multi-verse optimization (MVO) algorithm by a simulation validation. The simulation study reveals that the novel GSS- PO outperforms MVO under uniform irradiance conditions and under a sudden change in irradiance.
Źródło:
Archives of Electrical Engineering; 2019, 68, 3; 611-627
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Algorithm CFP-SFPwith parallel processing
Autorzy:
Kujawiak, M.
Powiązania:
https://bibliotekanauki.pl/articles/92930.pdf
Data publikacji:
2008
Wydawca:
Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Tematy:
association rules
data mining
web logs
a priori
a priori TID
a priori hybrid algorithm
FP-Tree
Opis:
Existing algorithms for finding association rules do not implement parallel processing. This paper proposes CFP-SFP (Creating Frequent Patterns with Set from Frequent Patterns) algorithm with parallel processing. The research involves running CEP-SEP algorithm with one thread and a dozen or so threads that are executed simultaneously. The research was conducted on a computer with one processor and dual-core processor.
Źródło:
Studia Informatica : systems and information technology; 2008, 1(10); 87-93
1731-2264
Pojawia się w:
Studia Informatica : systems and information technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An agent-oriented hierarchic strategy for solving inverse problems
Autorzy:
Smołka, M.
Schaefer, R.
Paszyński, M.
Pardo, D.
Álvarez-Aramberri, J.
Powiązania:
https://bibliotekanauki.pl/articles/329764.pdf
Data publikacji:
2015
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
inverse problem
hybrid optimization method
memetic algorithm
multi-agent system
magnetotelluric data inversion
zadanie odwrotne
optymalizacja hybrydowa
algorytm memetyczny
system wieloagentowy
Opis:
The paper discusses the complex, agent-oriented hierarchic memetic strategy (HMS) dedicated to solving inverse parametric problems. The strategy goes beyond the idea of two-phase global optimization algorithms. The global search performed by a tree of dependent demes is dynamically alternated with local, steepest descent searches. The strategy offers exceptionally low computational costs, mainly because the direct solver accuracy (performed by the hp-adaptive finite element method) is dynamically adjusted for each inverse search step. The computational cost is further decreased by the strategy employed for solution inter-processing and fitness deterioration. The HMS efficiency is compared with the results of a standard evolutionary technique, as well as with the multi-start strategy on benchmarks that exhibit typical inverse problems’ difficulties. Finally, an HMS application to a real-life engineering problem leading to the identification of oil deposits by inverting magnetotelluric measurements is presented. The HMS applicability to the inversion of magnetotelluric data is also mathematically verified.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2015, 25, 3; 483-498
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Estimation procedures for partially accelerated life test model based on unified hybrid censored sample from the Gompertz distribution
Autorzy:
Lone, Showkat Ahmad
Panahi, Hanieh
Powiązania:
https://bibliotekanauki.pl/articles/2172027.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
constant-stress
maximum a posteriori
maximum product of spacing
stochastic EM algorithm
unified hybrid censoring
Opis:
The accelerated life testing is the key methodology of evaluating product reliability rapidly. This paper presents statistical inference of Gompertz distribution based on unified hybrid censored data under constant-stress partially accelerated life test (CSPALT) model. We apply the stochastic expectation-maximization algorithm to estimate the CSPALT parameters and to reduce computational complexity. It is shown that the maximum likelihood estimates exist uniquely. Asymptotic confidence intervals and confidence intervals using bootstrap-p and bootstrap-t methods are constructed. Moreover the maximum product of spacing (MPS) and maximum a posteriori (MAP) estimates of the model parameters and accelerated factor are discussed. The performances of the various estimators of the CSPALT parameters are compared through the simulation study. In summary, the MAP estimates perform superior than MLEs (or MPSs) with respect to the smallest MSE values.
Źródło:
Eksploatacja i Niezawodność; 2022, 24, 3; 427--436
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Evolutionary algorithm for selecting dynamic signatures partitioning approach
Autorzy:
Zalasiński, Marcin
Laskowski, Łukasz
Niksa-Rynkiewicz, Tacjana
Cpałka, Krzysztof
Byrski, Aleksander
Przybyszewski, Krzysztof
Trippner, Paweł
Dong, Shi
Powiązania:
https://bibliotekanauki.pl/articles/2147146.pdf
Data publikacji:
2022
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
identity verification
dynamic signature
hybrid partitions
partitions’ selection
evolutionary algorithm
Opis:
In the verification of identity, the aim is to increase effectiveness and reduce involvement of verified users. A good compromise between these issues is ensured by dynamic signature verification. The dynamic signature is represented by signals describing the position of the stylus in time. They can be used to determine the velocity or acceleration signal. Values of these signals can be analyzed, interpreted, selected, and compared. In this paper, we propose an approach that: (a) uses an evolutionary algorithm to create signature partitions in the time and velocity domains; (b) selects the most characteristic partitions in terms of matching with reference signatures; and (c) works individually for each user, eliminating the need of using skilled forgeries. The proposed approach was tested using Biosecure DS2 database which is a part of the DeepSignDB, a database with genuine dynamic signatures. Our simulations confirmed the correctness of the adopted assumptions.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2022, 12, 4; 267--279
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Inverse and direct optimization shape of airfoil using hybrid algorithm Big Bang-Big Crunch and Particle Swarm Optimization
Autorzy:
Masoumi, Heidar
Jalili, Farhad
Powiązania:
https://bibliotekanauki.pl/articles/281379.pdf
Data publikacji:
2019
Wydawca:
Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
Tematy:
hybrid optimization algorithm
airfoil
inverse and direct optimization approaches
Euler’s equations
Opis:
In this paper, Big Bang-Big Crunch and Particle Swarm Optimization algorithms are combined and used for the first time to optimize airfoil geometry as a aerodynamic cross section. The optimization process is carried out both in reverse and direct directions. In the reverse approach, the object function is the difference between pressure coefficients of the optimized and target airfoils, which must be minimized. In the direct approach, three objective functions are introduced, the first of which is the drag to lift (D/L) ratio. It is minimized considering four different initial geometries, ultimately, all four geometries converge to the same final geometry. In other cases, maximizing lift the coefficient with the fixed drag coefficient constraint and minimizing the drag coefficient while the lift coefficient is fixed are defined as purposes. The results show that by changing the design parameters of the initial airfoil geometry, the proposed hybrid optimization algorithm as a powerful method satisfies the needs with proper accuracy and finally reaches the desired geometry.
Źródło:
Journal of Theoretical and Applied Mechanics; 2019, 57, 3; 697-711
1429-2955
Pojawia się w:
Journal of Theoretical and Applied Mechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Inverse Problem of Textile Material Design at Low Temperature Solved by a Hybrid Stochastic Algorithm
Rozwiązanie problemu inwersyjnego projektowania materiałów włókienniczych stosowanych w niskich temperaturach za pomocą hybrydowego algorytmu stochastycznego
Autorzy:
Weng, M
Xu, D
Zhou, X
Powiązania:
https://bibliotekanauki.pl/articles/232769.pdf
Data publikacji:
2015
Wydawca:
Sieć Badawcza Łukasiewicz - Instytut Biopolimerów i Włókien Chemicznych
Tematy:
inverse problem
textile design
hybrid stochastic algorithm
optimisation method
problem inwersyjny
projektowanie tkanin
hybrydowy algorytm stochastyczny
metody optymalizacji
Opis:
The inverse problem of textile material design (IPTMD) aims to determine textile materials with optimum thermal conductivities for the thickness designed in terms of the thermal comfort requirements of the wearer. In this paper, an IPTMD is presented on the basis of the physical nature of steady heat and moisture transfer in a human body-clothing-environment system. A globally convergent algorithm, the modified particle collision algorithm (MPCA), is proposed and its validity is verified. The MPCA is applied to solve the IPTMD for single-layer textile materials at low temperature. Numerical simulation results of the IPTMD proved the suitability of the IPTMD and effectiveness of the MPCA in solving complex global optimisation problems. The encouraging results indicate that the modelling method above and optimisation algorithm can be used for further applications.
Problem inwersyjny projektowania materiałów włókienniczych ma na celu określenie materiałów włókienniczych z optymalną przewodnością cieplną dla grubości zaprojektowanej z uwzględnieniem komfortu termicznego i wygody użytkownika. W pracy przedstawiono problem na podstawie stałego przepływu ciepła i wilgotności pomiędzy ciałem człowieka, odzieżą a środowiskiem zewnętrznym. Zaprezentowano i zweryfikowano prawidłowość algorytmu zbieżnego, algorytmu zderzenia cząstek modyfikowanych, który jest stosowany w celu rozwiązania problemu dla jednowarstwowych materiałów włókienniczych w niskich temperaturach. Wyniki symulacji numerycznych problemu dowodzą jego przydatności i skuteczności algorytmu w rozwiązywaniu złożonych problemów optymalizacji. Zachęcające rezultaty wskazują, że powyższy sposób modelowania i algorytm optymalizacji mogą być używane do różnych zastosowań.
Źródło:
Fibres & Textiles in Eastern Europe; 2015, 2 (110); 40-43
1230-3666
2300-7354
Pojawia się w:
Fibres & Textiles in Eastern Europe
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Minimizing the Makespan and Total Tardiness in Hybrid Flow Shop Scheduling with Sequence-Dependent Setup Times
Autorzy:
Mousavi, Seyyed Mostafa
Shahnazari-Shahrezaei, Parisa
Powiązania:
https://bibliotekanauki.pl/articles/2201180.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
dispatching rule
genetic algorithm
hybrid flow shop
neighborhood search structure
Opis:
The paper considers the production scheduling problem in a hybrid flow shop environment with sequence-dependent setup times and the objectives of minimizing both the makespan and the total tardiness. The multi-objective genetic algorithm is applied to solve this problem, which belongs to the non-deterministic polynomial-time (NP)-hard class. In the structure of the proposed algorithm, the initial population, neighborhood search structures and dispatching rules are studied to achieve more efficient solutions. The performance of the proposed algorithm compared to the efficient algorithm available in literature (known as NSGA-II) is expressed in terms of the data envelopment analysis method. The computational results confirm that the set of efficient solutions of the proposed algorithm is more efficient than the other algorithm.
Źródło:
Management and Production Engineering Review; 2023, 14, 1; 13--24
2080-8208
2082-1344
Pojawia się w:
Management and Production Engineering Review
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Multi-objective optimization of in-situ bioremediation of groundwater using a hybrid metaheuristic technique based on differential evolution, genetic algorithms and simulated annealing
Wielozadaniowa optymalizacja bioremediacji wód gruntowych in situ z zastosowaniem hybrydowej techniki metaheurystycznej opartej na zróżnicowanej ewolucji, algorytmach genetycznych i symulowanym wyżarzaniu
Autorzy:
Kumar, D.
Ch, S.
Mathur, S.
Adamowski, J.
Powiązania:
https://bibliotekanauki.pl/articles/292714.pdf
Data publikacji:
2015
Wydawca:
Instytut Technologiczno-Przyrodniczy
Tematy:
differential evolution
fuzzy logic
genetic algorithm
groundwater
hybrid algorithm
in situ bioremediation
simulated annealing
support vector machine (SVM)
bioremediacja in situ
algorytm hybrydowy
algorytm genetyczny
logika rozmyta
maszyna wektorów nośnych SVM
wyżarzanie symulowane
wody gruntowe
zróżnicowana ewolucja
Opis:
Groundwater contamination due to leakage of gasoline is one of the several causes which affect the groundwater environment by polluting it. In the past few years, In-situ bioremediation has attracted researchers because of its ability to remediate the contaminant at its site with low cost of remediation. This paper proposed the use of a new hybrid algorithm to optimize a multi-objective function which includes the cost of remediation as the first objective and residual contaminant at the end of the remediation period as the second objective. The hybrid algorithm was formed by combining the methods of Differential Evolution, Genetic Algorithms and Simulated Annealing. Support Vector Machines (SVM) was used as a virtual simulator for biodegradation of contaminants in the groundwater flow. The results obtained from the hybrid algorithm were compared with Differential Evolution (DE), Non Dominated Sorting Genetic Algorithm (NSGA II) and Simulated Annealing (SA). It was found that the proposed hybrid algorithm was capable of providing the best solution. Fuzzy logic was used to find the best compromising solution and finally a pumping rate strategy for groundwater remediation was presented for the best compromising solution. The results show that the cost incurred for the best compromising solution is intermediate between the highest and lowest cost incurred for other non-dominated solutions.
Zanieczyszczenie wód gruntowych wyciekami benzyny jest jedną z kilku przyczyn wpływających na środowisko wód podziemnych. W ostatnich latach bioremediacja in situ przyciągała uwagę badaczy z powodu jej zdolności do usuwania zanieczyszczeń w ich siedlisku i niskich kosztów procesu. Przedstawiona praca proponuje użycie nowego algorytmu hybrydowego do optymalizacji wielozadaniowej funkcji, która obejmuje koszty remediacji jako pierwsze zadanie i resztową zawartość zanieczyszczeń po zakończeniu procesu jako drugie z zadań. Algorytm hybrydowy powstał z połączenia metod różnicowej ewolucji, algorytmu genetycznego i symulowanego wyżarzania. Maszyna wektorów nośnych (SVM) została użyta jako wirtualny symulator biologicznej degradacji zanieczyszczeń w wodach gruntowych. Wyniki uzyskane z algorytmy hybrydowego porównano z wynikami zróżnicowanej ewolucji (DE), algorytmu genetycznego (NSGA II) i symulowanego wyżarzania (SA). Stwierdzono, że proponowany algorytm był w stanie zapewnić najlepsze rozwiązanie. Użyto metody z zakresu logiki rozmytej dla znalezienia najlepszego rozwiązania kompromisowego i na końcu przedstawiono dla tego rozwiązania strategię szybkości pompowania celem remediacji wód gruntowych. Wyniki pokazały, że koszty ponoszone na rozwiązanie kompromisowe są pośrednie między najwyższymi i najniższymi kosztami innych rozwiązań.
Źródło:
Journal of Water and Land Development; 2015, 27; 29-40
1429-7426
2083-4535
Pojawia się w:
Journal of Water and Land Development
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Multi-parametric and multi-objective thermodynamic optimization of a spark-ignition range extender ICE
Autorzy:
Toman, R.
Brankov, I.
Powiązania:
https://bibliotekanauki.pl/articles/243112.pdf
Data publikacji:
2018
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
Range Extender
hybrid electric vehicle
battery electric vehicle
internal combustion engine
spark ignition
thermodynamic optimization
genetic algorithm
Opis:
The current legislation pushes for the increasing level of vehicle powertrain electrification. A series hybrid electric vehicle powertrain with a small Range Extender (REx) unit – comprised of an internal combustion engine and an electric generator – has the technical potential to overcome the main limitations of a pure battery electric vehicle: driving range, heating, and air-conditioning demands. A typical REx ICE operates only in one or few steady-states operating points, leading to different initial priorities for its design. These design priorities, compared to the conventional ICE, are mainly NVH, package, weight, and overall concept functional simplicity – hence the costeffectiveness. The design approach of the OEMs is usually rather conservative: parting from an already-existing ICE or components and adapting it for the REx application. The fuel efficiency potential of a one-point operation of the REx ICE is therefore not fully exploited. This article presents a multi-parametric and multi-objective optimization study of a REx ICE. The studied ICE concept uses a well-known and proven technology with a favourable production and development costs: it is a two-cylinder, natural aspirated, port injected, four-stroke SI engine. The goal of our study is to find its thermodynamic optimum and fuel efficiency potential for different feasible brake power outputs. Our optimization tool-chain combines a parametric GT-Suite ICE simulation model and modeFRONTIER optimization software with various optimization strategies, such as genetic algorithms, gradient based methods or various hybrid methods. The optimization results show a great fuel efficiency improvement potential by applying this multi-parametric and multi-objective method, converging to interesting short-stroke designs with Miller valve timings.
Źródło:
Journal of KONES; 2018, 25, 3; 459-466
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
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
Artykuł
Tytuł:
On the hybridization of the artificial Bee Colony and Particle Swarm Optimization Algorithms
Autorzy:
El-Abd, M.
Powiązania:
https://bibliotekanauki.pl/articles/91658.pdf
Data publikacji:
2012
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
Artificial Bee Colony Algorithm
ABC
particle swarm optimization (PSO)
PSO
hybridization
hybrid algorithm
CEC05
Opis:
In this paper we investigate the hybridization of two swarm intelligence algorithms; namely, the Artificial Bee Colony Algorithm (ABC) and Particle Swarm Optimization (PSO). The hybridization technique is a component-based one, where the PSO algorithm is augmented with an ABC component to improve the personal bests of the particles. Three different versions of the hybrid algorithm are tested in this work by experimenting with different selection mechanisms for the ABC component. All the algorithms are applied to the well-known CEC05 benchmark functions and compared based on three different metrics, namely, the solution reached, the success rate, and the performance rate.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2012, 2, 2; 147-155
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 23

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