Temat: differential evolution

Skocz do pozycji: 1.

Tytuł:: Adapting differential evolution algorithms for continuous optimization via greedy adjustment of control parameters
Autorzy:: Leon, M.
Xiong, N.
Tematy:: differential evolution
optimization
parameter adaptation; Pokaż więcej
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Powiązania:: https://bibliotekanauki.pl/articles/91824.pdf Link otwiera się w nowym oknie
Opis:: Differential evolution (DE) presents a class of evolutionary and meta-heuristic techniques that have been applied successfully to solve many real-world problems. However, the performance of DE is significantly influenced by its control parameters such as scaling factor and crossover probability. This paper proposes a new adaptive DE algorithm by greedy adjustment of the control parameters during the running of DE. The basic idea is to perform greedy search for better parameter assignments in successive learning periods in the whole evolutionary process. Within each learning period, the current parameter assignment and its neighboring assignments are tested (used) in a number of times to acquire a reliable assessment of their suitability in the stochastic environment with DE operations. Subsequently the current assignment is updated with the best candidate identified from the neighborhood and the search then moves on to the next learning period. This greedy parameter adjustment method has been incorporated into basic DE, leading to a new DE algorithm termed as Greedy Adaptive Differential Evolution (GADE). GADE has been tested on 25 benchmark functions in comparison with five other DE variants. The results of evaluation demonstrate that GADE is strongly competitive: it obtained the best rank among the counterparts in terms of the summation of relative errors across the benchmark functions with a high dimensionality.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: The grouping differential evolution algorithm for multi-dimensional optimization problems
Autorzy:: Piotrowski, A. P.
Napiórkowski, J. J.
Tematy:: differential evolution
multidimensional problems
multimodal problems
metaheuristics; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Instytut Badań Systemowych PAN
Powiązania:: https://bibliotekanauki.pl/articles/969839.pdf Link otwiera się w nowym oknie
Opis:: A variant of the Differential Evolution method is presented. The classical Differential Evolution approach is very successful for simple problems, but does not perform well enough for troublesome multi-dimensional non-convex continuous functions. To overcome some of the drawbacks, the Grouped Multi-Strategy Differential Evolution algorithm is proposed here. The main idea behind the new approach is to exploit the knowledge about the local minima already found in different parts of the search space in order to facilitate further search for the global one. In the proposed method, the population is split into four groups: three of them rarely communicate with the others, but one is allowed to gain all available knowledge from the whole population throughout the search process. The individuals simultaneously use three different crossover/mutation strategies, which makes the algorithm more flexible. The proposed approach was compared with two Differential Evolution based algorithms on a set of 10- to 100-dimensional test functions of varying difficulty. The proposed method achieved very encouraging results; its advantage was especially significant when more difficult 50- and 100-dimensional problems were considered. When dividing population into separate groups, the total number of individuals becomes a crucial restriction. Hence, the impact of the number of individuals on the performance of the algorithms was studied. It was shown that increasing the number of individuals above the number initially proposed for classic Differential Evolution method is in most cases not advantageous and sometimes may even result in deterioration of results.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Optimization the dynamical parameters of three phase induction motor using genetic algorithm
Autorzy:: Mohammed, M.H.
Tematy:: induction motor
genetic algorithm
differential evolution
DE; Pokaż więcej
Wydawca:: Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
Powiązania:: https://bibliotekanauki.pl/articles/376222.pdf Link otwiera się w nowym oknie
Opis:: This paper deals with the optimization of the induction motor design with respect to torque as a dynamical parameter. Most studies on the design of an induction motor using optimization techniques are concerned with the minimization of the motor cost and describe the optimization technique that was employed, giving the results of a single (or several) optimal design(s).Procedure includes the relationship between torque of motor and other effects as they occur in an optimal design. The optimization method that was used in this paper is Differential Evolution as genetic algorithm. Optimal results are in picture as curves or in tabula.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Effect of strategy adaptation on differential evolution in presence and absence of parameter adaptation: an investigation
Autorzy:: Dawar, D.
Ludwig, S. A.
Tematy:: evolutionary algorithms
differential evolution
mutation strategy
adaptive control; Pokaż więcej
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Powiązania:: https://bibliotekanauki.pl/articles/91882.pdf Link otwiera się w nowym oknie
Opis:: Differential Evolution (DE) is a simple, yet highly competitive real parameter optimizer in the family of evolutionary algorithms. A significant contribution of its robust performance is attributed to its control parameters, and mutation strategy employed, proper settings of which, generally lead to good solutions. Finding the best parameters for a given problem through the trial and error method is time consuming, and sometimes impractical. This calls for the development of adaptive parameter control mechanisms. In this work, we investigate the impact and efficacy of adapting mutation strategies with or without adapting the control parameters, and report the plausibility of this scheme. Backed with empirical evidence from this and previous works, we first build a case for strategy adaptation in the presence as well as in the absence of parameter adaptation. Afterwards, we propose a new mutation strategy, and an adaptive variant SA-SHADE which is based on a recently proposed self-adaptive memory based variant of Differential evolution, SHADE. We report the performance of SA-SHADE on 28 benchmark functions of varying complexity, and compare it with the classic DE algorithm (DE/Rand/1/bin), and other state-of-the-art adaptive DE variants including CoDE, EPSDE, JADE, and SHADE itself. Our results show that adaptation of mutation strategy improves the performance of DE in both presence, and absence of control parameter adaptation, and should thus be employed frequently.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Differential evolution with competitive setting of control parameters
Autorzy:: Tvrdik, J.
Tematy:: global optimization
differential evolution
self-adaptation
numerical comparison; Pokaż więcej
Wydawca:: Politechnika Gdańska
Powiązania:: https://bibliotekanauki.pl/articles/1943260.pdf Link otwiera się w nowym oknie
Opis:: This paper is focused on the adaptation of control parameters in differential evolution. Competition of various control parameter settings was proposed in order to ensure self-adaptation of parameter values in the search process. Several variants of such algorithm were tested on six functions at four levels of the search-space dimension. The competitive variants of differential evolution have proved to be more reliable and less time-consuming than the standard differential evolution. The competitive variants have also outperformed other tested algorithms in their reliability and convergence rate.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 6.

Tytuł:: Intelligent hybrid fuzzy logic system for damage detection of beam-like structural elements
Autorzy:: Sahu, S.
Kumar, P. B.
Parhi, D. R.
Tematy:: fuzzy logic
differential evolution algorithm
crack
natural frequency; Pokaż więcej
Wydawca:: Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
Powiązania:: https://bibliotekanauki.pl/articles/281457.pdf Link otwiera się w nowym oknie
Opis:: Fuzzy logic has been used in different research fields for more than three decades. It has become a robust method to solve complex and intricate problems which are otherwise difficult to solve by traditional methods. But it still requires some human experience and knowledge. In the present study, an attempt is made to design a hybrid optimization technique for automatic formation of the fuzzy knowledge based rules using an evolutionary algorithm. This hybridization technique has been applied in the field of damage detection and location of cracks in cracked structural elements. In this paper, a robust fault diagnostic tool based on a differential evolution algorithm and fuzzy logic has been proposed. Theoretical and Finite Element analyzes are done to model the crack and to find the effect of the presence of cracks on changes of vibrational characteristic (natural frequencies) of a fixed-fixed beam. The inputs to DEA-FL system are the first three relative natural frequencies, and the outputs from the system are the relative crack depth and relative crack location. For the validation of the results obtained from the proposed method and to check the robustness of the controller, experimental analysis is performed. To find average error rates, the bootstrap method has been adopted.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 7.

Tytuł:: Shape Optimization of Mufflers Composed of Multiple Rectangular Fin-Shaped Chambers Using Differential Evolution Method
Autorzy:: Chiu, M.-C.
Chang, Y.-C.
Cheng, H.-C.
Tai, W.-T.
Tematy:: fin
multi-chamber
high-order-mode
differential evolution; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/177237.pdf Link otwiera się w nowym oknie
Opis:: There has been considerable research done on multi-chamber mufflers used in the elimination of industrial venting noise. However, most research has been restricted to lower frequencies using the plane wave theory. This has led to underestimating acoustical performances at higher frequencies. Additionally, because of the space-constrained problem in most plants, the need for optimization of a compact muffler seems obvious. Therefore, a muffler composed of multiple rectangular fin-shaped chambers is proposed. Based on the eigenfunction theory, a four-pole matrix used to evaluate the acoustic performance of mufflers will be deduced. A numerical case for eliminating pure tones using a three-fin-chamber muffler will also be examined. To delineate the best acoustical performance of a space-constrained muffler, a numerical assessment using the Differential Evolution (DE) method is adopted. Before the DE operation for pure tone elimination can be carried out, the accuracy of the mathematical model must be checked using experimental data. The results reveal that the broadband noise has been efficiently reduced using the three-fin-chamber muffler. Consequently, a successful approach in eliminating a pure tone using optimally shaped three-fin-chamber mufflers and a differential evolution method within a constrained space has been demonstrated.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 8.

Tytuł:: A new multi-objective optimization algorithm based on differential evolution and neighborhood exploring evolution strategy
Autorzy:: Lobato, F. S.
Steffen, Jr, V.
Tematy:: multi-objective optimization
differential evolution
neighborhood exploring
evolution strategy
sorting strategy; Pokaż więcej
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Powiązania:: https://bibliotekanauki.pl/articles/91590.pdf Link otwiera się w nowym oknie
Opis:: In this paper a new optimization algorithm based on Differential Evolution, non-dominated sorting strategy and neighborhood exploration strategy for guaranteeing convergence and diversity through the generation of neighborhoods of different sizes to potential candidates in the population is presented. The performance of the algorithm proposed is validated by using standard test functions and metrics commonly adopted in the specialized literature. The sensitivity analysis of some relevant parameters of the algorithm is performed and compared with the classical DE algorithm without the strategy of neighborhood exploration and with other state-of-the-art evolutionary algorithms.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 9.

Tytuł:: Solving the sudoku with the differential evolution
Ewolucja różnicowa w rozwiązywaniu Sudoku
Autorzy:: Boryczka, U.
Juszczuk, P.
Tematy:: ewolucja różnicowa
sudoku
optymalizacja dyskretna
differential evolution
discrete optimization; Pokaż więcej
Wydawca:: Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
Powiązania:: https://bibliotekanauki.pl/articles/341123.pdf Link otwiera się w nowym oknie
Opis:: In this paper, we present the application of the Differential Evolution (DE) algorithm to solving the combinatorial problem. The advantage of the DE algorithm is its capability of avoiding so-called "local minima" within the considered search space. Thanks to the special operator of the adaptive mutation, it is possible to direct the searching process within the solution space. The DE algorithm applies the selection operator that selects from the child population only the offspring with the greater value of the fitness function in comparison to their parents. An algorithm applied to a combinatorial optimization problem: Sudoku puzzle is presented. Sudoku consists of a nine by nine grid, divided into nine three by three boxes. Each of the eighty-one squares should be filled in with a number between one and nine. In this article we show, that the mutation schema has significant impact on the quality of created solution.
W artykule przedstawimy propozycję zastosowania algorytmu ewolucji różnicowej do rozwiązywania problemów kombinatorycznych. Przewagą ewolucji różnicowej jest zdolność do unikania optimów lokalnych w przestrzeni przeszukiwań. Specjalny operator mutacji pozwala ukierunkować proces poszukiwań rozwiązania. W ewolucji różnicowej stosowany jest operator selekcji, który promuje tylko najlepiej przystosowane osobniki z populacji rodziców i potomków. Przedstawimy zastosowanie opisanego algorytmu do problemu rozwiązywania Sudoku. Sudoku składa się z planszy 9 na 9, podzielonej na 9 sekcji -każda o rozmiarze 3 na 3 elementy. Każda z 81 kratek powinna zostać wypełniona wartością z przedziału 1 do 9. W artykule pokażemy, że ewolucja różnicowa pozwala na rozwiązywanie Sudoku.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 10.

Tytuł:: Self-Adaptive Differential Evolution with Hybrid Rules of Perturbation for Dynamic Optimization
Autorzy:: Trojanowski, K.
Raciborski, M.
Kaczyński, P.
Tematy:: adaptive differential evolution
dynamic optimization
symmetric a-stable distribution; Pokaż więcej
Wydawca:: Instytut Łączności - Państwowy Instytut Badawczy
Powiązania:: https://bibliotekanauki.pl/articles/308439.pdf Link otwiera się w nowym oknie
Opis:: In this paper an adaptive differential evolution approach for dynamic optimization problems is studied. A new benchmark suite Syringa is also presented. The suite allows to generate test-cases from a multiple number of dynamic optimization classes. Two dynamic benchmarks: Generalized Dynamic Benchmark Generator (GDBG) and Moving Peaks Benchmark (MPB) have been simulated in Syringa and in the presented research they were subject of the experimental research. Two versions of adaptive differential evolution approach, namely the jDE algorithm have been heavily tested: the pure version of jDE and jDE equipped with solutions mutated with a new operator. The operator uses a symmetric ?-stable distribution variate for modification of the solution coordinates.
Dostawca treści:: Biblioteka Nauki

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