Wyniki wyszukiwania - BazTech

1

Inertial shrinking projection algorithm with self-adaptive step size for split generalized equilibrium and fixed point problems for a countable family of nonexpansive multivalued mappings

Olona Musa A., Alakoya Timilehin O., Owolabi Abd-semii O.-E., Mewomo Oluwatosin T.

Demonstratio Mathematica

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2021

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Vol. 54, nr 1

47--67

EN

In this paper, we introduce a shrinking projection method of an inertial type with self-adaptive step size for finding a common element of the set of solutions of a split generalized equilibrium problem and the set of common fixed points of a countable family of nonexpansive multivalued mappings in real Hilbert spaces. The self-adaptive step size incorporated helps to overcome the difficulty of having to compute the operator norm, while the inertial term accelerates the rate of convergence of the proposed algorithm. Under standard and mild conditions, we prove a strong convergence theorem for the problems under consideration and obtain some consequent results. Finally, we apply our result to solve split mixed variational inequality and split minimization problems, and we present numerical examples to illustrate the efficiency of our algorithm in comparison with other existing algorithms. Our results complement and generalize several other results in this direction in the current literature.

2

A self-adaptive Tseng extragradient method for solving monotone variational inequality and fixed point problems in Banach spaces

Jolaoso Lateef Olakunle

Demonstratio Mathematica

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2021

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Vol. 54, nr 1

527--547

EN

In this paper, we introduce a self-adaptive projection method for finding a common element in the solution set of variational inequalities (VIs) and fixed point set for relatively nonexpansive mappings in 2-uniformly convex and uniformly smooth real Banach spaces. We prove a strong convergence result for the sequence generated by our algorithm without imposing a Lipschitz condition on the cost operator of the VIs. We also provide some numerical examples to illustrate the performance of the proposed algorithm by comparing with related methods in the literature. This result extends and improves some recent results in the literature in this direction.

3

Improved dolphin swarm optimization algorithm based on information entropy

Li Y., Wang X.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2019

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Vol. 67, nr 4

679--685

EN

In order to overcome the shortcomings of the dolphin algorithm, which is prone to falling into local optimum and premature conver-gence, an improved dolphin swarm algorithm, based on the standard dolphin algorithm, was proposed. As a measure of uncertainty, information entropy was used to measure the search stage in the dolphin swarm algorithm. Adaptive step size parameters and dynamic balance factors were introduced to correlate the search step size with the number of iterations and fitness, and to perform adaptive adjustment of the algorithm. Simulation experiments show that, comparing with the basic algorithm and other algorithms, the improved dolphin swarm algorithm is feasible and effective.

4

Optimization of trusses with self-adaptive approach in genetic algorithms

Grygierek K.

Architecture Civil Engineering Environment

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2016

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Vol. 9, no. 4

67--78

EN

This paper presents a genetic algorithm method for the optimization of the weight of steel truss structures. In the method of genetic algorithm integer encoding of a discrete set of design variables and novel self-adaptive method based on fuzzy logic mechanism are applied for improving the quality and speed of optimization. Self-adaptive method is applied simultaneously in the selection of chromosomes and to control basic parameters of genetic algorithm. The algorithm proposed in the work was tested on the examples of optimization of steel trusses. Obtained results proved the effectiveness of genetic algorithm in relation to classical genetic algorithm.

PL

W pracy przedstawiono metodę algorytmów genetycznych do optymalizacji masy kratownic stalowych. W metodzie algorytmów genetycznych zastosowano kodowanie całkowitoliczbowe do opisu dyskretnego zbioru zmiennych projektowych oraz nową metodę samoadaptacyjną bazującą na logice rozmytej celem poprawienia jakości oraz szybkości procesu optymalizacyjnego. Metodę samoadaptacyjną użyto równocześnie do selekcji chromosomów oraz kontroli podstawowych parametrów algorytmu genetycznego. Zaproponowany w pracy algorytm przetestowano na przykładach optymalizacji kratownic stalowych. Otrzymane rezultaty pokazały jego efektywność w stosunku do klasycznego algorytmu genetycznego.

5

A study on the synchronization behaviour of differential evolution and a self-adaptive extension

Santucci V., Milani A., Vella F.

Journal of Artificial Intelligence and Soft Computing Research

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2012

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Vol. 2, No. 4

279--301

EN

Differential Evolution (DE) is a popular and efficient continuous optimization technique based on the principles of Darwinian evolution. Asynchronous Differential Evolution is a DE generalization that allows to regulate the synchronization mechanism of the algorithm by tuning two additional parameters. This paper, after providing a further experimental analysis of the impact of the DE synchronization scheme on the evolution, introduces three self-adaptive techniques to handle the synchronization parameters. Moreover the integration of these new regulatory synchronization techniques into state-of-the-art (self) adaptive DE schemes are also proposed. Experiments on widely accepted benchmark problems show that the new schemes are able to improve performances of the state-of-theart (self) adaptive DEs by introducing the new synchronization parameters in the online automated tuning process.

6

The application of the self-adaptive tracking method to the sinusoidal phase modulating interferometry

He G., Lu Y., Liao C., Zeng Z.

Optica Applicata

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2009

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Vol. 39, nr 1

109--122

EN

In this paper, we analyze theoretically the accuracy of the surface profile measurement in a sinusoidal phase modulating interferometer, derive the relative error formula, and investigate the influence of spectral leakage on the measurement accuracy. The theoretical results show that when the offset of sampling frequency from its theoretical ideal is outside the range of - 0.188% to +0.075%, the spectrum leakage results in an relative error greater than ?/320 nm, and thus the spectral leakage is not negligible. In order to eliminate the influence of the spectral leakage, a self-adaptive tracking method is proposed. The tracking method can adjust automatically the sampling signal frequency in such a way that the sampling signal frequency is an integer multiple of the modulating signal frequency. The simulation and experimental results show that the problem of the spectrum leakage can be solved with the proposed technique, and therefore the measurement accuracy and reliability of the SPM interferometer are enhanced.

7

Adaptive Ant Colony Optimization Algorithm Based on Information Entropy: Foundation and Application

Li Y., Li W.

Fundamenta Informaticae

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2007

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Vol. 77, nr 3

229-242

EN

In order to solve the premature convergence problem of the basic Ant Colony Optimization algorithm, a promising modification based on the information entropy is proposed. The main idea is to evaluate stability of the current space of represented solutions using information entropy, which is then applied to turning of the algorithm's parameters. The path selection and evolutional strategy are controlled by the information entropy self-adaptively. Simulation study and performance comparison with other Ant Colony Optimization algorithms and other meta-heuristics on Traveling Salesman Problem show that the improved algorithm, with high efficiency and robustness, appears self -adaptive and can converge at the global optimum with a high probability. The work proposes a more general approach to evolutionary-adaptive algorithms related to the population's entropy and has significance in theory and practice for solving the combinatorial optimization problems.