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1

Synthesis of Reconfigurable Multiple Shaped Beams of a Concentric Circular Ring Array Antenna Using Evolutionary Algorithms

Dubey Sanjay Kumar, Mandal Debasis

Journal of Telecommunications and Information Technology

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2023

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nr 1

8--17

EN

The approach described in this paper uses evolutionary algorithms to create multiple-beam patterns for a concentric circular ring array (CCRA) of isotropic antennas using a common set of array excitation amplitudes. The flat top, cosec2, and pencil beam patterns are examples of multiple-beam patterns. All of these designs have an upward angle of θ = 0◦. All the patterns are further created in three azimuth planes (φ = 0◦, 5◦, and 10◦). To create the necessary patterns, non-uniform excitations are used in combination with evenly spaced isotropic components. For the flat top and cosecant-squared patterns, the best combination of common components, amplitude and various phases is applied, whereas the pencil beam pattern is produced using the common amplitude only. Differential evolutionary algorithm (DE), genetic algorithm (GA), and firefly algorithm (FA) are used to generate the best 4-bit discrete magnitudes and 5-bit discrete phases. These discrete excitations aid in lowering the feed network design complexity and the dynamic range ratio (DRR). A variety of randomly selected azimuth planes are used to verify the excitations as well. With small modifications in the desired parameters, the patterns are formed using the same excitation. The results proved both the efficacy of the suggested strategy and the dominance of DE over GA as well as FA.

2

A comparison of Differential Evolution, Particle Swarm Optimization, Artificial Bee Colony and Cuckoo Search for Multilevel thresholding

Kumar S., Pant M., Kumar R., Amiya A.

Computer Methods in Materials Science

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2013

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Vol. 13, No. 1

135--140

EN

The present study deals with the image segmentation of waste wood material using some popular nature inspired metaheuristics like: Differential Evolution (DE), Particle Swarm Optimization(PSO) Artificial bee Colony (ABC) and Cuckoo Search (CS). Otsu’s between class-variance and Kapur’s maximum entropy techniques are used as fitness functions. Experiments have been performed on various images and numerical results are compared. It is observed that in some cases Otsu method is giving the same performance as DE, PSO, ABC and CS. But when class size increases DE shows better results in comparison to others.

PL

Przedstawione badania dotyczą segmentacji obrazów odpadów drewna przy użyciu popularnych algorytmów inspirowanych naturą, takich jak: metoda ewolucji różnicowej (DE), metoda roju cząstek (PSO), algorytmu pszczelego (ABC) oraz algorytmu kukułki (CS). Jako funkcję celu wykorzystano wariancję międzyklasową Otsu oraz zasadę maksymalnej entropii. Porównując wyniki otrzymane dla różnych obrazów, zaobserwowano, że w niektórych przypadkach metoda Otsu wykazuje taką samą wydajność jak DE, PSO, ABC i CS. Jednak przy wzroście liczby klas wyniki otrzymane metodą DE są lepsze niż otrzymane pozostałymi metodami.

3

Optimization the dynamical parameters of three phase induction motor using genetic algorithm

Mohammed M.H.

Poznan University of Technology Academic Journals. Electrical Engineering

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2012

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No. 72

123--128

EN

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.

4

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.

5

A Comparative Study of Various Strategies in Differential Evolution

Boryczka U., Juszczuk P., Kłosowicz L.

Prace Naukowe Politechniki Warszawskiej. Elektronika

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2009

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z. 169

19-26

EN

This paper presents a comparison of various strategies of differential evolution. Differential evolution (DE) is a simple and powerful optimization method, which is mainly applied to numerical optimization and many other problems (for example: neural network train, filter design or image analysis). The comparison of various modifications (named strategies) of DE algorithm allows to choose the algorithm version which is best adjusted to desirable requirements. Three parameters are tested: speed, accuracy and completeness. The first section of this article presents general optimization problem and says a little about methods used to function optimization. The next section describes differential evolution - basic algorithm is presented. Two different crossover methods, process of initial population creation and basic mutation schema are described. The third section describes the most popular DE strategies. In the fourth section a new modification (called λ-modification) of DE algorithm is presented. Next section provides basic information about four test functions and differential evolution parameters used in research. The paper presents then summary and final conclusions.