Swarm Intelligence

Hassanien, Aboul Ella; Alamry, Eid

doi:10.1201/9781315222455

Cited by 78 publications

(15 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In these latter days swarm intelligence algorithms have shown good results in solving both continuous and discrete optimization problems. Swarm intelligence algorithms are successfully used to solve problems with the following features, namely, with a high dimension of the search space and computational complexity, with a complex topology of the region of acceptable values and the absence of an analytical expression [15]. As well as non-linear, multi-extremal and nondifferentiable problems [16].…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

The Method for Objects Detection on Satellite Imagery Based on the Firefly Algorithm

Khudov,

Khizhnyak,

Glukhov

et al. 2024

A.I.S.

View full text Add to dashboard Cite

The subject matter of the article is the method for detecting of objects on satellite imagery based on the firefly algorithm. The goal is to develop a method for detecting of objects on satellite imagery based on the firefly algorithm. The tasks are: analysis of existing methods for detecting of objects of interest on satellite imagery, development of a method for detecting of objects on satellite imagery, practical verification of the method for detecting of objects on satellite imagery based on the firefly algorithm, and quantitative assessment of the quality of the proposed method. The methods used are: methods of digital image processing, methods of data clustering, mathematical apparatus of matrix theory, methods of swarm intelligence, methods of mathematical modeling, methods of optimization theory, analytical and empirical methods of image comparison. The following results are obtained. The advantages and disadvantages of the main methods and approaches to the processing of satellite imagery for the purpose of detecting objects of interest on them are determined. The general principle of operation of the firefly algorithm is considered. It presents a flowchart of the method for detecting of objects on satellite imagery based on the firefly algorithm in one color channel. The values of the input data and parameters for the operation of the algorithm were selected experimentally. Experimental studies were conducted on the operation of the method for detecting of objects on a real satellite imagery based on the firefly algorithm. The values of the errors of the first and second kind for the processed image using the proposed method and the method based on the particle swarm algorithm were calculated. Conclusions. Analysis of the calculated values showed that the proposed method for detecting of objects on satellite imagery compared to the method based on the particle swarm algorithm: reduces the error of the first kind by about 11% and the error of the second kind by about 9%. The directions of further research are the study of the problem of selecting input parameters and data for the operation of the method based on the firefly algorithm.

show abstract

Section: Resultsmentioning

confidence: 99%

“…Swarm intelligence is a set of agents (particles) that simultaneously interact with each other in the swarm and with the environment. One of the representatives of swarm intelligence algorithms is the Firefly Algorithm [15].…”

Section: Resultsmentioning

confidence: 99%

The Method for Objects Detection on Satellite Imagery Based on the Firefly Algorithm

Khudov,

Khizhnyak,

Glukhov

et al. 2024

A.I.S.

View full text Add to dashboard Cite

show abstract

“…Table 2 shows the results of a comparison of the advantages and disadvantages of hardware acceleration of calculations based on the GPU and a programmable logic integrated circuit, obtained as a result of the analysis [17].…”

Section: Fig 2 Generalized Block Diagram Of a Programmable Logic Inte...mentioning

confidence: 99%

“…In addition, the need to decommission systems makes it difficult to change the fluctuations in computing load over time, which is typical, for example, for traffic analysis systems [17]. The solution to this problem is the transition from centralized to distributed computing systems [7].…”

Section: Limited Choice Of Chips Making Transferring Projects Between...mentioning

confidence: 99%

The Method of Observing Moving Objects

Mezin,

Kuchuk,

Lyashova

et al. 2024

CNCS

View full text Add to dashboard Cite

The article analyzes known algorithms for tracking moving objects. Based on an analysis of known algorithms for tracking moving objects, it was concluded that the best tracking quality in problems with a large number of observed objects is achieved by solutions built on the basis of probabilistic and hierarchical methods. Each of them has complementary advantages, which creates prospects for creating new algorithmic solutions built on the synergy of these approaches. The main task of promising tracking methods is that they should provide ease of scaling with an increase in the number of moving objects that need to be monitored, localize objects in three-dimensional space, and also be able to work with heterogeneous sensors. This approach has both purely technical advantages and those related to the availability of microelectronics components in modern geopolitical realities.

show abstract

“…The most of them are based on a biological evolution principle, where each generation (candidate solution) is developing during the outer conditions and interaction with each other. Different kinds of reproduction, mutation, recombination, and selection define the processes of their computational behavior [2].…”

mentioning

confidence: 99%

Study of Parallel Model of Artificial Bee Colony Algorithm

Sinkevych,

Boyko,

Monastyrsky

et al. 2024

ELIT

View full text Add to dashboard Cite

The paper presents the exploration of artificial bee colony optimization algorithm for multidimension functions which is implemented in Python 3. Due to increase of the searching complexity, the high dimensional applied problems require development of the concurrent computing approaches which involve either parallel or asynchronous programming. The latter causes growth of general runtime and becomes bottleneck of single-processing implementation. In order to deal with the raised issue, we propose the parallelization algorithms based on multiprocessing library. The developed parallel approaches have shown the significant increase of the performance and allow taking into account multi-dimensional character of optimization problems. Despite the limitation of Python 3 multi-threading capabilities and the computational cost of creating execution processes, the proposed and explored approaches have demonstrated their efficiency for a number of benchmark functions.

show abstract

Swarm Intelligence

Cited by 78 publications

References 18 publications

The Method for Objects Detection on Satellite Imagery Based on the Firefly Algorithm

The Method for Objects Detection on Satellite Imagery Based on the Firefly Algorithm

The Method of Observing Moving Objects

Study of Parallel Model of Artificial Bee Colony Algorithm

Contact Info

Product

Resources

About