Evolutionary Neural Networks algorithm for the Dynamic Frequency Assignment Problem

Elhachmi, Jamal; Guennoun, Zouhair

doi:10.5121/ijcsit.2011.3304

Cited by 8 publications

(3 citation statements)

References 11 publications

(22 reference statements)

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“…Under that class of algorithms the following techniques were investegated:Neural Networks [9-11], Genetic algorithms or Evolutionary computation based approaches [12][13][14][15][16][17][18], Local search approaches such as simulated annealing [19][20], tabu search [21][22][23][24] and Ant algorithm [25][26]. Some hybrid approaches combining several techniques were also investigated, such as the use of GA with Neural network [27]; a GA coupled with fuzzy logic [28], Evolutionary Neural Networks Algorithm [29].…”

Section: Bendimerad Fethi Tarekmentioning

confidence: 99%

Solving frequency assignment problem using API algorithm

Debbat

Tarek

2014

2014 International Conference on Multimedia Computing and Systems (ICMCS)

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The efficient frequency assignment for radio communications becomes more and more crucial when developing new information technologies and their applications. It is consists in defining an assignment of frequencies to radio links, to be established between base stations and mobile transmitters. Separation of the frequencies assigned is necessary to avoid interference. However, unnecessary separation causes an excess requirement for spectrum, the cost of which may be very high. This problem is NP-hard problem which cannot be solved by conventional optimization algorithms. It is therefore necessary to use metaheuristic methods to solve it. This paper proposes API method (shortest of APIcalis) to solve this problem. It is based on a model of Pachycondyla APIcalis ants' foraging behavior. Computational results, obtained on a number of standard problem instances, testify the effectiveness of the proposed approach. Direct comparison of our approach with simulated annealing (SA) and Tabu Search (TS) on the same set of testsshows the interest of our approach for this problem.

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Section: Bendimerad Fethi Tarekmentioning

confidence: 99%

Solving frequency assignment problem using API algorithm

Debbat

Tarek

2014

2014 International Conference on Multimedia Computing and Systems (ICMCS)

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“…The selection process is an important factor of the success of GA. The application of GA is numerous, for example, it is used to optimize the parameters and topology of the network [1].…”

Section: Introductionmentioning

confidence: 99%

Self-Adaptation Mechanism to Control the Diversity of the Population in Genetic Algorithm

Jassadapakorn¹,

Chongstitvatana²

2011

IJCSIT

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“…A multi-objective approach to the evolution of ART networks with adaptive parameters for the genetic algorithm was proposed in a PhD thesis by Kaylani [135]. Hierarchical genetic algorithms, which used parametric and control genes to construct the chromosome, were applied for neuroevolution by Elhachmi and Guennoun [126]. On the side of ANN training procedures the focus is in recent years on novel combinations of GA with gradient-based or local optimization methods, which were used to address the problem of stock market time-series prediction [120] and optimize multi-objective processes in material synthesis [128].…”

Section: Hybrid Ann+easmentioning

confidence: 99%

Adaptation and Hybridization in Nature-Inspired Algorithms

Fister

Strnad

Yang³

2015

Adaptation, Learning, and Optimization

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Abstract. The aim of this chapter is to familiarize readers with the basics of adaptation and hybridization in nature-inspired algorithms as necessary for understanding the main contents of this book. Adaptation is a metaphor for flexible autonomous systems that respond to external changing factors (mostly environmental) by adapting their wellestablished behavior. Adaptation emerges in practically all areas of human activities as well. Such adaptation mechanisms can be used as a general problem-solving approach, though it may suffer from a lack of problem-specific knowledge. To solve specific problems with additional improvements of possible performance, hybridization can be used in order to incorporate a problem-specific knowledge from a problem domain. In order to discuss relevant issues as general as possible, the classification of problems is identified at first. Additionally, we focus on the biological foundations of adaptation that constitute the basis for the formulation of nature-inspired algorithms. This book highlights three types of inspirations from nature: the human brain, Darwinian natural selection, and the behavior of social living insects (e.g., ants, bees, etc.) and animals (e.g., swarm of birds, shoals of fish, etc.), which influence the development of artificial neural networks. evolutionary algorithms, and swarm intelligence, respectively. The mentioned algorithms that can be placed under the umbrella of computational intelligence are described from the viewpoint of adaptation and hybridization so as to show that these mechanisms are simple to develop and yet very efficient. Finally, a brief review of recent developed applications is presented.

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Evolutionary Neural Networks algorithm for the Dynamic Frequency Assignment Problem

Abstract: Wireless communication is used in many different

Cited by 8 publications

References 11 publications

Solving frequency assignment problem using API algorithm

Solving frequency assignment problem using API algorithm

Self-Adaptation Mechanism to Control the Diversity of the Population in Genetic Algorithm

Adaptation and Hybridization in Nature-Inspired Algorithms

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