Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm

俎云霄,; 周杰,

doi:10.1088/1674-1056/21/1/019501

Cited by 17 publications

(5 citation statements)

References 55 publications

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“…Finally, it finds and decodes the solution from the last pool of mature strings obtained by ranking strings, exchanging the portions of strings and changing some bits of the strings. GAs have been developed for the solution of many non-linear problems in engineering and applied sciences, such as the multi-user cognitive radio network resource allocation system, [50][51][52] an ameliorative method for two-dimensional Poisson equations, [53] an algorithm to retrieve ocean atmosphere ducts, [54] the wireless sensor network [55] and the optimization of double-screen frequency selective surface structures. [56] Our intention is to use GA, PS and IPM techniques, as well as hybrid approaches GA-IPM and PS-IPM, for learning weights for the DENN.…”

Section: Learning Methodologiesmentioning

confidence: 99%

Unsupervised neural networks for solving Troesch's problem

Raja¹

2014

Chinese Phys. B

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In this study, stochastic computational intelligence techniques are presented for the solution of Troesch's boundary value problem. The proposed stochastic solvers use the competency of a feed-forward artificial neural network for mathematical modeling of the problem in an unsupervised manner, whereas the learning of unknown parameters is made with local and global optimization methods as well as their combinations. Genetic algorithm (GA) and pattern search (PS) techniques are used as the global search methods and the interior point method (IPM) is used for an efficient local search. The combination of techniques like GA hybridized with IPM (GA-IPM) and PS hybridized with IPM (PS-IPM) are also applied to solve different forms of the equation. A comparison of the proposed results obtained from GA, PS, IPM, PS-IPM and GA-IPM has been made with the standard solutions including well known analytic techniques of the Adomian decomposition method, the variational iterational method and the homotopy perturbation method. The reliability and effectiveness of the proposed schemes, in term of accuracy and convergence, are evaluated from the results of statistical analysis based on sufficiently large independent runs.

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Section: Learning Methodologiesmentioning

confidence: 99%

Unsupervised neural networks for solving Troesch's problem

Raja¹

2014

Chinese Phys. B

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“…( 1), a = [a Certain bands would have strong restrictions against certain power level transmission (GPS), locations (TV broadcast), or time (public safety). [5] Furthermore, the hardware also provides constraints for decisions that cognitive radios can make. For example, even though orthogonal frequency division multiplexing (OFDM) waveform is a suitable choice for a cognitive radio in a certain circumstance, it may not be used because the radio front end may not support such a wide bandwidth.…”

Section: Problem Formulation 21 General Problem Formulationmentioning

confidence: 99%

Cognitive radio adaptation for power consumption minimization using biogeography-based optimization

Zheng

Yang

et al. 2016

Chinese Phys. B

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Adaptation is one of the key capabilities of cognitive radio, which focuses on how to adjust the radio parameters to optimize the system performance based on the knowledge of the radio environment and its capability and characteristics. In this paper, we consider the cognitive radio adaptation problem for power consumption minimization. The problem is formulated as a constrained power consumption minimization problem, and the biogeography-based optimization (BBO) is introduced to solve this optimization problem. A novel habitat suitability index (HSI) evaluation mechanism is proposed, in which both the power consumption minimization objective and the quality of services (QoS) constraints are taken into account. The results show that under different QoS requirement settings corresponding to different types of services, the algorithm can minimize power consumption while still maintaining the QoS requirements. Comparison with particle swarm optimization (PSO) and cat swarm optimization (CSO) reveals that BBO works better, especially at the early stage of the search, which means that the BBO is a better choice for real-time applications.

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“…Simulations are conducted using the adaptive niche immune genetic algorithm, the simulated annealing algorithm, the quantum genetic algorithm and the simple genetic algorithm, respectively [19].…”

Section: Liao Et Al Presented a Novel Algorithm-isolation Nichementioning

confidence: 99%

An Improved Immune Genetic Algorithm and Its Application in Computer- aided Landscape Design

Yao-Kuan¹,

Liu²

2014

TOCSJ

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This paper proposes an improved immune genetic algorithm, and utilizes it in evaluating the results of computer-aided landscape design. After analyzing the related works and the flow chart of the standard immune genetic algorithm, an improved immune genetic algorithm is designed. The main modifications of our proposed immune genetic algorithm lie in the following aspects. 1) We modified the standard immune genetic algorithm using symbolic coding and full binary tree in the chromosomes to describe solutions. 2) The crossover operator with single point is used, and the cross point can be selected from the intermediate nodes and the root nodes.3) The mutation operator is modified to avoid the dependence of mutation probability on the initial value. 4) The modified immune genetic algorithm not only can keep random global search ability, but also can avoid local premature convergence. Next, the landscape design evaluation results can be obtained by SVM, the parameters of which can be optimized by the proposed modified immune genetic algorithm. Finally, experiments are conducted on three datasets using an index system which is including 17 indexes. Experimental results demonstrate that the proposed scheme can effectively evaluate the quality of computer-aided landscape design.

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Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm

Cited by 17 publications

References 55 publications

Unsupervised neural networks for solving Troesch's problem

Unsupervised neural networks for solving Troesch's problem

Cognitive radio adaptation for power consumption minimization using biogeography-based optimization

An Improved Immune Genetic Algorithm and Its Application in Computer- aided Landscape Design

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