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In this paper, a novel firefly algorithm (FA) is presented to reduce the dependency on parameters. The new FA algorithm is called dynamic step factor based FA (DSFFA), in which the step factor is not fixed and it is dynamically updated during the evolution. Experimental study on several classical benchmark functions show that DSFFA is superior to the basic FA and three other FAs.
In wireless cellular networks, the interference alignment (IA) is a promising technique for interference management. A new IA scheme for downlink cellular network with multi-cell and multi-user was proposed. In the proposed scheme, the interference in the networks is divided into inter-cell interference (ICI) among cells and inter-user interference (IUI) in each cell. The ICI is aligned onto a multi-dimensional...
In order to overcome shortages of fuzzy neural network (FNN) and basic Particle Swarm Optimization (PSO) algorithm, the article proposes a novel method that the parameters of structure equivalent FNN (SEFNN) trained by Time Variant Particle Swarm Optimization (TVPSO) algorithm. TVPSO is made adaptive in nature by adaptively and dynamically changing its acceleration coefficients and its inertia weight...
Shearlet is a new effective signal representation tool in many image applications. A novel image denoising scheme based on Shearlet transform and particle swarm optimization is proposed in this paper. Experiments show that the proposed scheme can remove the pseudo-Gibbs artifacts and image noise effectively. Besides, it outperforms the existing schemes in regard of both the peak-signal-to-noise-ratio...
Owing to the problem that particle swarm optimization algorithm is easily falling into local optima point in optimization of high-dimensional and complex functions. In this paper, a novel two sub-swarms exchange particle swarm optimization based on multi-phases(TSEM-PSO) is proposed. The particle swarm is divided into two identical sub-swarms, with the first adopting the standard PSO model, and the...
Particle swarm optimization and its modifications appear premature convergence for complex optimization problem, because particles' performance becomes same in seeking later period. In this paper, a new model is proposed to avoiding particles' performance same and possessing strong exploration capacity. Considering exploration and exploitation capacity diverse in different stage, the particle swarm...
Particle swarm optimization and its modification for two sub-swarms exchange appear premature convergence for complex optimization problem, because particles' performance becomes same in seeking later period. Therefore, in this paper, a modified two sub-swarms exchange particle swarm optimization is proposed. The particle swarm is divided into two identical sub-swarms, with the first adopting the...
Particle swarm optimization and its modifications appear premature convergence for complex optimization problem, because particles' performance becomes same in seeking later period. In this paper, A new model is proposed to avoiding particles' performance same and possessing strong exploration capacity. Considering exploration and exploitation capacity diverse in different stage, the particle swarm...
In this paper, a Two Sub-swarms Quantum-behaved Particle Swarm Optimization Algorithm Based on Exchange Strategy (TS-QPSO) is proposed. Two sub-swarms of particles with quantum Behavior are set up in TS-QPSO. Once the whole swarm falls into local optima and the best value of the global swarm is not improved after the allowable iterations, the exchange strategy will be carried out. The amount of exchange...
In order to overcome the drawback of the standard PSO, such as being subject to falling into local optimization, an improved PSO algorithm based on three sub-swarms exchange is proposed. Firstly the method divides the whole swarm into three sub-swarms which evolve jointly according to three different models, that is, one evolves with the standard PSO model, and the second with social only model and...
Particle swarm optimization (PSO) has shown its fast search speed in many complicated optimization and search problems. However, PSO could often easily fall into local optima. This paper presents an improved PSO with adaptive jump. The proposed method combines a novel jump strategy and an adaptive Cauchy mutation operator to help escape from local optima. The new algorithm was tested on a suite of...
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