Heart chaotic system and the ability of particle swarm optimization (PSO) method motivated us to benefit the method of chaotic particle swarm optimization (CPSO) to synchronize the heart three-oscillator model. It can be a suitable algorithm for strengthening the controller in presence of unknown parameters. In this paper we apply adaptive control (AC) on heart delay model, also examine the sys...

Clustering is a popular data analysis and data mining technique. In this paper, a novel chaotic particle swarm fuzzy clustering (CPSFC) algorithm based on chaotic particle swarm (CPSO) and gradient method is proposed. Fuzzy clustering model optimization is challenging, in order to solve this problem, adaptive inertia weight factor (AIWF) and iterative chaotic map with infinite collapses (ICMIC)...

Chaotic particle swarm optimization (CPSO) is a newly developed optimization technique which combines the benefits of particle swarm optimization (PSO) and the chaotic optimization. This combination aims at avoiding the premature convergence of the PSO and the shortcomings of the chaotic optimization, in particular, the slow searching speed and the low accuracy when applied in optimizing a larg...

(Background) To solve the cluster analysis better, we propose a new method based on chaotic particle swarm optimization (CPSO) algorithm.
 (Methods) In order to enhance performance in clustering, novel CPSO. We first evaluate clustering of this model using Variance Ratio Criterion (VRC) as evaluation metric. The effectiveness CPSO algorithm is compared with that traditional Particle Swarm ...

Data clustering is a powerful technique for discerning the structure of and simplifying the complexity of large scale data. An improved technique combining chaotic map particle swarm optimization (CPSO) with an acceleration strategy, is proposed in this paper. Accelerated chaotic particle swarm optimization (ACPSO) searches for cluster centers of an arbitrary data set and can effectively find t...

Two popular particle swarm optimization (PSO) formulations; fuzzy–PSO (FPSO) and chaos–PSO (CPSO) have previously been studied in the literature. The charisma factor in FPSO gives the ability to track the particles which are closest to the optimum. CPSO has been aimed to search the area by using the chaotic maps. These two different algorithms are shown to demonstrate sufficient performance ind...

To deal with pattern synthesis of antenna arrays, a chaotic particle swarm optimization (CPSO) is presented to avoid the premature convergence. By fusing with the ergodic and stochastic chaos, the novel algorithm explores the global optimum with the comprehensive learning strategy. The chaotic searching region can be adjusted adaptively. To evaluate the performance of CPSO, several representati...

Winding hotspot temperature is the key factor affecting the load capacity and service life of transformers. For the early detection of transformer winding hotspot temperature anomalies, a new prediction model for the hotspot temperature fluctuation range based on fuzzy information granulation (FIG) and the chaotic particle swarm optimized wavelet neural network (CPSO-WNN) is proposed in this pa...

Economic load dispatch is a non linear optimization problem which is of great importance in power systems . While analytical methods suffer from slow conversion and curse of dimensionality particle swarm optimization can be an efficient alternative to solve large scale non linear optimization problem.A lot of advancements have been done to modify this algorithm. This paper presents an overview ...

Chaotic particle swarm optimization (CPSO) algorithm is proposed to optimize the Kriging model, which can improve the precision of curve fitting. A typical example is selected to demonstrate the advantage of the optimized Kriging model, compared with other curve fitting tools.