CONTEXT Cactus pear (Opuntia ficus-indica (L.) Mill. (Cactaceae)) is a medicinal plant widely used to treat diabetes. OBJECTIVE This work investigates the hypoglycemic and antihyperglycemic effect of cactus pear seed oil (CPSO), its mechanism of action, and any toxic effects. MATERIALS AND METHODS The hypoglycemic effect of CPSO was evaluated in groups of six healthy Wistar rats given 1 or ...

Based on the study of cultural algorithms and particle swarm optimization, a new method of FIR digital filters design, called cultural particle swarm optimization (CPSO) is presented in this paper. CPSO is a global convergent algorithm that can find out the global optima of the problem. The algorithm concept and implementing procedure of CPSO are described in detail. The simulation results show...

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...

This paper proposes a cellular particle swarm optimization (CPSO), hybridizing cellular automata (CA) and particle swarm optimization (PSO) for function optimization. In the proposed CPSO, a mechanism of CA is integrated in the velocity update to modify the trajectories of particles to avoid being trapped in the local optimum. With two different ways of integration of CA and PSO, two versions o...

Adaptation to dynamic optimization problems is currently receiving a growing interest as one of the most important applications of evolutionary algorithms. In this paper, a compound particle swarm optimization (CPSO) is proposed as a new variant of particle swarm optimization to enhance its performance in dynamic environments. Within CPSO, compound particles are constructed as a novel type of p...

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...

Forecasting of gas emission rate in mine is a complicated problem due to its nonlinearity and the small quantity of training data. Support vector regression (SVR) can solve the problem with small samples, nonlinear and high dimensions. However, the precision of SVR is significantly affected by its parameter. In order to improve the mine gas emission rate accurately, an optimal selection approac...

To resolve the problems that cloud particle swarm optimization (CPSO) was easily trapped in local minimum and possessed slow convergence speed and early-maturing during distribution grid reactive power optimization, CPSO algorithm was improved based on cloud digital features in this paper. The method combined Local search with global search together based on solution space transform, where the ...

Compared to conventional PSO algorithm, particle swarm optimization algorithms inspired by immunityclonal strategies are presented for their rapid convergence, easy implementation and ability of optimization. A novel PSO algorithm, clonal particle swarm optimization (CPSO) algorithm, is proposed based on clonal principle in natural immune system. By cloning the best individual of successive gen...

The recent finding of a 1-bond→2-phonon ‘percolation’-type behaviour in several random zincblende alloys, supporting an unsuspected 1-bond→2-mode behaviour in the bond length distribution, renews interest for a discussion of CuPt-type spontaneous ordering (CPSO) as a purely intrinsic effect. We investigate this key issue from both experimental (Raman scattering) and theoretical (first-principle...