regarding the large number of developed particle swarm optimization (pso) algorithms and the various applications for which pso has been used, selecting the most suitable variant of pso for solving a particular optimization problem is a challenge for most researchers. in this paper, using a comprehensive survey and taxonomy on different types of pso, an expert system (es) is designed to identif...

Abstract—In this paper, an intelligent backstepping controller, tuned using a chaotic particle swarm optimization (CPSO), is proposed to control of chaos in Genesio-Tesi chaotic system. Thebackstepping method consists of parameters with positive values. The parameters are usually chosen optional by trial and error method. The improper selection of the parameters leads to inappropriate responses...

Scientists are beginning to realize more and more that nature is a great source for inspiration in order to develop intelligent systems and algorithms. In the field of Computational Intelligence, especially Evolutionary Computation and Swarm-based systems, the degree of imitation from nature is surprisingly high and we are at the edge of developing and proposing new algorithms and/or systems, w...

Artificial bee colony (ABC) algorithm is an optimization algorithm based on a particular intelligent behaviour of honeybee swarms. This work compares the performance of ABC algorithm with that of differential evolution (DE), particle swarm optimization (PSO) and evolutionary algorithm (EA) for multi-dimensional numeric problems. The simulation results show that the performance of ABC algorithm ...

In this paper, the newly introduced swarm-based optimisation algorithm called intelligent water drops (IWDs) algorithm is adjusted to optimise a modified Otsu’s criterion for automatic multilevel thresholding. The proposed algorithm simply called IWD-AMLT is tested with several grey-level images and its performance is assessed by the peak signal to noise ratio (PSNR) measure. The thresholded im...