Artificial Neural Network (ANN) has found widespread application in the field of classification. Many domains have benefited with the use of ANN based models over traditional statistical models for their classification and prediction needs. Many techniques have been proposed to arrive at optimal values for parameters of the ANN model to improve its prediction accuracy. This paper compares the i...

Particle swarm optimization is a popular method for solving difficult optimization problems. There have been attempts to formulate the method in formal probabilistic or stochastic terms (e.g. bare bones particle swarm) with the aim to achieve more generality and explain the practical behavior of the method. Here we present a Bayesian interpretation of the particle swarm optimization. This inter...

This paper presents a convergence analysis of particle swarm optimization system by treating it as a discrete-time linear time-variant system firstly. And then, based on the results of system convergence conditions, dynamic optimal control of a deterministic PSO system for parameters optimization is studied by using dynamic programming; and an approximate dynamic programming algorithm – swarm-b...

The classification accuracy of the least squares support vector machine (LSSVM) models strongly depends on proper setting of its parameters. An optimal selection approach of LSSVM parameters is put forward based on multi-swarm cooperative chaos particle swarm optimization (MCCPSO) algorithm. Chaos particle swarm optimization (CPSO) can improve the ability of local search optimization with good ...

This paper proposes a TSK-type Neuro-Fuzzy system tuned with a novel learning algorithm. The proposed algorithm used an improved version of the standard Particle Swarm Optimization algorithm, it employs several sub-swarms to explore the search space more efficiently. Each particle in a sub-swarm correct her position based on the best other positions, and the useful information is exchanged amon...

Traditional mathematical algorithms are incapable of solving real time engineering design problems because of its rigid procedure mainly due to discrete or random data and multiobjective functions in a problem. An optimization algorithm is a procedure which is executed iteratively by comparing various solutions till the optimum or a satisfactory solution is found. There are two population based...

Particle Swarm Optimization (PSO) is proposed in our research to generate Fuzzy Controller, a fuzzy logic control (FLC) is proposed to control manufacturing system presented by mmachine line as an m-order state-space. As results indicated, use particle swarm optimization (PSO) method for optimizing a fuzzy logic controller (FLC) for manufacturing system is better than that of fuzzy logic contro...

In the last decennium, particle swarms have received considerable attention in the fields of optimization and control. Inspired by swarms of social animals, such as birds, fish, and termites, simple behavior on the local level has been shown to result in useful complex behavior on the global level. Particle Swarm Optimization has proven to be a very powerful optimization heuristic, and swarm ag...

Swarm intelligence (SI) is an Artificial Intelligence technique based on the study of collective behaviour in decentralized, selforganizing systems. It enables relatively simple agents to collectively perform complex tasks, which could not be performed by individual agents separately. Particles can interact either directly or indirectly (through the environment). The key to maintain global, sel...