This MATLAB project implements a hybrid optimization algorithm that combines Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The algorithm is designed to optimize a set of parameters ...

The approach integrates the advantages of Proportional-Integral-Derivative control, particle swarm optimization, and neural networks. By constructing a neural network model with input, hidden, and ...

Specifically, the Multi-objective Particle Swarm Optimization (MOPSO) algorithm, implemented within the MATLAB environment, serves as our chosen tool to navigate this intricate optimization landscape.

A particle swarm optimization toolbox (PSOt) for use with the Matlab scientific programming environment has been developed. PSO is introduced briefly and then the use of the toolbox is explained with ...

Article citations More>> Alam, M.N. (2016) Particle Swarm Optimization: Algorithm and Its Codes in MATLAB. has been cited by the following article: TITLE: Implementation of Particle Swarm Optimization ...

Discover how Particle Swarm Optimization algorithm can accurately determine material parameters in elastic strain-energy functions. Simulation of rubber behavior using various strain-energy functions.

A particle swarm optimization toolbox (PSOt) for use with the Matlab scientific programming environment has been developed. PSO is introduced briefly and then the use of the toolbox is explained with ...