A new Particle swarm optimisation (PSO) algorithm based on the Hénon chaotic map (hereafter HCPSO algorithm) is presented in this paper to deal with the premature convergence problem of the ...

This paper proposes three versions of particle swarm optimizers (PSO) with novel learning strategies where each dimension of a particle learns from just one particle's historical best information, ...

Particle Swarm Optimization (PSO): An optimisation algorithm inspired by social behaviours in nature, where a population of candidate solutions (particles) iteratively move through the search ...

Particle swarm optimization (PSO) algorithm is an optimization technique with remarkable performance for problem solving. The convergence analysis of the method is still in research.

Through this process, Simulink and the particle swarm optimization algorithm are tightly integrated, ensuring that each optimization iteration adjusts the control strategy based on simulation data, ...

Novel 'cuckoo search algorithm' beats particle swarm optimization in engineering design Date: May 28, 2010 Source: Inderscience Summary: The familiar early summer call of the cuckoo has inspired ...

By far the most common way to train a neural network is to use the back-propagation algorithm. But there are important alternatives. In this article, I'll demonstrate how to train a neural network ...

What algorithms should an air defense system work with? Particle swarm algorithms if there are ten targets to be hit. If there are more than ten targets, greedy algorithms work best.

These "particle robots," as the team calls them, are simple, circular devices that can connect to each other magnetically to move and work as a swarm. Individually, the robots are pretty basic.