11 citations · 33 across the 12 of their papers we have counts for
9 papers · 1 filter
Coefficients' Settings in Particle Swarm Optimization: Insight and Guidelines
Mauro S. Innocente, Johann Sienz
Particle Swam Optimization is a population-based and gradient-free optimization method developed by mimicking social behaviour observed in nature. Its ability to optimize is not sp…
Pseudo-Adaptive Penalization to Handle Constraints in Particle Swarm Optimizers
Mauro S. Innocente, Johann Sienz
The penalization method is a popular technique to provide particle swarm optimizers with the ability to handle constraints. The downside is the need of penalization coefficients wh…
Individual and Social Behaviour in Particle Swarm Optimizers
Johann Sienz, Mauro S. Innocente
Three basic factors govern the individual behaviour of a particle: the inertia from its previous displacement; the attraction to its own best experience; and the attraction to a gi…
Particle Swarm Optimization: Fundamental Study and its Application to Optimization and to Jetty Scheduling Problems
Johann Sienz, Mauro S. Innocente
The advantages of evolutionary algorithms with respect to traditional methods have been greatly discussed in the literature. While particle swarm optimizers share such advantages,…
Combining Particle Swarm Optimizer with SQP Local Search for Constrained Optimization Problems
Carwyn Pelley, Mauro S. Innocente, Johann Sienz
The combining of a General-Purpose Particle Swarm Optimizer (GP-PSO) with Sequential Quadratic Programming (SQP) algorithm for constrained optimization problems has been shown to b…
Numerical Comparison of Neighbourhood Topologies in Particle Swarm Optimization
Mauro S. Innocente, Johann Sienz
Particle Swarm Optimization is a global optimizer in the sense that it has the ability to escape poor local optima. However, if the spread of information within the population is n…