A Strongly Interacting Dynamic Particle Swarm Optimization Method

Dynamic Model Updating Using Particle Swarm Optimization Method

Introduction Finite Element (FE) model updating entails tuning the model so that it can better reflect the measured data from the physical structure being modeled [1]. One fundamental characteristic of an FE model is that it can never be a true reflection of the physical structure but will forever be an approximation. In other words, FE updating fundamentally implies that we are identifying a b...

portfolio optimization using particle swarm optimization method

the markowitz’s optimization problem is considered as a standard quadratic programming problem that has exact mathematical solutions. considering real world limits and conditions, the portfolio optimization problem is a mixed quadratic and integer programming problem for which efficient algorithms do not exist. therefore, the use of meta-heuristic methods such as neural networks and evolutionar...

Dynamic Sociometry in Particle Swarm Optimization

The performance of Particle Swarm Optimization is greatly affected by the size and sociometry of the swarm. This research proposes a dynamic sociometry, which is shown to be more effective on some problems than the standard star and ring sociometries. The performance of various combinations of swarm size and sociometry on six different test functions is qualitatively analyzed.

Particle Swarm Optimization in Dynamic Environments

Unified Particle Swarm Optimization in Dynamic Environments

A first investigation of the recently proposed Unified Particle Swarm Optimization algorithm on dynamic environments is provided and discussed on widely used test problems. Results are very promising compared to the corresponding results of the standard Particle Swarm Optimization algorithm, indicating the superiority of the new scheme.