Decision Maturity Framework: Introducing Maturity In Heuristic Search
Heuristics-based search methodologies normally
work on searching a problem space of possible solutions toward
finding a “satisfactory" solution based on “hints" estimated from the
problem-specific knowledge. Research communities use different
types of methodologies. Unfortunately, most of the times, these hints
are immature and can lead toward hindering these methodologies by
a premature convergence. This is due to a decrease of diversity in
search space that leads to a total implosion and ultimately fitness
stagnation of the population. In this paper, a novel Decision Maturity
framework (DMF) is introduced as a solution to this problem. The
framework simply improves the decision on the direction of the
search by materializing hints enough before using them. Ideas from
this framework are injected into the particle swarm optimization
methodology. Results were obtained under both static and dynamic
environment. The results show that decision maturity prevents
premature converges to a high degree.
Heuristic Search, hints, Particle Swarm
Optimization, Decision Maturity Framework.
A PSO-based End-Member Selection Method for Spectral Unmixing of Multispectral Satellite Images
An end-member selection method for spectral unmixing that is based on Particle Swarm Optimization (PSO) is developed in this paper. The algorithm uses the K-means clustering algorithm and a method of dynamic selection of end-members subsets to find the appropriate set of end-members for a given set of multispectral images. The proposed algorithm has been successfully applied to test image sets from various platforms such as LANDSAT 5 MSS and NOAA's AVHRR. The experimental results of the proposed algorithm are encouraging. The influence of different values of the algorithm control parameters on performance is studied. Furthermore, the performance of different versions of PSO is also investigated.
End-members selection, multispectral satellite imagery, particle swarm optimization, spectral unmixing.
Dynamic Clustering using Particle Swarm Optimization with Application in Unsupervised Image Classification
A new dynamic clustering approach (DCPSO), based
on Particle Swarm Optimization, is proposed. This approach is
applied to unsupervised image classification. The proposed approach
automatically determines the "optimum" number of clusters and
simultaneously clusters the data set with minimal user interference.
The algorithm starts by partitioning the data set into a relatively large
number of clusters to reduce the effects of initial conditions. Using
binary particle swarm optimization the "best" number of clusters is
selected. The centers of the chosen clusters is then refined via the Kmeans
clustering algorithm. The experiments conducted show that
the proposed approach generally found the "optimum" number of
clusters on the tested images.
Clustering Validation, Particle Swarm Optimization,
Unsupervised Clustering, Unsupervised Image Classification.