 Abstract | Clustering algorithms have been widely applied and different approaches have
been developed for different domains of application. This thesis investigates efficient
and effective clustering and soft computing algorithms. In the investigation
of k-medoids algorithms, several improved algorithms are proposed, such
as the Clustering Large Applications Based on Simulated Annealing (CLASA)
algorithm, the Multi-Centroids with Multi-Runs Sampling Scheme (MCMRS)
and Incremental Multi-Centroid, Multi-Run Sampling Scheme (IMCMRS) algorithms.
The Partial Distance Search (PDS), Triangular Inequality Elimination
(TIE) and Previous Medoid Index are also presented to improve the clustering
speed of k-medoids based algorithms. In addition, a new memory utilization
scheme is derived and applied to efficient k-medoids algorithms.
In the investigation of centroid-based clustering algorithms, the tabu search
with simulated annealing algorithm is proposed and applied to codebook design
for vector quantization. Genetic clustering is also presented for mean-residual
vector quantization. Several theorems based on Hadamard Transform for nearest
neighbour search are presented and applied to efficient cluster (codeword) search
for vector quantization. A label bisecting clustering algorithm is proposed and
applied to create a robust watermarking technique.
Parallel particle swarm optimization based on three communication strategies
are proposed to solve the problems in which the relationship between parameters
are either independent, loosely correlated, strongly correlated or unknown. Seven
communication strategies for Ant Colony Systems (ACS) are proposed to improve
the ACS for the traveller salesman problem and the Constrained Ant Colony
Optimization (CACO) based on the quadratic metric, sum of k nearest neighbour
distance, constrained addition of pheromone and a shrinking range strategy is also
proposed and demonstrated to be better than the Ant Colony Optimization with
Different Favor (ACODF).
|
