An evolutionary technique based on K-means algorithm for optimal clustering in RN
Information Sciences—Applications: An International Journal
Adaptive double self-organizing maps for clustering gene expression profiles
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Comparison among five evolutionary-based optimization algorithms
Advanced Engineering Informatics
Differential evolution and particle swarm optimisation in partitional clustering
Computational Statistics & Data Analysis
A novel quantum swarm evolutionary algorithm for solving 0-1 knapsack problem
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
An improved probability particle swarm optimization algorithm
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
| Hi-index | 0.00 |
In this paper, a novel discrete particle swarm optimization (DPSO) algorithm is presented for solving the combinational optimization problems such as knapsack and clustering. The proposed algorithm mainly employs the idea of the information stored and exchanged among particles through Information-Shared Matrix (ISM). There are two reasons for using the idea. To begin with, the mechanism, storing and exchanging information, makes it possible to construct a discrete algorithm to solve combinational problems. Furthermore, the positions of particles in the space are adjusted according to not only historical information and global information current particles left, but also the information the other particles left. Therefore, information can be more sufficiently shared by each particle. The performance of DPSO algorithm is evaluated in comparison with well-known ACO algorithm, TS algorithm and other discrete PSO algorithms. Our computational simulations reveal very encouraging results in terms of the quality of solution found.