In this chapter, one gives an introduction to different kinds of particle swarm optimization (PSO) algorithms. One also introduces artificial neural networks (ANNs), support vector machines (SVMs) and evolutionary computing to show how PSO may be used to determine optimal parameters using an ANN or SVM regime, for classification of DNA strings. In addition, PSO is used in the design of an SVM-based clustering algorithm. Ant colony optimization (ACO) algorithms are also introduced in the chapter. Using ACO algorithms has been of great success to solve many discrete optimization and non-deterministic polynomial (NP)-hard problems, for instance the travelling salesman problem. The behaviour of ants is also used, for instance, to design an algorithm for data clustering. We want to develop later a similar application based on this clustering algorithm and compare it with the SVM one using PSO.

Chapter Contents:

• Abstract
• 18.1 Introduction
• 18.2 Global particle optimization
• 18.3 Local particle optimization
• 18.4 PSO variations
• 18.4.1 Clamping
• 18.4.2 Inertia weight
• 18.4.3 Constriction coefficient
• 18.4.4 Algorithmic aspects
• 18.4.4.1 Velocity models
• 18.4.4.2 Initialization
• 18.4.4.3 Termination
• 18.5 PSO parameters
• 18.6 Evolutionary computing and PSO
• 18.6.1 Mutation
• 18.6.2 Crossover
• 18.6.3 Summary
• 18.6.4 A selection-based PSO algorithm
• 18.6.5 Replacement
• 18.7 A multi-phase PSO
• 18.8 Custom PSO
• 18.9 Artificial neural network
• 18.9.1 The biological neuron
• 18.9.2 The multi-layer perceptron
• 18.9.2.1 The artificial neuron
• 18.9.2.2 The layers
• 18.9.2.3 The backpropagation algorithm
• 18.10 Support vector machine
• 18.10.1 The Kernel
• 18.10.2 Definition of Kernels
• 18.10.3 Quadratic programming
• 18.10.4 KKT conditions
• 18.11 DNA classification
• 18.11.1 Presentation of the problem
• 18.11.2 DNA recognition theory
• 18.11.3 Feature extraction
• 18.11.3.1 Normalization
• 18.11.4 Use of PSO
• 18.11.5 Experiments
• 18.11.5.1 Results
• 18.12 Support vector clustering
• 18.12.1 Theory
• 18.12.2 SVC–PSO relation
• 18.12.3 Data set
• 18.12.4 The experiment
• 18.13 Ant colony systems
• 18.13.1 The pheromone
• 18.13.2 Optimization: travelling salesman problem
• 18.14 Ant colonies clustering
• References

Inspec keywords: neural nets; evolutionary computation; particle swarm optimisation; travelling salesman problems; support vector machines; computational complexity; pattern clustering

Other keywords: SVM regime; data clustering; DNA strings; optimal parameters; artificial neural networks; particle swarm optimization algorithms; evolutionary computing; PSO; ACO algorithms; clustering algorithm; ANN; ant colony optimization algorithms; discrete optimization; support vector machines; nondeterministic polynomial-hard problems; travelling salesman problem

Subjects: Neural computing techniques; Optimisation techniques; Knowledge engineering techniques; Combinatorial mathematics; Data handling techniques; Computational complexity