Comparison of Hybrid PSO-SA Algorithm and Genetic Algorithm for Classification

In this work, we propose and present a Hybrid particle swarm optimization-Simulated annealing algorithm and compare it with a Genetic algorithm for training respectively neural networks of identical architectures. These neural networks were then tested on a classification task. In particle swarm optimization, behavior of a particle is influenced by the experiential knowledge of the particle as well as socially exchanged information. Particle swarm optimization follows a parallel search strategy. In simulated annealing uphill moves are made in the search space in a stochastic fashion in addition to the downhill moves. Simulated annealing therefore has better scope of escaping local minima and reach a global minimum in the search space. Thus simulated annealing gives a selective randomness to the search. Genetic algorithm performs parallel and randomized search. The goal of training the neural network is to minimize the sum of the squares of the error between the target and observed output values for all the training samples and to deliver good test performance on the test inputs. We compared the performance of the neural networks of identical architectures trained by the Hybrid particle swarm optimization-simulated annealing and Genetic algorithm respectively on a classification task and noted the results obtained. Neural network trained by Hybrid particle swarm optimization-simulated annealing has given better results compared to the neural network trained by the Genetic algorithm in the tests conducted by us. Keywords: Classification, Hybrid particle swarm optimization-Simulated annealing, Simulated Annealing, Genetic algorithm, Neural Network etc