International Journal of Computational and Applied Mathematics
  • Year: 2009
  • Volume: 4
  • Issue: 1

A Comparative Performance of Swarm Intelligence Optimization Method and Evolutionary Optimization Method on Some Noisy Numerical Benchmark Test Problems

  • Author:
  • Sanjeev Kumar Singh1, Munindra Borah2
  • Total Page Count: 9
  • Page Number: 1 to 9

1Department of Mathematics & Computer Sc., Union Christian College, Ri-Bhoi-793122, Meghalaya, India.

2Department of Mathematical Sc., Tezpur University, Tezpur, Assam, India.

Abstract

Recently many algorithms have been developed which mimics the natural procedure better known as Evolutionary Algorithm and claims to perform better than others. The objective of this paper is to test the performance of Genetic algorithm and Repulsive Particle Swarm method on some benchmark functions. Since GA(Genetic Algorithm) mimics the nature and (RPS) exploits the swarm intelligence, it will be interesting to see the performance of these two methods on the certain test functions. A brief idea of these functions are given in this section are as follows. These functions are also represented by graph to facilitate conceptualization of the nature of these functions by visual means.

Keywords

Genetic Algorithm, Particle Swarm Optimization, Genetic Programming (GP), Ant Colony System(ACO),Simulated Annealing, Tabu Search, Memetic Algorithm, External Optimization(EO)