Performance of Repulsive Particle Swarm Method in Global Optimization of Some Important Test Functions: A Fortran Program

Abstract:     
The Repulsive Particle Swarm (RPS) method of global optimization is perhaps the simplest to understand and implement. Due to its simplicity, it can be easily modified to suit the purpose and therefore, it has better prospects as well. The method has been frequently used in the field of artificial intelligence. It is well founded on philosophical and methodological grounds (bounded rationality and efficacy of decentralized decision-making to reach the global best) also.

The method of RPS has been programmed (in FORTRAN) and run to optimize 32 test functions (such as Ackley, Beale, Booth, Dixon & Price, Easom, Griewank, Himmelblau, Hump, Levy, Michalewics, Rastrigin, Rosenbrock, Schwefel, Shubert, Trid, etc). The program has successfully optimized these functions. The paper also provides graphical presentations of most of these functions and the FORTRAN codes of RPS method.

Keywords: Global optimization, multi-modal, Repulsive Particle swarm, Ackley, Beale, Booth, Dixon & Price, Easom, Griewank, Himmelblau, Hump, Levy, Matyas, Michalewics, Rastrigin, Rosenbrock, Schwefel, Shubert, Trid, Weierstrass, Shekel, Branin, Zakharov, test functions, Fortran, computer program, non-convex

JEL Classifications: C15, C63

Working Paper Series