Biologically Inspired Computing Technique for Optimizing Discontinuous Mathematical Functions

Abstract

This paper explores the immense capabilities of the recently proposed biologically inspired soft computing technique named Particle Swarm Optimization for the optimization of non-convex, nonlinear and discontinuous mathematical functions; which primarily focus upon the attainment of the global optimum, despite of the existence of local multi-optimums in the vicinity. This technique uses an innovative distributed intelligent paradigm for solving optimization problems that originally took its inspiration from the biological examples like bird flocking and fish schooling. This is also a population-based optimization tool, which could be implemented and applied easily to solve various function optimization problems. But unlike Genetic Algorithm it uses only primitive mathematical operator and does not uses cross-over, mutation and reproduction. Potential of this technique is illustrated by implementing it on the well known bench mark mathematical problems which includes Rastrigins, Ackley, Alpine, and Schaffer's F6 functions.