Probabilistically Driven Particle Swarms for Optimization of Multi Valued Discrete Problems : Design and Analysis
Kalyan K. Veeramachaneni, Lisa Ann Osadciw, Ganapathi Kamath · 2007
A new particle swarm optimization (PSO) algorithm that is more effective for discrete, multi-valued optimization problems is presented. The new algorithm is probabilistically driven since it uses probabilistic transition rules to move from one discrete value to another in the search for an optimum solution. Properties of the binary discrete particle swarms are discussed. The new algorithm for discrete multi-values is designed with the similar properties. The algorithm is tested on a suite of benchmarks and comparisons are made between the binary PSO and the new discrete PSO implemented for ternary, quaternary systems. The results show that the new algorithm's performance is close and even slightly better than the original discrete, binary PSO designed by Kennedy and Eberhart. The algorithm can be used in any real world optimization problems, which have a discrete, bounded field