A Binary Particle Swarm Optimization Based on Proportion Probability

Particle swarm optimization (PSO), as a novel computational intelligence technique, has succeeded in many continuous problems. But in discrete or binary version there are still some difficulties. In this paper a novel binary PSO is proposed. This algorithm proposes a new definition for the position vector of binary PSO. The probability of a certain particle element assuming a value of 0 or 1 is positive proportional to values 0s or 1s of this element in the current position of the particle, the historic best position it experienced, and the best point found by the whole swarm, but negative proportional to value of the former position of the particle, which determines the next movement of the particle. It will be shown that this algorithm is a better interpretation of continuous PSO into discrete PSO than the older versions. Also a number of benchmark optimization problems are solved using this concept and quite satisfactory results are obtained.