PSO algorithm with chaos and gene density mutation for solving nonlinear zero-one integer programming problems

Abstract

By the penalty function method we transform zero-one nonlinear programming problems into unconstrained zero-one integer optimization problems. A particle swarm optimization algorithm with chaos and gene density mutation is given to solve unconstrained the zero-one nonlinear program problems. We use chaos to initialize populations and use the 0-1 integer operation in updating positions to produce 0-1 integer points. We use the fitness variance and gene density strategy to determine whether the population premature phenomenon or not. If it appears that we use the gene density mutation to increase the population diversity or restart and reset the population by chaos technique. Numerical simulations show that the proposed algorithm for most test functions is feasible, effective and has high precision. © 2011 Springer-Verlag.