Hybrid particle swarm optimization algorithm for mixed-integer nonlinear programming

For improving particle swarm optimization(PSO) to be suitable to solve mixed-integer nonlinear programming(MINLP),a hybrid particle swarm optimization(HPSO) was proposed,which can enhance the ability of dealing with constraints and integer variables.Constraints satisfaction of each solution was evaluated based on a constraint matrix for particle swarm,and the concept of Pareto domination was used to evaluate the quality of solution and determine the local and global best positions.A random rounding method based on distance function was introduced to dealing with integer variables,and a stochastic mutation based solution repair strategy was embedded for increasing the convergence speed.Both multi-swarms strategy and velocity update strategy which utilizing all particles' local best information were adopted to enhance the population diversity,which was propitious to improve the global optimization ability.Several case studies were illustrated to verify the effectiveness and superiority of HPSO.The results show that compared with the literature's results,HPSO has better global optimization ability and faster convergence speed.