A Modified Particle Swarm Optimization for Solving Constrained Optimization Problems

In trying to solve constrained optimization problems by particle swarm optimization, the way to handle the constrained conditions is the key factor for success. Some features of particle swarm optimization and a large number of constrained optimization problems are taken into account and then a new method is proposed, which means to separate the objective functions from its constrained functions. Therefore, every particle of (particle) swarm optimization has double fitness values whether the particle is better or not will be decided by its two fitness values. The strategy to keep a fixed proportion of infeasible individuals is used in this new method. (Numerical) results show that the improved PSO is feasible and can get more precise results than particle swarm optimization by using penalty functions and genetic algorithm and other optimization algorithms.