Improved particle swarm optimization for class of nonlinear min-max problems
Chang Zhou · Journal of Computer Applications · 2008
Concerning the fact that the unsmoothness objective function of a class of nonlinear minimax problem caused difficult solution,a new algorithm was proposed.This algorithm used improved Particle Swarm Optimization combined with maximum entropy function method.Firstly,the maximum entropy function was used to transform the unconstrained and constrained minimax problems into a smooth function of unconstrained optimization problems;this smooth function was used as Particle Swarm Optimization's fitness function.Then a new position update equation was proposed by using the strategy of extrapolation in Mathematics.Thus,a new class of Particle Swarm Optimization was given.The new algorithm was applied to solving the minimax problems.The numerical results show that the algorithm converges faster and has numerical stability,and it is an effective algorithm for nonlinear minimax problems.