  The combining of a General-Purpose Particle Swarm Optimizer (GP-PSO) with
Sequential Quadratic Programming (SQP) algorithm for constrained optimization
problems has been shown to be highly beneficial to the refinement, and in some
cases, the success of finding a global optimum solution. It is shown that the
likely difference between leading algorithms are in their local search ability.
A comparison with other leading optimizers on the tested benchmark suite,
indicate the hybrid GP-PSO with implemented local search to compete along side
other leading PSO algorithms.
