Combining Particle Swarm Optimizer with SQP Local Search for Constrained Optimization Problems
This is an incremental improvement for researchers and practitioners in optimization, focusing on hybrid methods for constrained problems.
The authors tackled constrained optimization problems by combining a General-Purpose Particle Swarm Optimizer with Sequential Quadratic Programming local search, showing it competes with leading PSO algorithms on benchmark tests.
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.