2021/01/25 by Carwyn Pelley, Pelley, Carwyn, Mauro S. Innocente +3
Computer Science · Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Neural and Evolutionary Computing (cs.NE) #Optimization and Control (math.OC) #cs.NE #math.OC
paper · pdf · doi:10.48550/arxiv.2101.10936
Preprint submitted to the 8th ASMO UK Conference on Engineering Design Optimization
arxiv created 2021/01/25 · arxiv updated 2021/01/27
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.