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.
|Title of host publication||Proceedings of the 8th ASMO UK Conference on Engineering Design Optimization|
|Publisher||Association for Structural and Multidisciplinary Optimization in the UK|
|Number of pages||12|
|Publication status||Published - 2010|
|Event||8th ASMO UK Conference on Engineering Design Optimization - Queen Mary University of London, London, United Kingdom|
Duration: 8 Jul 2010 → 9 Jul 2010
Conference number: 8
|Conference||8th ASMO UK Conference on Engineering Design Optimization|
|Period||8/07/10 → 9/07/10|
Pelley, C., Innocente, M., & Sienz, J. (2010). Combining Particle Swarm Optimizer with SQP Local Search for Constrained Optimization Problems. In Proceedings of the 8th ASMO UK Conference on Engineering Design Optimization (pp. 268-279). Association for Structural and Multidisciplinary Optimization in the UK.