Hybrid Optimisation Method Using PGA and SQP Algorithm

This paper investigates the hybridisation of two very different optimisation methods, namely the parallel genetic algorithm (PGA) and sequential quadratic programming (SQP) algorithm. The different characteristics of genetic-based and traditional quadratic programming-based methods are discussed and to what extent the hybrid method can benefit the solving of optimisation problems with nonlinear complex objective and constraint functions. Experiments show the hybrid method effectively combines the robust and global search property of parallel genetic algorithms with the high convergence velocity of the sequential quadratic programming algorithm, thereby reducing computation time, maintaining robustness and increasing solution quality