SUPER-SAPSO: A New SA-Based PSO Algorithm

Swarm Optimisation (PSO) has been received increasing attention due to its simplicity and reasonable convergence speed surpassing genetic algorithm in some circumstances. In order to improve convergence speed or to augment the exploration area within the solution space to find a better optimum point, many modifications have been proposed. One of such modifications is to fuse PSO with other search strategies such as Simulated Annealing (SA) in order to make a new hybrid algorithm – so called SAPSO. To the best of the authors’ knowledge, in the earlier studies in terms of SAPSO, the researchers either assigned an inertia factor or a global temperature to particles decreasing in the each iteration globally. In this study the authors proposed a local temperature, to be assigned to the each particle, and execute SAPSO with locally allocated temperature. The proposed model is called SUPERSAPSO because it often surpasses the previous SAPSO model and standard PSO appropriately. Simulation results on different benchmark functions demonstrate superiority of the proposed model in terms of convergence speed as well as optimisation accuracy