In the original particle swarm optimisation (PSO) algorithm, the particles� velocities and positions are updated after the whole\nswarmperformance is evaluated. This algorithm is also known as synchronous PSO (S-PSO). The strength of this update method is\nin the exploitation of the information. Asynchronous update PSO (A-PSO) has been proposed as an alternative to S-PSO. A particle\nin A-PSO updates its velocity and position as soon as its own performance has been evaluated. Hence, particles are updated using\npartial information, leading to stronger exploration. In this paper, we attempt to improve PSO by merging both update methods\nto utilise the strengths of both methods. The proposed synchronous-asynchronous PSO (SA-PSO) algorithm divides the particles\ninto smaller groups. The best member of a group and the swarm�s best are chosen to lead the search. Members within a group\nare updated synchronously, while the groups themselves are asynchronously updated. Five well-known unimodal functions, four\nmultimodal functions, and a real world optimisation problemare used to study the performance of SA-PSO,which is comparedwith\nthe performances of S-PSO and A-PSO.The results are statistically analysed and show that the proposed SA-PSO has performed\nconsistently well.
Loading....