A nature inspired adaptive inertia weight in particle swarm optimisation
Madhuri Arya, Kusum Deep, Jagdish Chand Bansal · International Journal of Artificial Intelligence and Soft Computing · 2014
The selection of an appropriate strategy for adjusting inertia weight w is one of the most effective ways of enhancing the performance of particle swarm optimisation (PSO). Recently, a new idea, inspired from social behaviour of humans, for adaptation of inertia weight in PSO, has been proposed, according to which w adapts itself as the improvement in best fitness at each iteration. The same idea has been implemented in two different ways giving rise to two inertia weight variants of PSO namely globally adaptive inertia weight (GAIW) PSO, and locally adaptive inertia weight (LAIW) PSO. In this paper, the performance of these two variants has been compared with three other inertia weight variants of PSO employing an extensive test suite of 15 benchmark global optimisation problems. The experimental results establish the supremacy of the proposed variants over the existing ones in terms of convergence speed, and computational effort. Also, LAIW PSO comes out to be the best performer out of all the algorithms considered in this study.