An adaptive inertia weight strategy for Particle Swarm Optimizer

摘要

The overall performance of Particle Swarm Optimizer lies on its ability to harmonize global and local search process. By dividing the whole swarm into equal sub-swarms with iterative cooperation, and taking a series of Sugeno functions as inertia weight decline curves for each sub-swarm, an adaptive strategy was proposed to adaptively select different inertia decline curve according to the vary rate of the sub-swarm's fitness value. Experimental results on several benchmark functions show that the modified algorithm can effectively balance global and local search ability to avoid premature problem, and obtain better solutions with higher convergence speed.