An Improved Opposition-Based Disruption Operator in Gravitational Search Algorithm
Hao Liu, Guiyan Ding, Huafei Sun · 2012
Gravitational search algorithm (GSA) is based on the law of gravity and mass interactions. In this paper, firstly, we introduced opposition-based learning to generate initial population to improve population quality. Secondly, we propose an improved disruption operator in GSA to enhance the exploration and exploitation abilities and introduce a new updating strategy for position to improve the convergence rate. We confirm the high performance of the proposed improved GSA, which is called DGSA and has been evaluated on 23 nonlinear benchmark functions. We also verify DGSA's stability by the average of mean-best values.