MODIFIED GRAVITATIONAL SEARCH ALGORITHM WITH PARTICLE MEMORY ABILITY AND ITS APPLICATION
Binjie Gu, Feng Pan · 2013
Gravitational search algorithm (GSA) is a type of optimization algorithm based on the law of gravity and mass interactions, which is lacking of memory ability. To enhance particle memory ability and search accuracy of GSA, a modied GSA (MGSA) is developed. MGSA adopts the idea of local optimum solution and global optimum so- lution from particle swarm optimization (PSO) algorithm into GSA. Furthermore, the convergence property of MGSA is analyzed. The performance of MGSA has been eval- uated on 12 standard benchmark functions, and the results were compared with GSA. The obtained experimental results veried the effectiveness of MGSA in solving high- dimensional benchmark functions. Additionally, to test MGSA performance in practical issue, MGSA is applied into support vector machine (SVM) parameter settings, the re- sults showed that suitable SVM parameters could be effectively found by MGSA. Keywords: Gravitational search algorithm, Particle swarm optimization, Particle mem- ory ability, Benchmark function, Support vector machine classication