Improved Simplified Particle Swarm Optimization

The Simplified Particle Swarm Optimization(SPSO) has some defects,such as relapsing into local extremum and slow convergence velocity,which is due to the weak differences between particles causing by the same iterative equation.According to this problem,based on SPSO and grouping idea of Shuffled Frog Leaping Optimization(SFLA),an improved algorithm named Shuffled Frog Leaping Simplified Particle Swarm Optimization(SFLA-SPSO) algorithm was proposed.In this algorithm,particle swarms were divided into several groups which search the problem space in parallel.After several generations,the particle swarms need to be divided again.Each particle can get more information to change its best position by different iterative equations,ensuring the differences between particles.PSO,SPSO,SFLA and SFLA-SPSO were tested by four classic functions.Experimental results show that the new algorithm not only outperforms SPSO in terms of accuracy and convergence rate but also avoids effectively being trapped in local minima.