总结性自适应变异的粒子群算法

针对粒子群优化(particle swarm optimization,PSO)算法在迭代期间易陷入局部最优及寻优精度不高的缺点,提出一种总结性自适应变异的粒子群算法SCVPSO(self-conclusion and self-adaptive variation particle swarm optimizatio...

Self-Conclusion and Self-Adaptive Variation Particle Swarm Optimization

Aiming at the shortcomings of particle swarm optimization（PSO） algorithm, which is easy to fall into local optimum and low precision during iteration, this paper proposes a self-conclusion and self-adaptive variation particle swarm optimization（SCVPSO）. Firstly, the position of each particle is dynamically updated by nonlinear turning up and then decreasing inertia weight to avoid premature. Secondly, the local particles are searched backward to improve the efficiency of population optimization. Finally, a new parameter scr（self-conclusion rate） is introduced to summarize the recent solution situation of each particle, and the probability directed variation is used to guide the particles to the global optimum to increase the diversity of particles. With the help of 15 test functions, compared with other variant particle swarm optimization algorithm, the results show that the improved algorithm is significantly better than other algorithms in solving performance, which verifies the effectiveness of the strategy.