Sugeno模糊积分粒子群优化算法的实验研究

Experimental Research of Particle Swarm Optimization Algorithm Using Sugeno Fuzzy Integral

粒子群优化算法是模拟鸟类觅食的行为思想的随机搜索算法,主要是通过迭代寻找最优解。将模糊积分技术引入优化算法调整粒子的多样性的同时动态改变惯性权重,以此来提高粒子的搜索能力。仿真实验结果表明,该方法大大提高了搜索过程中粒子的多样性,并缩短了粒子的搜索时间,保持快速的收敛性的同时获得了算法最优解。

The fuzzy integral which is a aggregation technique is introduced to improve the base Particle Swarm Optimization （PSO） in this article. It is incorporated into the PSO to increase the diversity of the particles and to improve the weights. In our paper, the new improved PSO algorithm we proposed is based on the Sugeno fuzzy integral. This generalized approach was developed to address the Sugeno fuzzy integral for enhanced the diversity of particles and inertia weight. The simulation empirical result shows that the proposed method has a strong ability to find the optimistic solution, keeps a rapid convergence and the higher precision solution than PSO algorithm.