一种基于复合惯性权值的粒子群优化算法
A particle swarm optimization algorithm based on compound inertia weight
云南民族大学学报:自然科学版,2017,26(4):306-309

贺晓春 HXC

摘要


在研究神经网络优化的问题上,粒子群优化算法被广泛应用.针对基本粒子群优化算法收敛速度慢和易陷入局部最优等问题,提出了一种改进的粒子群优化算法.该算法除了采用线性惯性权值和进化速度-聚集度动态惯性权值相结合的方式来调整其权值,还将一种新颖的收缩因子引入到算法中.通过对4种典型测试函数进行仿真测试,实验结果表明新算法在收敛速度、收敛精度、改善优化性能上完全优于基本的粒子群优化算法,有效避免了基本群优化算法的缺陷. An improved particle swarm optimization algorithm is proposed to solve the problem that PSO algorithm has a slow convergence speed and falls easily into local optimization. Besides, it makes use of the classical linear inertia weight and the dynamical inertia weight to adjust relevant weight percentages, and introduces some new constriction factors to the algorithm. The proposed algorithm is tested with four well-known benchmark functions. The experimental results show that the new algorithm has great convergence accuracy and convergence velocity.

参考



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