Improved particle swarm optimization and applications to Hidden Markov Model and Ackley function

Saeed Motiian, H. Soltanian-Zadeh
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引用次数: 13

Abstract

Particle Swarm Optimization (PSO) is an algorithm based on social intelligence, utilized in many fields of optimization. In applications like speech recognition, due to existence of high dimensional matrices, the speed of standard PSO is very low. In addition, PSO may be trapped in a local optimum. In this paper, we introduce a novel algorithm that is faster and generates superior results than the standard PSO. Also, the probability of being trapped in a local optimum is decreased. To illustrate advantages of the proposed algorithm, we use it to train a Hidden Markov Model (HMM) and find the minimum of the Ackley function.
改进粒子群算法及其在隐马尔可夫模型和Ackley函数中的应用
粒子群优化算法(PSO)是一种基于社会智能的算法,应用于许多优化领域。在语音识别等应用中,由于高维矩阵的存在,标准粒子群算法的速度非常低。此外,粒子群可能陷入局部最优。在本文中,我们介绍了一种新的算法,它比标准粒子群算法更快,产生更好的结果。同时,降低了陷入局部最优的概率。为了说明该算法的优点,我们使用它来训练隐马尔可夫模型(HMM)并找到Ackley函数的最小值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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