A novel particle swarm optimization algorithm for non-separable and ill-conditioned problems
A novel particle swarm optimization algorithm for non-separable and ill-conditioned problems
复制标题
DOI：
10.1109/smc.2016.7844551
复制
发表时间：
2016-10
期刊：
影响因子：
--
通讯作者：
Yosuke Hariya;T. Shindo;K. Jin'no
中科院分区：
文献类型：
--
作者：
Yosuke Hariya;T. Shindo;K. Jin'no
粒子群优化（PSO）是一种基于人口的随机算法，专为房地产参数优化问题而设计。 PSO是一种简单而强大的算法。但是，在不可分离且条件不足的问题的情况下，PSO的性能会降低。在本文中，我们讨论了函数的Hessian矩阵与搜索分布的协方差矩阵之间的关系。需要协方差矩阵适应机制来解决非分离和条件不良的问题。因此，为了解决此类问题，我们提出了一种简单的协方差矩阵适应机制，该机制使用了个人最佳位置的差异向量。此外，我们提出了一项选择规则，以提高本地搜索能力。最后，我们通过使用测试功能阐明了所提出的方法在解决非分离和条件不良问题方面的有效性。
Particle swarm optimization (PSO) is a stochastic population-based algorithm that is designed for real-parameter optimization problems. PSO is a simple and powerful algorithm. However, the performance of PSO is degraded in the case of non-separable and ill-conditioned problems. In this article, we discuss the relation between the Hessian matrix of a function and the covariance matrix of the search distribution. The covariance matrix adaptation mechanism is required to solve non-separable and ill-conditioned problems. Therefore, in order to solve such problems, we propose a simple covariance matrix adaptation mechanism that uses the difference vector of the personal best positions. In addition, we propose a selection rule to improve the local search ability. Finally, we clarify the effectiveness of the proposed method in solving non-separable and ill-conditioned problems by using test functions.