The Particle Swarm Optimisation (PSO) algorithm was inspired by the social and biological
behaviour of bird flocks searching for food sources. In this nature-based algorithm …

Particle swarm optimization (PSO) has undergone many changes since its introduction in
1995. As researchers have learned about the technique, they have derived new versions …

讨论微粒群算法的开发与应用. 首先回顾从1995 年以来的开发过程, 然后根据一些已有的测试
结果对其参数设置进行系统地分析, 并讨论一些非标准的改进手段, 如簇分解, 选择方法 …

Over the ages, nature has constantly been a rich source of inspiration for science, with much
still to discover about and learn from. Swarm Intelligence (SI), a major branch of artificial …

Adjusting the search behaviors of swarm-based algorithms during their execution is a
fundamental errand for addressing real-world global optimizing challenges. Along this line …

Metaheuristics are widely recognized as efficient approaches for many hard optimization
problems. This paper provides a survey of some of the main metaheuristics. It outlines the …

As a clean and renewable energy source, wind energy has been increasingly gaining global
attention. Wind speed forecast is of great significance for wind energy domain: planning and …

在对基本PSO 算法分析的基础上, 提出了一种能够保证以概率1 收敛于全局最优解的PSO 算法—
随机PSO 算法(Stochastic PSO, 简称SPSO), 并利用F. Solis 和R. Wets 的研究结果对其全局 …

The particle swarm is an algorithm for finding optimal regions of complex search spaces
through the interaction of individuals in a population of particles. This paper analyzes a …

Swarm intelligence refers to a kind of problem-solving ability that emerges in the interactions
of simple information-processing units. The concept of a swarm suggests multiplicity …