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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >Multi swarm optimization algorithm with adaptive connectivity degree
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Multi swarm optimization algorithm with adaptive connectivity degree

机译：具有自适应连接度的多群优化算法

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摘要

Particle swarm optimization algorithms are very sensitive to their population topologies. In all PSO variants, each particle adjusts it flying velocity according to a set of attractor particles. The cardinality of this set is a feature of neighborhood topology. In order to investigate this property exclusively, this paper defines the concept of connectivity degree for the particles and presents an approach for its adaptive adjustment. The presented approach is based on cellular learning automata (CLA). The entire population of the particles is divided into several swarms, and each swarm is resided in one cell of a CLA. Each cell of the CLA also contains a group of learning automata. These learning automata are responsible for adjusting the connectivity degrees of their related particles. This task is achieved through periods of learning. In each period, the learning automata realize suitable connectivity degrees for the particles based on their experienced knowledge. The empirical studies on a divers set of problems with different characteristics show that the proposed multi swarm optimization method is quite effective in solving optimization problems.
机译：粒子群优化算法对他们的人口拓扑非常敏感。在所有PSO变体中，每个颗粒根据一组吸引物颗粒调节它的飞速。该组的基数是邻域拓扑的一个特征。为了专门调查此属性，本文定义了粒子的连接程度的概念，并提出了一种自适应调整的方法。提出的方法是基于蜂窝学习自动机（CLA）。颗粒的整个人口分为几个群体，并且每个群体都居中在一个CLA的一个细胞中。 CLA的每个单元也包含一组学习自动机。这些学习自动机负责调整相关颗粒的连接程度。这项任务是通过学习时期实现的。在每个时期，学习自动机基于经验丰富的知识来实现​​粒子的合适的连通性度。不同特征潜水问题的实证研究表明，所提出的多群优化方法在解决优化问题方面非常有效。

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