Abstract
The swarm intelligence algorithm has become an important method to solve optimization problems because of its excellent self-organization, self-adaptation, and self-learning characteristics. However, when a traditional swarm intelligence algorithm faces high and complex multi-peak problems, population diversity is quickly lost, which leads to the premature convergence of the algorithm. In order to solve this problem, dimension entropy is proposed as a measure of population diversity, and a diversity control mechanism is proposed to guide the updating of the swarm intelligence algorithm. It maintains the diversity of the algorithm in the early stage and ensures the convergence of the algorithm in the later stage. Experimental results show that the performance of the improved algorithm is better than that of the original algorithm.
Highlights
This paper is organized as follows: This section introduces some basic knowledge about the swarm intelligence algorithm and its diversity; in the second section, we describe a variety of diversity models and discuss the dimensional entropy model
This paper proposes a species diversity measure based on the dimension entropy mechanism, which creatively combines dimension learning and entropy
Dimension entropy controls the population diversity of the swarm intelligence algorithm update strategy, which relies on the dimension entropy calculation of population diversity
Summary
They involve the need to determine specific performance requirements for a certain problem if there are multiple alternative solutions, and to select one of them to maximize or minimize the determined performance requirement index [1]. In order to better solve optimization problems, evolutionary algorithms simulating the process and mechanism of biological evolution and swarm intelligence algorithms simulating the foraging mechanism of biological populations have gradually become a research hotspot in recent years. Swarm intelligence refers to the behavior of group cooperation and collective intelligence presented by a group composed of many simple individuals in nature [5]. Swarm intelligence is a kind of group-based computing method with self-organization, self-adaptation, and self-learning characteristics, which is put forward by referring to and utilizing various mechanisms of natural phenomena or organisms in nature. A large number of swarm intelligence optimization algorithms have been born, among which the classic swarm intelligence optimization algorithms include the artificial bee colony algorithm [6], the ant colony algorithm [7], and the particle swarm optimization algorithm [8]
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