关键词: 群体智能算法, 图像分割, 蚁群算法, 粒子群算法, 麻雀搜索算法, 蝙蝠算法, 鲸鱼优化算法, 蜉蝣算法

Abstract:

The general method of image segmentation has always been a hot and difficult point in image processing. With the rise and development of artificial intelligence, swarm intelligence algorithms have become the current hot research direction. Image segmentation technology combined with swarm intelligence algorithms has become a new and effective improvement method. The swarm intelligence algorithm simulates the law of action of things in nature, combines artificial intelligence and swarm biology, searches for the optimal solution in the solution space, and provides new solutions to solve complex problems. It describes the research status and development process of swarm intelligence algorithms, combining the early Ant Colony Optimization（ACO）, the classic Particle Swarm Optimization Algorithm（PSO） and the newer Sparrow Search Algorithm（SSA） as an example to introduce its algorithm principles and methods in detail, and briefly describes the principles of Bat Algorithm（BA）, Whale Optimization Algorithm（WOA）, Artifical Bee Colony Algorithm（ABC）, Firefly Algorithm（FA）, Cuckoo Search（CS）, Bacterial Foraging Optimization（BFO） and the latest Mayfly Algorithm（MA）, based on this, combines with domestic and foreign literature to analyze and summarize the improved method of the above algorithm and the comprehensive improvement and application of image segmentation technology. The swarm intelligence algorithm combined with the representative algorithms of image segmentation technology is extracted for list analysis and summary, and then the unified framework, common characteristics, different differences and existing problems of the swarm intelligence algorithm are summarized. Finally, the future trend is prospected.

Key words: swarm intelligence algorithm, image segmentation, ant colony algorithm, particle swarm optimization algorithm, sparrow search algorithm, bat algorithm, whale optimization algorithm, mayfly algorithm