【摘要】 集群智能领域的研究正呈爆炸趋势增长,每年都有无数新的集群智能算法以及改进算法被提出,这些算法在各自的领域内都扮演着相当重要的角色。从集群智能算法的特点与待解决问题出发,首先介绍集群智能算法的概念及部分经典算法,重点介绍粒子群算法与蚁群优化算法的主要思想;然后根据不同集群智能算法在不同应用问题的差异表现,对当下的几个热点问题如Ad Hoc网络、大数据与机器学习、智能电网与智慧交通等领域的集群智能算法作了简单介绍;其次是关于集群智能算法领域理论研究的讨论,主要针对集群智能算法智能行为的产生机制、不同集群智能算法在面对同一问题的性能表现不同的原因、场景选定后集群智能算法性能最优的设计方法等问题展开,并给出了这些研究具有代表性的工作及未来的研究方向;最后对集群智能算法研究尤其是基础理论研究的发展方向进行了展望。更多还原

【Abstract】 The research of swarm intelligence is growing explosively. Countless new swarm intelligence algorithms and their improved algorithms are proposed every year. These algorithms play a very important role in their respective fields. Starting from the characteristics of swarm intelligence algorithm and the problems to be solved, this article first introduces the concept of swarm intelligence and some classic algorithms, and focuses on the main ideas of particle swarm optimization and ant colony optimization algorithms. Then, according to the performance differences of different swarm intelligence algorithms in different application problems, a brief introduction of swarm intelligence algorithms in several current hot issues such as Ad Hoc networks, big data and machine learning, smart grids and smart transportation is given. Followed by the discussion on the theoretical research of swarm intelligence algorithms mainly focuses on the generation mechanism of intelligence behavior in swarm intelligence algorithms, the reason why different swarm intelligence algorithms perform differently in the same problem, and the method to optimize the performance of the swarm intelligent algorithm in certain problem. The representative work and future research directions of these studies are given. Finally, the development direction of swarm intelligence algorithm research, especially basic theory research, is prospected.更多还原