On the Mathematical Models and Applications of Swarm Intelligent Optimization Algorithms

With the highly increasing demand in engineering, traditional algorithms may fail to meet required performances. Recently, intelligent algorithms have been widely studied, gradually achieving successful applications in industry. Among them, swarm intelligent algorithms are a combination of intelligent algorithms and bionic swarm theory, with advantages of simple principles, high accuracy, high efficiency, wide application scenarios, solid stability, etc. However, the research of swarm intelligent algorithms is still developing, making it difficult to compare algorithms recently proposed with others. In this paper, ten swarm intelligent optimization algorithms are comprehensively discussed with principles, applications, and improvements, as well as compared and analyzed in terms of accuracy, convergence speed, and time complexity. Furthermore, a conclusion of algorithms with less parameter dependence but better performances is summarized, which can provide further insight for engineers to choose appropriate algorithms to meet actual needs.