The Firefly Algorithm (FA) is a new type of algorithm that imitates the process of mutual attraction between particles or insects in the animal kingdom. It is based on the firefly's flicker and attraction characteristics to imitate the social behavior of individual fireflies. FA has been widely applied in different categories, but it also has many problems such as easy to cause the consequences of local optima+l solutions, algorithm premature and lower precision. For these defects, a new algorithm perspective is proposed based on the Cubic Mapping Model and the Rough Set theory of Firefly optimization algorithms (CM-RS-FA) in this paper. First, the cubic mapping chaotic operator is used to constitute the evenly distributed initial firefly population, and the firefly with a better position is selected. Then, using the definition and properties of rough set theory, the local convergence of the standard firefly algorithm is improved, improved algorithm uses formulas of upper approximation sets and lower approximation sets in rough sets to define feature coefficients. Then use the newly generated feature coefficients to update the position of each firefly. The CM-RS-FA is applied for function optimization, contrasted with the other three intelligent optimization algorithms, the experiment results indicate that the algorithm can get rid of the limitation of local optimization and find the global optimal solution accurately. Finally, the CM-RS-FA is applied to the data classification. Compared with other swarm intelligent optimization algorithms in classification, CM-RS-FA has higher classification accuracy on some data sets.
Improved Firefly Optimization Algorithm Based on Cubic Mapping Model and Rough Set Theory
Smart Innovation, Systems and Technologies
Advances in Smart Vehicular Technology, Transportation, Communication and Applications ; Kapitel : 3 ; 19-36
02.07.2021
18 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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