关键词: 蚁狮优化算法, 混沌, 侦查机制, 支持向量机, 参数优化

Abstract: As ant lion optimizer (ALO) is a new bionic intelligence algorithm, there are a number of respects on the improvement and development. Since antlion’s population (species) has some poor-fitness individuals in basic ALO algorithm, the behavior of ants selecting those antlions for random walk will result in increasing the possibility of its trapping into local optima and impacting on the algorithm’s optimal performance. Considering this question, this paper proposes ant lion optimizer with chaotic investigation mechanism (CIALO), which draws experience from the investigation idea of artificial bee colony algorithm (ABC) and brings in chaos search mechanism based on the original information of antlions. The CIALO algorithm firstly defines poor-fitness individuals of the sorted antlions’ population as investigative ant lions (IAL). Meanwhile, the original position information of these antlions is regarded as the initial value of Fuch chaotic mapping. Then it can gain a better-much position by a certain number of chaos search iteration and reassigns the position to IAL, which is beneficial to improve the superiority of antlion’s population and the optimal performance of the algorithm. Eventually, the CIALO algorithm is used to optimize the parameters of support vector machine (SVM). The public datasets from University of California Irvine (UCI) is employed for evaluating the proffered algorithm. The experimental results imply that the CIALO algorithm for optimizing SVM parameters has stronger optimal performance and better stability of the algorithm.

Key words: ant lion optimizer, chaos, investigation mechanism, support vector machine, parameter optimization