An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications

Abstract

This paper proposes a new support vector machine (SVM) optimization scheme based on an improved chaotic fly optimization algorithm (FOA) with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. In the improved FOA, the chaotic particle initializes the fruit fly swarm location and replaces the expression of distance for the fruit fly to find the food source. However, the proposed mutation strategy uses two distinct generative mechanisms for new food sources at the osphresis phase, allowing the algorithm procedure to search for the optimal solution in both the whole solution space and within the local solution space containing the fruit fly swarm location. In an evaluation based on a group of ten benchmark problems, the proposed algorithm’s performance is compared with that of other well-known algorithms, and the results support the superiority of the proposed algorithm. Moreover, this algorithm is successfully applied in a SVM to perform both parameter setting turning for the SVM and feature selection to solve real-world classification problems. This method is called chaotic fruit fly optimization algorithm (CIFOA)-SVM and has been shown to be a more robust and effective optimization method than other well-known methods, particularly in terms of solving the medical diagnosis problem and the credit card problem.

A Novel Real‐Time Image Restoration Algorithm in Edge Computing

“…The results of each iteration are recorded. When the algorithm is locally optimal, represents the best individual [13,14], and represents the worst individual in each iteration. The size of the Drosophila population is represented by .…”

“…In formula (14), ∈ is expressed as the weight value of the vector, ∈ represents the threshold calculated for the data, and (⋅) is expressed as the inner product operation of the data. The value of and is finally obtained.…”

“…The server was simulated in Windows 7 operating system (i5-3210M, 2.5 GHz CPU, 8.00 GB), and the TFOA algorithm was implemented using Matlab2014a. The simulation experiment is carried out by taking the minimum value of 10 benchmark functions [14,16] as an example. The effects of parameters on the convergence accuracy and speed of the algorithm were analyzed and compared.…”

The Short-Term Power Load Forecasting Based on Sperm Whale Algorithm and Wavelet Least Square Support Vector Machine with DWT-IR for Feature Selection

“…T max t−i , i = 1, · · · , 7 represents the t − ith day's maximum temperature C 8 , · · · , C 14 T avg t−i , i = 1, · · · 7 represents the t − ith day's average temperature C 15 , · · · , C 21 T min t−i , i = 1, · · · 7 represents the t − ith day's minimum temperature C 22 R t represents the tth day's precipitation C 23 W t represents the wind speed on day t C 24 H t represents the humidity on day t C 25 Cld t represents the cloud situation on day t C 26 M t represents the month in which day t is located C 27 Sea t represents the season in which day t is located C 28 Hol t represent whether day t is holiday, 0 is holiday, 1 is not holiday. C 29 Wk t represent whether day t is weekend, 0 is weekend, 1 is not weekend.…”

“…Through the above analysis, we can find that some researchers usually apply optimization algorithms to optimize the parameters of LSSVM, so as to solve the blind selection problems of the penalty coefficient and kernel parameters. There are various optimization algorithms that used to optimize the parameters of SVM in terms of overcoming local optimal problem, such as particle swarm optimization algorithm (PSO) [21], genetic algorithm (GA) [14,22], artificial bee colony algorithm (ABC) [23], fruit fly optimization algorithm (FOA) [17,18,23], ant colony optimization algorithm (ACO) [24], imperialist competitive algorithm (ICA) [19], cuckoo search algorithm (CS) [20], etc.…”