TY  - EJOU
AU  - Zheng, Jianguo 
AU  - Chen, Shuilin 

TI  - Multi-Strategy Boosted Spider Monkey Optimization Algorithm for Feature Selection
T2  - Computer Systems Science and Engineering

PY  - 2023
VL  - 46
IS  - 3
SN  - 

AB  - To solve the problem of slow convergence and easy to get into the local optimum of the spider monkey optimization algorithm, this paper presents a new algorithm based on multi-strategy (ISMO). First, the initial population is generated by a refracted opposition-based learning strategy to enhance diversity and ergodicity. Second, this paper introduces a non-linear adaptive dynamic weight factor to improve convergence efficiency. Then, using the crisscross strategy, using the horizontal crossover to enhance the global search and vertical crossover to keep the diversity of the population to avoid being trapped in the local optimum. At last, we adopt a Gauss-Cauchy mutation strategy to improve the stability of the algorithm by mutation of the optimal individuals. Therefore, the application of ISMO is validated by ten benchmark functions and feature selection. It is proved that the proposed method can resolve the problem of feature selection.
KW  - Spider monkey optimization; refracted opposition-based learning; crisscross strategy; Gauss-Cauchy mutation strategy; feature selection

DO  - 10.32604/csse.2023.038025