TY - JOUR
T1 - A Self-adaptive Fireworks Algorithm for Classification Problems
AU - Xue, Yu
AU - Zhao, Binping
AU - Ma, Tinghuai
AU - Pang, Wei
N1 - his work was supported in part by the National Natural Science Foundation of China under Grants 61403206 and 61771258, in part by the Natural Science Foundation of Jiangsu Province under Grants BK20141005 and BK20160910, in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant 14KJB520025, in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions, in part by the Open Research Fund of Jiangsu Engineering Research Center of Communication and Network Technology, NJUPT, under Grant JSGCZX17001, and in part by the Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an University of Technology, under Contract SKL2017CP01.
PY - 2018
Y1 - 2018
N2 - Fireworks algorithm (FWA) is a novel swarm intelligence algorithm recently proposed for solving complex optimization problems. Because of its powerful global optimization ability to solve classification problems, we first present an optimization classification model in this paper. In this model, a linear equation set is constructed according to classification problems. This optimization classification model can be solved by most evolutionary computation techniques. In this research, a self-adaptive fireworks algorithm (SaFWA) is developed so that the optimization classification model can be solved efficiently. In SaFWA, four candidate solution generation strategies (CSGSs) are employed to increase the diversity of solutions. In addition, a self-adaptive search mechanism has also been introduced to use the four CSGSs simultaneously. To extensively assess the performance of SaFWA on solving classification problems, eight datasets have been used in the experiments. The experimental results show that it is feasible to solve classification problems through the optimization classification model and SaFWA. Furthermore, SaFWA performs better than FWA, FWA variants with only one CSGS, particle swarm optimization (PSO), and differential evolution (DE) on most of the training sets and test sets.
AB - Fireworks algorithm (FWA) is a novel swarm intelligence algorithm recently proposed for solving complex optimization problems. Because of its powerful global optimization ability to solve classification problems, we first present an optimization classification model in this paper. In this model, a linear equation set is constructed according to classification problems. This optimization classification model can be solved by most evolutionary computation techniques. In this research, a self-adaptive fireworks algorithm (SaFWA) is developed so that the optimization classification model can be solved efficiently. In SaFWA, four candidate solution generation strategies (CSGSs) are employed to increase the diversity of solutions. In addition, a self-adaptive search mechanism has also been introduced to use the four CSGSs simultaneously. To extensively assess the performance of SaFWA on solving classification problems, eight datasets have been used in the experiments. The experimental results show that it is feasible to solve classification problems through the optimization classification model and SaFWA. Furthermore, SaFWA performs better than FWA, FWA variants with only one CSGS, particle swarm optimization (PSO), and differential evolution (DE) on most of the training sets and test sets.
U2 - 10.1109/ACCESS.2018.2858441
DO - 10.1109/ACCESS.2018.2858441
M3 - Article
SN - 2169-3536
VL - 6
SP - 44406
EP - 44416
JO - IEEE Access
JF - IEEE Access
ER -