Selfish herds optimization algorithm with orthogonal design and information update for training multi-layer perceptron neural network
作者: Ruxin ZhaoYongli WangPeng HuHamed JelodarChi YuanYanChao LiIsma MasoodMahdi Rabbani
作者单位: 1School of Computer Science and Engineering, Nanjing University of Science and Technology
刊名: Applied Intelligence, 2019, Vol.49 (6), pp.2339-2381
来源数据库: Springer Nature Journal
DOI: 10.1007/s10489-018-1373-1
关键词: Selfish herd optimization algorithmOrthogonal designMulti-layer perceptron (MLP) neural networkInformation updateMeta-heuristic optimization algorithm
英文摘要: Abstract(#br)Selfish herd optimization algorithm is a novel meta-heuristic optimization algorithm, which simulates the group behavior of herds when attacked by predators in nature. With the further research of algorithm, it is found that the algorithm cannot get a better global optimal solution in solving some problems. In order to improve the optimization ability of the algorithm, we propose a selfish herd optimization algorithm with orthogonal design and information update (OISHO) in this paper. Through using orthogonal design method, a more competitive candidate solution can be generated. If the candidate solution is better than the global optimal solution, it will replace the global optimal solution. At the same time, at the end of each iteration, we update the population information...
全文获取路径: Springer Nature  (合作)
影响因子:1.853 (2012)