An intelligence method of swarm neural networks for equalities-constrained nonconvex optimization

摘要：

　　To deal with equalities-constrained nonconvex optimization problem, an intelligence method of swarm neural networks (SNN) is introduced in this paper.The proposed method handles the problem into two parts, which combines local searching ability of one-layer recurrent neural network (RNN) and global searching ability of Shuffled frog leaping algorithm (SFLA).First, a RNN model based on general nonconvex optimization is presented.Then the convergence property of RNN is analyzed and proved.Moreover, based on SFLA framework, neural networks are treated as frogs which must be divided into several memeplexes and evolve by its own differential equations to search a local exact solution.Next, through shuffling the best solution of each memeplex, we can obtain the global best point.Finally, numerical examples with simulation results are given to illustrate the effectiveness and good characteristics of the proposed method solving nonconvex optimization problem.

关键词： nonconvex optimization Swarm neural networks Shuffled frog leaping algorithm global best

会议类型: 国内会议

会议名称: 西南大学2014年全国博士生学术论坛(电子技术与信息科学领域)

会议地点: 重庆

会议语种:英文

页码: 341-352

在线出版日期: 2014-12-01（万方平台首次上网日期，不代表论文的发表时间）