Nonsmooth nonconvex optimization problem based on an improved porcellio scaber algorithm

被引：0
作者：
Miao, Peng [1 ]
Wu, Deyu [1 ]
Chen, Li [2 ]
机构：
[1] Zhengzhou Univ Sci & Technol, Dept Basic Courses, Zhengzhou 450064, Henan, Peoples R China
[2] Zhengdong New Dist Teaching & Res Off, Zhengzhou 450000, Henan, Peoples R China
来源：
2021 PROCEEDINGS OF THE 40TH CHINESE CONTROL CONFERENCE (CCC) | 2021年
关键词：
nonsmooth nonconvex optimization; improved porcellio scaber algorithm; group behavior; individual behavior; feasible region; NEURAL-NETWORKS; MINIMIZATION; CONVERGENCE; DESIGN;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, an improved porcellio scaber algorithm is used to solve the nonsmooth nonconvex optimization problem. The nonsmooth nonconvex optimization problem arises in a variety of scientific and engineering applications. But, it is very difficult to obtain their optimal solution. In order to overcome this, an improved porcellio scaber algorithm is proposed to solve the nonsmooth nonconvex optimization problem which satisfies some assumptions. The improved porcellio scaber algorithm includes equality and inequality constraints of the nonsmooth nonconvex optimization problem. Its pseudo code is given and two typical examples are used to show the effectiveness of our method.
引用
收藏
页码：1634 / 1638
页数：5
相关论文
共 29 条
  • [1] Bertsekas D. P., 1999, NONLINEAR PROGRAMMIN
  • [2] Subgradient-Based Neural Networks for Nonsmooth Nonconvex Optimization Problems
    Bian, Wei
    Xue, Xiaoping
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS, 2009, 20 (06): : 1024 - 1038
  • [3] Proximal alternating linearized minimization for nonconvex and nonsmooth problems
    Bolte, Jerome
    Sabach, Shoham
    Teboulle, Marc
    [J]. MATHEMATICAL PROGRAMMING, 2014, 146 (1-2) : 459 - 494
  • [4] Stabilization via nonsmooth, nonconvex optimization
    Burke, James V.
    Henrion, Didier
    Lewis, Adrian S.
    Overton, Michael L.
    [J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2006, 51 (11) : 1760 - 1769
  • [5] Chen Y., 2020, ARXIV200711245
  • [6] Feasible methods for nonconvex nonsmooth problems with applications in green communications
    Facchinei, Francisco
    Lampariello, Lorenzo
    Scutari, Gesualdo
    [J]. MATHEMATICAL PROGRAMMING, 2017, 164 (1-2) : 55 - 90
  • [7] Generalized neural network, for nonsmooth nonlinear programming problems
    Forti, M
    Nistri, P
    Quincampoix, M
    [J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS, 2004, 51 (09) : 1741 - 1754
  • [8] Convergence of neural networks for programming problems via a nonsmooth Lojasiewicz inequality
    Forti, Mauro
    Nistri, Paolo
    Quincampoix, Marc
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS, 2006, 17 (06): : 1471 - 1486
  • [9] Blind Separation of Quasi-Stationary Sources: Exploiting Convex Geometry in Covariance Domain
    Fu, Xiao
    Ma, Wing-Kin
    Huang, Kejun
    Sidiropoulos, Nicholas D.
    [J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2015, 63 (09) : 2306 - 2320
  • [10] A Unified Algorithmic Framework for Block-Structured Optimization Involving Big Data
    Hong, Mingyi
    Razaviyayn, Meisam
    Luo, Zhi-Quan
    Pang, Jong-Shi
    [J]. IEEE SIGNAL PROCESSING MAGAZINE, 2016, 33 (01) : 57 - 77
123