Extended Hopfield Model of Neural Networks for Combinatorial Multiobjective Optimization Problems

被引：24

作者：

Balicki, J ^{[1
]}

Kitowski, Z ^{[1
]}

Stateczny, A ^{[1
]}

机构：

[1] Polish Naval Acad, PL-81919 Gdynia 19, Poland

来源：

IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE | 1998年

关键词：

D O I：

10.1109/IJCNN.1998.686025

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, an extended Hopfield model of a neural network for solving NP-hard combinatorial multiobjective optimization problems has been proposed Some models for satisfaction of representative constraints have been studied Moreover, the Hopfield model for solving combinatorial constrained optimization problems with linear objective function has been considered Afterwards the network model for solving combinatorial constrained optimization problems with quasi-quadratic function has been considered Finally, the family of extended Hopfield models for finding Pareto-optimal solutions have been developed. Some numerical examples related with the chosen two-objective optimization of operation allocations in distributed processing systems have been given.

引用

页码：1646 / 1651

页数：6

共 50 条

[21] Power-efficient combinatorial optimization using intrinsic noise in memristor Hopfield neural networks
Fuxi Cai
Suhas Kumar
Thomas Van Vaerenbergh
Xia Sheng
Rui Liu
Can Li
Zhan Liu
Martin Foltin
Shimeng Yu
Qiangfei Xia
J. Joshua Yang
Raymond Beausoleil
Wei D. Lu
John Paul Strachan
Nature Electronics, 2020, 3 : 409 - 418
[22] Identifying preferred solutions in multiobjective combinatorial optimization problems
Lokman, Banu
Koksalan, Murat
TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, 2019, 27 (03) : 1970 - 1981
[23] AN EFFICIENT ALGORITHM FOR SOLVING OPTIMIZATION PROBLEMS ON HOPFIELD-TYPE NEURAL NETWORKS
TANAKA, T
HIGUCHI, T
FURUYA, T
SYSTEMS AND COMPUTERS IN JAPAN, 1995, 26 (02) : 73 - 84
[24] Effective anytime algorithm for multiobjective combinatorial optimization problems
Angel Dominguez-Rios, Miguel
Chicano, Francisco
Alba, Enrique
INFORMATION SCIENCES, 2021, 565 : 210 - 228
[25] A learning method in Hopfield neural network for combinatorial optimization problem
Rong, LW
Zheng, T
Qi, PC
NEUROCOMPUTING, 2002, 48 : 1021 - 1024
[26] Optimization by means of neural networks for combinatorial problems - On the Uesaka's conjecture
Nishi, T
Imai, K
ISCAS '97 - PROCEEDINGS OF 1997 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOLS I - IV: CIRCUITS AND SYSTEMS IN THE INFORMATION AGE, 1997, : 617 - 620
[27] On the Uesaka's Conjecture as to the optimization by means of neural networks for combinatorial problems
Nishi, T
IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES, 1998, E81A (09) : 1811 - 1817
[28] General method to solve combinatorial optimization problems with random neural networks
Aguilar, J
NUMERICAL METHODS IN ENGINEERING SIMULATION, 1996, : 349 - 356
[29] Supplementing recurrent neural networks with annealing to solve combinatorial optimization problems
Khandoker, Shoummo Ahsan
Abedin, Jawaril Munshad
Hibat-Allah, Mohamed
MACHINE LEARNING-SCIENCE AND TECHNOLOGY, 2023, 4 (01):
[30] Solving Nonlinear Equality Constrained Multiobjective Optimization Problems Using Neural Networks
Mestari, Mohammed
Benzirar, Mohammed
Saber, Nadia
Khouil, Meryem
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2015, 26 (10) : 2500 - 2520

← 1 2 3 4 5 →