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