A Coupled Gradient Network Approach for Statistic and Temporal Mixed-Integer Optimization  

Oleh:

Hassoun, M. H. ; Watta, P. B.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 7 no. 3 (1996), page 578-593.
Topik: optimization; gradient network; static; mixed - integer; optimization

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.1 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Utilizes the ideas of artificial neural networks to propose new solution methods for a class of constrained mixed - integer optimization problems. These new solution methods are more suitable to parallel implementation than the usual sequential methods of mathematical programming. Another attractive feature of the proposed approach is that some global search mechanisms may be easily incorporated into the computation, producing results which are more globally optimal. To formulate the solution method proposed in this paper, a penalty function approach is used to define a coupled gradient - type network with an appropriate architecture, energy function and dynamics such that high - quality solutions may be obtained upon convergence of the dynamics. Finally, it is shown how the coupled gradient net may be extended to handle temporal mixed - integer optimization problems, and simulations are presented which demonstrate the effectiveness of the approach.

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