C++ Neural Networks and Fuzzy Logic: Preface

•  Training neural nets with the reactive Tabu search

•  Tabu Learning: a neural network search method for solving nonconvex optimization problems

•  Massively parallel Tabu search for the quadratic assignment problem

•  Connection machine implementation of a Tabu search algorithm for the traveling salesman

problem

•  A Tabu search procedure for multicommodity location/allocation with balancing requirements

Summary

The traveling salesperson problem is presented in this chapter as an example of nonlinear optimization with

neural networks. Details of formulation are given of the energy function and its evaluation. The approaches to

the solution of the traveling salesperson problem using a Hopfield network and using a Kohonen

self−organizing map are presented. C++ programs are included for both approaches.

The output with the C++ program for the Hopfield network refers to examples of four− and five−city tours.

The output with the C++ program for the Kohonen approach is given for a tour of five cities, for illustration.

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The solution obtained is good, if not optimal. The problem with the Hopfield approach lies in the selection of

appropriate values for the parameters. Hopfield’s choices are given for his 10−city tour problem. The same

values for the parameters may not work for the case of a different number of cities. The version of this

approach given by Anzai is also discussed briefly.

Use of neural networks for nonlinear optimization as applied to portfolio selection is also presented in this

chapter. You are introduced to Tabu search and its use in optimization with neural computing.

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C++ Neural Networks and Fuzzy Logic:Preface

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