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Neural Networks Approaches for Combinatorial Optimization Problems

In: Handbook of Combinatorial Optimization

Author

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  • Theodore B. Trafalis

    (University of Oklahoma, School of Industrial Engineering)

  • Suat Kasap

    (University of Oklahoma, School of Industrial Engineering)

Abstract

Most of the engineering design problems and applications can be formulated as a nonlinear programming problem in which the objective function is nonlinear and has many local optima in its feasible region. It is desirable to find a local optimum that corresponds to the global optimum. The problem of finding the global optimum is known as the global optimization problem. Most such global optimization problems are difficult to solve. The main difficulties in finding the global optimum are that there are no operationally useful optimality conditions for identifying whether a point is indeed a global optimum, except in cases of special structured problems [33] and so it is computationally intensive to obtain the global optimum. Therefore, it is desirable and sometimes necessary to find a near global optimum in a reasonable time rather than obtaining the global optimum.

Suggested Citation

  • Theodore B. Trafalis & Suat Kasap, 1999. "Neural Networks Approaches for Combinatorial Optimization Problems," Springer Books, in: Ding-Zhu Du & Panos M. Pardalos (ed.), Handbook of Combinatorial Optimization, pages 259-293, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4757-3023-4_5
    DOI: 10.1007/978-1-4757-3023-4_5
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