Showing papers on "Extremal optimization published in 1987"

An analogue approach to the travelling salesman problem using an elastic net method

Abstract: The travelling salesman problem is a classical problem in the field of combinatorial optimization, concerned with efficient methods for maximizing or minimizing a function of many independent variables. Given the positions of N cities, which in the simplest case lie in the plane, what is the shortest closed tour in which each city can be visited once? We describe how a parallel analogue algorithm, derived from a formal model for the establishment of topographically ordered projections in the brain, can be applied to the travelling salesman problem. Using an iterative procedure, a circular closed path is gradually elongated non-uniformly until it eventually passes sufficiently near to all the cities to define a tour. This produces shorter tour lengths than another recent parallel analogue algorithm, scales well with the size of the problem, and is naturally extendable to a large class of optimization problems involving topographic mappings between geometrical structures.

Towards practical `neural' computation for combinatorial optimization problems

Abstract: We propose a hill climbing attachment called Iterated Descent useful in conjunction with any local search algorithm, including neural net algorithms.

A Coupled Algorithmic-Heuristic Approach for Design Optimization

Abstract: An optimization strategy is presented that provides a frame-work in which optimization algorithms and heuristic procedures can be coupled to solve nonlinearly constrained design optimization problems These problems cannot be efficiently solved by either approach independently The approach is based on an optimization algorithm dealing with local monotonicity and sequential quadratic programming techniques with heuristic procedures which are statistically derived from observations obtained by applying the optimization algorithm to different classes of test problems