Showing papers on "Metaheuristic published in 1970"

Searching for the Multiplier in One-Constraint Optimization Problems

Abstract: One-constraint optimization problems are approached via Lagrange multipliers. Sequential search schemes for generating suitable trial multiplier values are compared, and it is shown that, in general, the minimax sequential search is bisection. For certain applications, it pays to design search procedures that take advantage of special structure, such as recursively defined functions. An efficient, one-pass search procedure based on bisection for a multi-item, multi-echelon inventory problem is also presented.

An Approach to Solving Linear Discrete Optimization Problems

Abstract: An approach to solving general linear 0-1 restricted optimization problems on a digital computer is presented. The approach used employs a combination of partial and random search conducted over the space of 2 '~ n-dimensional binary vectors. The partial search moves from randomly selected starting points, through the space, to precisely characterized local optima. Random search is then reapplied in an attempt to overcome the problem of generating a locally optimum solution which is not globally optimum. The resulting algorithm has been implemented on the GE 635 computer. Computational experience obtained thus far indicates that the approach taken is feasible for solving such problems and offers the possibilities of obtaining good solutions to large problems at modest computation costs.

Minimizing the Number of Operations in Certain Discrete-Variable Optimization Problems

Abstract: This paper deals with the solution to a discrete optimization problem by decomposition. It develops a criterion for ranking the decomposition procedures, discusses the properties of the optimal decompositions, and gives an algorithm for finding the best decomposition in the case of no storage limitation.

Solution of Practical Optimization Problems

Abstract: A method is described for the solution of both linear and nonlinear optimization problems with inequality constraints. Use of the suggested method permits the point of conditional absolute minimum to be found directly when the objective function has a single minimum and when the constraints generate a feasible design region. The method is based on simple repetitive calculations and is, therefore, especially suitable for use with a digital computer. Beacuse of the procedure of the calculation process, the method is called the "direct search along active constraints."

Search Methods In Structural Optimization —Mixed-discrete Variables And ParallelComputing

Abstract: The basic concepts of two search methods in structural optimization, genetic algorithms and evolution strategies, are introduced. These methods require only information of function-values. Because of their simple search mechanisms, they are well suited for wide classes of optimization problems . The increasing availability of high-speed and parallel computing caused a renewed interest in these zero-order methods, in particular in Monte-Carlo techniques, genetic algorithms and evolution strategies, which are described herein.