Showing papers on "Metaheuristic published in 1986"
01 Jan 1986
TL;DR: GA's are shown to be effective for both levels of the systems optimization problem and are applied to the second level task of identifying efficient GA's for a set of numerical optimization problems.
Abstract: The task of optimizing a complex system presents at least two levels of problems for the system designer. First, a class of optimization algorithms must be chosen that is suitable for application to the system. Second, various parameters of the optimization algorithm need to be tuned for efficiency. A class of adaptive search procedures called genetic algorithms (GA) has been used to optimize a wide variety of complex systems. GA's are applied to the second level task of identifying efficient GA's for a set of numerical optimization problems. The results are validated on an image registration problem. GA's are shown to be effective for both levels of the systems optimization problem.
2,924 citations
Journal Article•
TL;DR: In this article, the application of a GA to the optimal design of a ten member, plane truss is considered, and results show surprising speed as near-optimal results are obtained after examining a small fraction of the search space.
Abstract: The application of a genetic algorithm (GA) to the optimal design of a ten member, plane truss is considered. Genetic algorithms are search procedures based upon the mechanics of natural genetics, combining a Darwinian survival-of-the-fittest with a randomized, yet structured information exchange among a population of artificial chromosomes. Computer results show surprising speed as near-optimal results are obtained after examining a small fraction of the search space. The method is ready for application to more complex problems of engineering optimization.
309 citations
53 citations
TL;DR: A technique is presented for extending the constrained search approach used in MINOS to exploring integer-feasible solutions once a continuous optimal solution is obtained.
Abstract: This paper describes recent experience in tackling large nonlinear integer programming problems using the MINOS large-scale optimization software. A technique is presented for extending the constrained search approach used in MINOS to exploring integer-feasible solutions once a continuous optimal solution is obtained. Computational experience with this approach is described for two classes of problems: quadratic assignment problems and pipeline network design problems.
49 citations
19 citations
10 citations
01 Jan 1986
5 citations
TL;DR: Golomb as discussed by the authors showed that Riemann's zeta function induces a probability distribution on the positive integers, for any s > 1, and studied some of its properties connected to divisibility.
Abstract: Abstract S. Golomb noticed that Riemann's zeta function ζ induces a probability distribution on the positive integers, for any s > 1, and studied some of its properties connected to divisibility. The object of this paper is to show that the probability distribution mentioned above is the unique solution of an entropy-maximization problem.
3 citations
TL;DR: This paper formulate an optimization problem that is equivalent to a general equilibrium problem that cannot be easily represented in an explicit form, and the direct application of an available nonlinear optimization algorithm is not plausible.
3 citations
01 Jan 1986
2 citations
TL;DR: The Kristers LU method, a simple algorithm for optimization with discrete design variables is presented and it is shown how his algorithm can be improved by using a one-step line search algorithm and/or the cutting plane method.
01 Jan 1986
TL;DR: This work presents an overview of the use of semi-infinite optimization algorithms in linear, multivariable control system design and deals with problem formulation, basics of algorithms, numerical aspects and software requirements.
Abstract: We present an overview of the use of semi-infinite optimization algorithms in linear, multivariable control system design. We deal with problem formulation, basics of algorithms, numerical aspects and software requirements.