Showing papers on "Metaheuristic published in 1980"
TL;DR: Experience with the algorithm on small problems indicates it converges exceptionally quickly to the optimal answer, often in as few iterations as are needed to perform a single simulation with no optimization using more conventional approaches.
Abstract: Based on recent work by Powell, a new optimization algorithm is presented. It merges the Newton-Raphson method and quadratic programming. A unique feature is that one does not converge the equality and tight inequality constraints for each step taken by the optimization algorithm. The article show how to perform the necessary calculations efficiently for very large problems which require the use of mass memory. Experience with the algorithm on small problems indicates it converges exceptionally quickly to the optimal answer, often in as few iterations (5 to 15) as are needed to perform a single simulation with no optimization using more conventional approaches.
66 citations
TL;DR: An improved, nonlinear, constrained mathematical programming optimization algorithm that couples a rotating coordinate pattern search with a feasible direction finding procedure used at points of pattern search termination that proved superior to all others in the overall generality and efficiency rating.
10 citations
TL;DR: An Integrated Reliability Optimization Model that includes both of these two methods for improving the reliability of a system is presented and it is shown that under certain assumptions the integrated model reduces to a Redundancyoptimization Model and under certain other assumptions reduces to an Design Optimization model.
Abstract: The two traditional methods used in improving the reliability of a multi-stage system are examined The first method is the creation of redundancy in system components, whereas the second method consists of overdesigning the system components An Integrated Reliability Optimization Model that includes both of these two methods for improving the reliability of a system is presented It is shown that under certain assumptions the integrated model reduces to a Redundancy Optimization Model and under certain other assumptions reduces to a Design Optimization Model Several methods that have been previously suggested for obtaining solutions to the Integrated Optimization Model are reviewed and a generalized solution procedure for such a model is presented This solution procedure involves the successive solution of two subproblems a number of times The first subproblem is a design optimization problem that is solved by the Davidon-Fletcher-Powell optimization algorithm The second subproblem is a reliability redundancy optimization problem that is solved with the heuristic approach of Aggarwal, et al
10 citations
TL;DR: A modification to the pattern search of Hooke and Jeeves is given which improves convergence and is particularly useful when solving constrained optimization problems by a penalty function approach.
Abstract: A modification to the pattern search of Hooke and Jeeves is given which improves convergence. The modification is particularly useful when solving constrained optimization problems by a penalty function approach.
6 citations
5 citations
3 citations
01 Jan 1980
TL;DR: A software concept developed during realisation of an optimization algorithm for solving a class of large-scale problems with a linear constraint system showing a nested structure with coupling variables seems to be applicable to a fairly general class of algorithms in Hierarchical Optimization.
Abstract: This paper deals with a software concept, which was developed during realisation of an optimization algorithm for solving a class of large-scale problems with a linear constraint system showing a nested structure with coupling variables. Special attention shall be drawn to the interactions between the software concept to be designed and choice and realisation of the mathematical method. The principal ideas, on which the software concept is based, seem to be applicable to a fairly general class of algorithms in Hierarchical Optimization.