Evolutionary pattern search algorithms for unconstrained and linearly constrained optimization
نویسندگان
چکیده
منابع مشابه
Evolutionary pattern search algorithms for unconstrained and linearly constrained optimization
Redescribe aconvergence theory forevolutionary pattern search algorithms (EPS.4S) ona broad class of unconstrained and linearlyconstrainedproblems. EPSAS adaptively modify the step size of the mutation operator in response to the success of previous optimization steps. The design of EPSAS is inspired by recent analysesof pattern search methods. Our analysis significantly extends the previous co...
متن کاملNewton-type methods for unconstrained and linearly constrained optimization
This paper describes two numerically stable methods for unconstrained optimization and their generalization when linear inequality constraints are added. The difference between the two methods is simply that one requires the Hessian matrix explicitly and the other does not. The methods are intimately based on the recurrence of matrix factorizations and are linked to earlier work on quasi-Newton...
متن کاملPattern Search Methods for Linearly Constrained Minimization
We extend pattern search methods to linearly constrained minimization. We develop a general class of feasible point pattern search algorithms and prove global convergence to a KarushKuhn-Tucker point. As in the case of unconstrained minimization, pattern search methods for linearly constrained problems accomplish this without explicit recourse to the gradient or the directional derivative of th...
متن کاملFilter Pattern Search Algorithms for Mixed Variable Constrained Optimization Problems
A new class of algorithms for solving nonlinearly constrained mixed variable optimization problems is presented. This class combines and extends the Audet-Dennis Generalized Pattern Search (GPS) algorithms for bound constrained mixed variable optimization, and their GPS-filter algorithms for general nonlinear constraints. In generalizing existing algorithms, new theoretical convergence results ...
متن کاملStationarity Results for Generating Set Search for Linearly Constrained Optimization
We derive new stationarity results for derivative-free, generating set search methods for linearly constrained optimization. We show that a particular measure of stationarity is of the same order as the step length at an identifiable subset of the iterations. Thus, even in the absence of explicit knowledge of the derivatives of the objective function, we still have information about stationarit...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Evolutionary Computation
سال: 2001
ISSN: 1089-778X
DOI: 10.1109/4235.942532