Benchmarking Derivative-Free Optimization Algorithms
نویسندگان
چکیده
منابع مشابه
Benchmarking Derivative-Free Optimization Algorithms
We propose data profiles as a tool for analyzing the performance of derivativefree optimization solvers when there are constraints on the computational budget. We use performance and data profiles, together with a convergence test that measures the decrease in function value, to analyze the performance of three solvers on sets of smooth, noisy, and piecewise-smooth problems. Our results provide...
متن کاملFairer Benchmarking of Optimization Algorithms via Derivative Free Optimization
Research in optimization algorithm design is often accompanied by benchmarking a new algorithm. Some benchmarking is done as a proof-of-concept, by demonstrating the new algorithm works on a small number of difficult test problems. Alternately, some benchmarking is done in order to demonstrate that the new algorithm in someway out-performs previous methods. In this circumstance it is important ...
متن کاملExperimental Comparisons of Derivative Free Optimization Algorithms
— In this paper, the performances of the quasi-Newton BFGS algorithm, the NEWUOA derivative free optimizer, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), the Differential Evolution (DE) algorithm and Particle Swarm Optimizers (PSO) are compared experimentally on benchmark functions reflecting important challenges encountered in real-world optimization problems. Dependence of the...
متن کاملTwo derivative-free optimization algorithms for mesh quality improvement
High-quality meshes are essential in the solution of partial differential equations (PDEs), which arise in numerous science and engineering applications, as the mesh quality affects the solution accuracy, the solver execution time, and the problem conditioning. Mesh quality improvement is necessary when the mesh is of less than desirable quality (either from mesh generation or deformation). Non...
متن کاملEmpirical comparisons of several derivative free optimization algorithms
In this paper, the performances of the quasi-Newton BFGS algorithm, the NEWUOA derivative free optimization algorithm, the CovarianceMatrix Adaptation Evolution Strategy (CMAES), the Differential Evolution (DE) algorithm and a Particle Swarm Optimization (PSO) algorithm are compared experimentally on benchmark functions reflecting important challenges encountered in real-world optimization prob...
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: SIAM Journal on Optimization
سال: 2009
ISSN: 1052-6234,1095-7189
DOI: 10.1137/080724083