Experimental Comparisons of Derivative Free Optimization Algorithms (Invited Paper)
نویسندگان
چکیده
— 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 performances in the conditioning of the problem and rotational invariance of the algorithms are in particular investigated.
منابع مشابه
Experimental 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 کاملExperimental Comparisons of Derivative Free Optimization Algorithms1
— 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 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 کاملQuery Complexity of Derivative-Free Optimization
This paper provides lower bounds on the convergence rate of Derivative Free Optimization (DFO) with noisy function evaluations, exposing a fundamental and unavoidable gap between the performance of algorithms with access to gradients and those with access to only function evaluations. However, there are situations in which DFO is unavoidable, and for such situations we propose a new DFO algorit...
متن کاملDerivative-free optimization: a review of algorithms and comparison of software implementations
This paper addresses the solution of bound-constrained optimization problems using algorithms that require only the availability of objective function values but no derivative information. We refer to these algorithms as derivative-free algorithms. Fueled by a growing number of applications in science and engineering, the development of derivativefree optimization algorithms has long been studi...
متن کامل