Document Type
Poster
Publication Date
4-17-2019
Abstract
For many situations, the function that best models a situation or data set can have a derivative that may be difficult or impossible to find. Thus, numerical methods for finding these important values without the direct involvement of the derivative have been developed to find the optimal value of the function. This is our motivation to use Derivative-free optimization (DFO) algorithms. In our analysis of these algorithms, we tested three global solvers: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulating Annealing (SA) on a set of 25 problems of varying in convex/non-convex, separable/non-separable, differentiable/non-differentiable, and unimodal/multimodal. For each algorithm, we used the built-in code from MATLAB, unedited or revised. For all problems, we varied the number of dimensions, increasing from 2 dimensions to 100 dimensions. We introduce new criteria to compare DFO solver performance using certain generalized characteristics: speed, accuracy and efficiency. Numerical results proposed for most known standard benchmark problems.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.
Recommended Citation
McCart, Jonathan, "New Criteria for Comparing Global Stochastic Derivative—Free Optimization Algorithms" (2019). Papers, Posters, and Recordings. 21.
https://knightscholar.geneseo.edu/great-day-works/21