Title :
Gradient estimation in global optimization algorithms
Author :
Hazen, Megan ; Gupta, Maya R.
Author_Institution :
Appl. Phys. Lab., Univ. of Washington, Seattle, WA
Abstract :
The role of gradient estimation in global optimization is investigated. The concept of a regional gradient is introduced as a tool for analyzing and comparing different types of gradient estimates. The correlation of different estimated gradients to the direction of the global optima is evaluated for standard test functions. Experiments quantify the impact of different gradient estimation techniques in two population-based global optimization algorithms: fully-informed particle swarm (FIPS) and multiresolutional estimated gradient architecture (MEGA).
Keywords :
correlation methods; gradient methods; particle swarm optimisation; correlation method; fully-informed particle swarm optimization algorithms; global optimization algorithms; gradient estimation; multiresolutional estimated gradient architecture; population-based global optimization algorithms; Convergence; Finite difference methods; Laboratories; Optimization methods; Particle swarm optimization; Physics; Search methods; Stochastic processes; Testing;
Conference_Titel :
Evolutionary Computation, 2009. CEC '09. IEEE Congress on
Conference_Location :
Trondheim
Print_ISBN :
978-1-4244-2958-5
Electronic_ISBN :
978-1-4244-2959-2
DOI :
10.1109/CEC.2009.4983165
