Gradient estimation in global optimization algorithms

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).