A HYBRID OPTIMIZATION METHOD BASED ON DIFFERENTIAL EVOLUTION AND HARMONY SEARCH

摘要

The differential evolution (DE) and harmony search (HS) are two well-known nature-inspired computing techniques. Both of them can be applied to effectively cope with nonlinear optimization problems. In this paper, we propose and study a new DE method, DE-HS, by utilizing the fresh individual generation mechanism of the HS. The HS-based approach can enhance the local search capability of the original DE. Optimization of some unconstrained and constrained benchmark problems and a real-world wind generator demonstrate that our DE-HS has an improved convergence property.