Multi-modal function optimization with the ZEDS (zoomed evolutionary dual strategy) algorithmzoomed evolutionary algorithm

Abstract

This article introduces a new evolutionary algorithm for multi-modal function optimization called ZEDS (zoomed evolutionary dual strategy). ZEDS employs a two-step, zoomed (global to local), evolutionary approach. In the first (global) step, an improved ‘GT algorithm’ is employed to perform a global recombinatory search that divides the search space into niches according to the positions of its approximate solutions. In the second (local) step, a ‘niche evolutionary strategy’ performs a local search in the niches obtained from the first step, which is repeated until acceptable solutions are found. The ZEDS algorithm was applied to some challenging problems with good results, as shown in this article.

Keywords:

• Multi-modal function optimization
• Dual strategy evolutionary algorithm
• Global to local search

Acknowledgement

This work was supported by the foundation of National Science of China (No. 40275034, No. 60133010, No. 60073043, No. 70071042).