A Genetic Algorithm with Zooming for the Determination of the Optimal Open Pit Mines Layout
Identifiers and Pagination:Year: 2016
First Page: 301
Last Page: 322
Publisher ID: TOCIEJ-10-301
Article History:Received Date: 2/12/2015
Revision Received Date: 4/3/2016
Acceptance Date: 30/3/2016
Electronic publication date: 31/05/2016
Collection year: 2016
open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 International Public License (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/legalcode), which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
A Genetic Algorithm (GA) with nested zooming strategy is proposed for the determination of the optimal open pit mine design.
Different genetic procedures are applied to increase robustness, namely two typologies of admissible mutations for the elite sub-population subjected to zooming and mutation and reproduction for the remaining individuals. In order to further improve convergence rate, a user-defined population percentage, depending on individuals fitness, is replaced with new phenotypes, enforcing chromosomic renewal.
Several comparisons with (traditionally used) dynamic programming approaches are provided both for 2D and 3D open pit mines. Both small and large scale mines are analyzed, to benchmark the code in presence of several variables.
Results show that the procedure proposed requires a very limited computational effort, both for challenging problems with several variables and when a micro-GA (populations with few individuals) is adopted for small scale problems.