Abstract
This is a parallel variable population multi-objective optimizer with an adaptive unified differential evolution algorithm or a genetic algorithm.
It can also be used for single objective optimization.
Some features of this code include:
1) The population size varies from generation to generation to save the total # of objective function evaluations.
2) The population is uniformly distributed to a number of parallel processors for simultaneous objective function evaluation.
3) The objective function evaluation can be attained from an external simulation program with control variables in its input file and objectives calculated from its output files.
4) The optimizer includes an adaptive unified differential evolution algorithm and a real value genetic algorithm. The parameters in the unified differential evolution algorithm can be chosen to attain any mutation schemes in the published literature.
- Developers:
-
Qiang, Ji [1]
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Release Date:
- 2024-06-03
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Licenses:
-
BSD 3-clause "New" or "Revised" License
- Sponsoring Org.:
-
USDOEPrimary Award/Contract Number:AC02-05CH11231
- Code ID:
- 132940
- Site Accession Number:
- 2024-040
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Country of Origin:
- United States
Citation Formats
Qiang, Ji.
Parallel Variable Population Multi-Objective Optimizer (pvpmoo) v1.0.
Computer Software.
https://github.com/qianglbl/PVPmoo.
USDOE.
03 Jun. 2024.
Web.
doi:10.11578/dc.20240624.2.
Qiang, Ji.
(2024, June 03).
Parallel Variable Population Multi-Objective Optimizer (pvpmoo) v1.0.
[Computer software].
https://github.com/qianglbl/PVPmoo.
https://doi.org/10.11578/dc.20240624.2.
Qiang, Ji.
"Parallel Variable Population Multi-Objective Optimizer (pvpmoo) v1.0." Computer software.
June 03, 2024.
https://github.com/qianglbl/PVPmoo.
https://doi.org/10.11578/dc.20240624.2.
@misc{
doecode_132940,
title = {Parallel Variable Population Multi-Objective Optimizer (pvpmoo) v1.0},
author = {Qiang, Ji},
abstractNote = {This is a parallel variable population multi-objective optimizer with an adaptive unified differential evolution algorithm or a genetic algorithm.
It can also be used for single objective optimization.
Some features of this code include:
1) The population size varies from generation to generation to save the total # of objective function evaluations.
2) The population is uniformly distributed to a number of parallel processors for simultaneous objective function evaluation.
3) The objective function evaluation can be attained from an external simulation program with control variables in its input file and objectives calculated from its output files.
4) The optimizer includes an adaptive unified differential evolution algorithm and a real value genetic algorithm. The parameters in the unified differential evolution algorithm can be chosen to attain any mutation schemes in the published literature.},
doi = {10.11578/dc.20240624.2},
url = {https://doi.org/10.11578/dc.20240624.2},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20240624.2}},
year = {2024},
month = {jun}
}