HPSOGA-O2_ClustersOpt

Hybrid Particle Swarm Optimization and Genetic Algorith (HPSOGA) implementation for global minimum search in molecular oxygen clusters

Description

This repository contains the implementation of a HPSOGA used to explore the potential energy surface of molecular oxygen clusters and find their minimum-energy configurations.

The code was developed as part of the study:

"Molecular oxygen tetramer: multiplet structure and global minima"

The implementation provided in this repository is based on the Hybrid Particle Swarm Optimization and Genetic Algorithm with population partitioning (HPSOGA) originally proposed by:

Ali, A. F., & Tawhid, M. A. (2017).
A hybrid particle swarm optimization and genetic algorithm with population partitioning for large scale optimization problems.
Ain Shams Engineering Journal, 8(2), 191–206.
https://doi.org/10.1016/j.asej.2016.07.008

Potential Energy Surface (PES)

This code implements only the HPSOGA optimization algorithm. It requires a function to evaluate the Potential Energy Surface (PES).

In this repository, the PES routine used is implemented in:

src/BaseDesacoplada.f95

This file is called by the optimizer during the energy evaluation step and is required for the program to run.

Repository structure

bin/            # Executable 
inout/          # Input and output files
make/           # Makefile
src/            # Source code
README.md   

How to use

General workflow:

1. Define the input variables in inout/data.inp
2. Adjust the search space in inout/range.inp by setting hte range and presicion of each dimension.
3. If the executable is not available, run make in hte make/ directory to compile the program. The executable will be created in bin/
4. Run the executable.
5. Analyze the results in inout/results/