https://github.com/konrad1991/opti
Numerical optimization using particle swarm algorithm
https://github.com/konrad1991/opti
numerical-optimization particle-swarm-optimization pso pso-algorithm swarm-intelligence topology
Last synced: about 1 month ago
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Numerical optimization using particle swarm algorithm
- Host: GitHub
- URL: https://github.com/konrad1991/opti
- Owner: Konrad1991
- License: gpl-2.0
- Created: 2021-02-02T17:14:02.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2022-08-12T11:40:03.000Z (over 3 years ago)
- Last Synced: 2025-08-02T06:36:40.015Z (8 months ago)
- Topics: numerical-optimization, particle-swarm-optimization, pso, pso-algorithm, swarm-intelligence, topology
- Language: Fortran
- Homepage:
- Size: 1.89 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Overview
The repository opti contains a particle swarm optimizer (PSO) written in fortran. In the future more optimizer are planed.
For the PSO the user can chose between two topologies. Either the traditional star topology or an random adaptive neighberhood which is better for exploration of parameter spaces. This is useful when many parameters have to be optimized.
# Installation
The project structured as a fpm (fortran package manager) project. Thus, the project can be used by adding the following two lines to the toml file:
[dependencies] \
opti = { git="https://github.com/Konrad1991/opti"}
# Documentation
The project contains only one subroutine called optimizer. It accepts the following parameter:
1. **integer ::** number of particles (particle)
2. **integer ::** number of generations (n_gen)
3. **integer ::** how many parameters have to be optimized (n_params)
4. **real(8), dimension(n_params) ::** An array containing the lower boundaries for the optimization (dimension(n_params))
5. **real(8), dimension(n_params) ::** An array containing the upper boundaries for the optimization (dimension(n_params))
6. **real(8) ::** The desired minimal error
7. The loss function is of type:
```fortran
interface
function fct (inp, problem_size) result(out)
implicit none
integer, intent(in) :: problem_size
real(8), intent(in), dimension(problem_size) :: inp
real(8) :: out
end function fct
end interface
```
8. **real(8), dimension(n_params) ::** An array where the optimized parameter are stored (dimension(n_params))
9. **integer ::** An optional parameter defining the topology. If chosen 1 the star topology is used. If chosen 2 the random adaptive topology is used. The default value is 1 (topo)
10. **integer ::** An optional parameter defining the number of neighbours if the topology is the random adaptive topology. Otherwise the parameter is ignored. The default value is set to 4.
## An easy example:
```fortran
module objectivefct
contains
function f(inp, problem_size) result(out)
!! This function defines a multidimensional rosenbrock function
!! For problem_size = 3 Minimum at (1,1,1)
!! For 4 <= problem_size <= 7 local minima near (-1, 1, ...,1)
implicit none
integer, intent(in) :: problem_size
real(8), intent(in), dimension(problem_size) :: inp
real(8) :: out
integer :: i
out = 0.0
do i = 1, (problem_size -1)
out = out + 100.0*(inp(i+1) - inp(i)**2)**2 + (1 - inp(i) )**2
end do
end function
end module
program example
use objectivefct
use psomod
implicit none
integer:: n_swarm, n_generations, n_params
real(8) :: lb(3), ub(3)
real(8) :: desired_error
real(8) :: result(3)
n_swarm = 40
n_generations = 10000
n_params = 3
lb = -10
ub = 10
desired_error = 0.00001
call optimizer(n_swarm, n_generations, n_params, lb, ub, desired_error, f, result)
print*, result
end program example
```