项目作者: XiangwenWang

项目描述 :
An implementation of the Particle Swarm Optimization algorithm
高级语言: Python
项目地址: git://github.com/XiangwenWang/psoa.git
创建时间: 2020-07-05T10:04:05Z
项目社区:https://github.com/XiangwenWang/psoa

开源协议:BSD 2-Clause "Simplified" License

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psoa

An implementation of the Particle Swarm Optimization algorithm

The algorithm can be summarized as follows:

  • a swarm of moving particles are used to find the extreme value of an objective function
  • each particle has its own position and velocity
    • use the given function to evaluate particles’ positions
    • results can be compared, depending on whether we want to maximize or minimize the objective function
  • In each step, a particle receives three forces, they are used to update the particle’s velocity
    • a friction force that slows down the particle
    • a random attraction force towards the particle’s previous best position
    • a random attraction force towards the swarm’s previous best position
  • the particle’s velocity is then used to update the particle’s position
  • if boundary conditions are included, we consider them to be reflecting boundaries, i.e. if the particle move beyond the boundary in one direction, we would
    • place the particle back onto the boundary
    • and reverse the particle’s velocity in that direction
  • keep updating the particles’ positions and velocities until
    • the swarm’s best position converges
    • or until we reach the preset maximum number of steps

Installation:

  1. pip install psoa

or

  1. conda install -c wangxiangwen psoa

Example Usage:

  1. >>> import psoa
  2. >>> import numpy as np
  3. >>> s = psoa.swarm()
  4. >>> obj = lambda x: -((x[0] - 10) ** 2 + (x[1] - 25) ** 2)
  5. >>> s.maximize(obj, dim=2)
  6. ([10.0, 25.0], -0.0)
  7. >>> obj2 = lambda x: np.sum([xi ** 2 - 10 * np.cos(2 * np.pi * xi)
  8. >>> for xi in x]) + 10 * len(x)
  9. >>> s.minimize(obj2, dim=5, max_iteration=1e5,
  10. >>> boundaries=((-5.12, -5.12, -5.12, -5.12, -5.12),
  11. >>> (5.12, 5.12, 5.12, 5.12, 5.12)))
  12. ([-2.8743161872992346e-10,
  13. 2.579205368330527e-09,
  14. -4.79709591601136e-09,
  15. -1.0974277510973518e-09,
  16. 4.4227337040162274e-10],
  17. 0.0)