Implemented fully documented Particle Swarm Optimization algorithm (basic model with few advanced features) using Python programming language

Related tags

Deep Learningpythonoptimizationminimizationparticle-swarm-optimizationpsomaximizationpso-algorithm
Overview

Enhanced Particle Swarm Optimization (PSO) with Python

GitHub license GitHub issues

Implemented fully documented Particle Swarm Optimization (PSO) algorithm in Python which includes a basic model along with few advanced features such as updating inertia weight, cognitive, social learning coefficients and maximum velocity of the particle.

Dependencies

  • Numpy
  • matplotlib

Utilities

Once the installation is finished (download or cloning), go the pso folder and follow the below simple guidelines to execute PSO effectively (either write the code in command line or in a python editor).

>>> from pso import PSO

Next, a fitness function (or cost function) is required. I have included four different fitness functions for example purposes namely fitness_1, fitness_2, fitness_3, and fitness_4.

Fitness-1 (Himmelblau's Function)

Minimize: f(x) = (x2 + y - 11)2 + (x + y2 - 7)2

Optimum solution: x = 3 ; y = 2

Fitness-2 (Booth's Function)

Minimize: f(x) = (x + 2y - 7)2 + (2x + y - 5)2

Optimum solution: x = 1 ; y = 3

Fitness-3 (Beale's Function)

Minimize: f(x) = (1.5 - x - xy)2 + (2.25 - x + xy2)2 + (2.625 - x + xy3)2

Optimum solution: x = 3 ; y = 0.5

Fitness-4

Maximize: f(x) = 2xy + 2x - x2 - 2y2

Optimum solution: x = 2 ; y = 1

>>> from fitness import fitness_1, fitness_2, fitness_3, fitness_4

Now, if you want, you can provide an initial position X0 and bound value for all the particles (not mandatory) and optimize (minimize or maximize) the fitness function using PSO:

NOTE: a bool variable min=True (default value) for MINIMIZATION PROBLEM and min=False for MAXIMIZATION PROBLEM

>>> PSO(fitness=fitness_1, X0=[1,1], bound=[(-4,4),(-4,4)]).execute()

You will see the following similar output:

OPTIMUM SOLUTION
  > [3.0000078, 1.9999873]

OPTIMUM FITNESS
  > 0.0

When fitness_4 is used, observe that min=False since it is a Maximization problem.

>>> PSO(fitness=fitness_4, X0=[1,1], bound=[(-4,4),(-4,4)], min=False).execute()

You will see the following similar output:

OPTIMUM SOLUTION
  > [2.0, 1.0]

OPTIMUM FITNESS
  > 2.0

Incase you want to print the fitness value for each iteration, then set verbose=True (here Tmax=50 is the maximum iteration)

>>> PSO(fitness=fitness_2, Tmax=50, verbose=True).execute()

You will see the following similar output:

Iteration:   0  | best global fitness (cost): 18.298822
Iteration:   1  | best global fitness (cost): 1.2203953
Iteration:   2  | best global fitness (cost): 0.8178153
Iteration:   3  | best global fitness (cost): 0.5902262
Iteration:   4  | best global fitness (cost): 0.166928
Iteration:   5  | best global fitness (cost): 0.0926638
Iteration:   6  | best global fitness (cost): 0.0926638
Iteration:   7  | best global fitness (cost): 0.0114517
Iteration:   8  | best global fitness (cost): 0.0114517
Iteration:   9  | best global fitness (cost): 0.0114517
Iteration:   10 | best global fitness (cost): 0.0078867
Iteration:   11 | best global fitness (cost): 0.0078867
Iteration:   12 | best global fitness (cost): 0.0078867
Iteration:   13 | best global fitness (cost): 0.0078867
Iteration:   14 | best global fitness (cost): 0.0069544
Iteration:   15 | best global fitness (cost): 0.0063058
Iteration:   16 | best global fitness (cost): 0.0063058
Iteration:   17 | best global fitness (cost): 0.0011039
Iteration:   18 | best global fitness (cost): 0.0011039
Iteration:   19 | best global fitness (cost): 0.0011039
Iteration:   20 | best global fitness (cost): 0.0011039
Iteration:   21 | best global fitness (cost): 0.0007225
Iteration:   22 | best global fitness (cost): 0.0005875
Iteration:   23 | best global fitness (cost): 0.0001595
Iteration:   24 | best global fitness (cost): 0.0001595
Iteration:   25 | best global fitness (cost): 0.0001595
Iteration:   26 | best global fitness (cost): 0.0001595
Iteration:   27 | best global fitness (cost): 0.0001178
Iteration:   28 | best global fitness (cost): 0.0001178
Iteration:   29 | best global fitness (cost): 0.0001178
Iteration:   30 | best global fitness (cost): 0.0001178
Iteration:   31 | best global fitness (cost): 0.0001178
Iteration:   32 | best global fitness (cost): 0.0001178
Iteration:   33 | best global fitness (cost): 0.0001178
Iteration:   34 | best global fitness (cost): 0.0001178
Iteration:   35 | best global fitness (cost): 0.0001178
Iteration:   36 | best global fitness (cost): 0.0001178
Iteration:   37 | best global fitness (cost): 2.91e-05
Iteration:   38 | best global fitness (cost): 1.12e-05
Iteration:   39 | best global fitness (cost): 1.12e-05
Iteration:   40 | best global fitness (cost): 1.12e-05
Iteration:   41 | best global fitness (cost): 1.12e-05
Iteration:   42 | best global fitness (cost): 1.12e-05
Iteration:   43 | best global fitness (cost): 1.12e-05
Iteration:   44 | best global fitness (cost): 1.12e-05
Iteration:   45 | best global fitness (cost): 1.12e-05
Iteration:   46 | best global fitness (cost): 1.12e-05
Iteration:   47 | best global fitness (cost): 2.4e-06
Iteration:   48 | best global fitness (cost): 2.4e-06
Iteration:   49 | best global fitness (cost): 2.4e-06
Iteration:   50 | best global fitness (cost): 2.4e-06

OPTIMUM SOLUTION
  > [1.0004123, 2.9990281]

OPTIMUM FITNESS
  > 2.4e-06

Now, incase you want to plot the fitness value for each iteration, then set plot=True (here Tmax=50 is the maximum iteration)

>>> PSO(fitness=fitness_2, Tmax=50, plot=True).execute()

You will see the following similar output:

OPTIMUM SOLUTION
  > [1.0028365, 2.9977422]

OPTIMUM FITNESS
  > 1.45e-05

Fitness

Finally, in case you want to use the advanced features as mentioned above (say you want to update the weight inertia parameter w), simply use update_w=True and thats it. Similarly you can use update_c1=True (to update individual cognitive parameter c1), update_c2=True (to update social learning parameter c2), and update_vmax=True (to update maximum limited velocity of the particle vmax)

>>> PSO(fitness=fitness_1, update_w=True, update_c1=True).execute()

References:

[1] Almeida, Bruno & Coppo leite, Victor. (2019). Particle swarm optimization: a powerful technique for solving engineering problems. 10.5772/intechopen.89633.

[2] He, Yan & Ma, Wei & Zhang, Ji. (2016). The parameters selection of pso algorithm influencing on performance of fault diagnosis. matec web of conferences. 63. 02019. 10.1051/matecconf/20166302019.

[3] Clerc, M., and J. Kennedy. The particle swarm — explosion, stability, and convergence in a multidimensional complex space. ieee transactions on evolutionary computation 6, no. 1 (february 2002): 58–73.

[4] Y. H. Shi and R. C. Eberhart, “A modified particle swarm optimizer,” in proceedings of the ieee international conferences on evolutionary computation, pp. 69–73, anchorage, alaska, usa, may 1998.

[5] G. Sermpinis, K. Theofilatos, A. Karathanasopoulos, E. F. Georgopoulos, & C. Dunis, Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and particle swarm optimization, european journal of operational research.

[6] Particle swarm optimization (pso) visually explained (https://towardsdatascience.com/particle-swarm-optimization-visually-explained-46289eeb2e14)

[7] Rajib Kumar Bhattacharjya, Introduction to Particle Swarm Optimization (http://www.iitg.ac.in/rkbc/ce602/ce602/particle%20swarm%20algorithms.pdf)

Owner
GitHub Repository
Object Tracking and Detection Using OpenCV

Object tracking is one such application of computer vision where an object is detected in a video, otherwise interpreted as a set of frames, and the object’s trajectory is estimated. For instance, yo

Happy N. Monday 4 Aug 21, 2022
Convert BART models to ONNX with quantization. 3X reduction in size, and upto 3X boost in inference speed

fast-Bart Reduction of BART model size by 3X, and boost in inference speed up to 3X BART implementation of the fastT5 library (https://github.com/Ki6a

Siddharth Sharma 19 Dec 09, 2022
Tools to create pixel-wise object masks, bounding box labels (2D and 3D) and 3D object model (PLY triangle mesh) for object sequences filmed with an RGB-D camera.

Tools to create pixel-wise object masks, bounding box labels (2D and 3D) and 3D object model (PLY triangle mesh) for object sequences filmed with an RGB-D camera. This project prepares training and t

305 Dec 16, 2022
This repository contains the code for "Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP".

Self-Diagnosis and Self-Debiasing This repository contains the source code for Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based

Timo Schick 62 Dec 12, 2022
Representing Long-Range Context for Graph Neural Networks with Global Attention

Graph Augmentation Graph augmentation/self-supervision/etc. Algorithms gcn gcn+virtual node gin gin+virtual node PNA GraphTrans Augmentation methods N

UC Berkeley RISE 67 Dec 30, 2022
Implementation for our AAAI2021 paper (Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction).

SSAN Introduction This is the pytorch implementation of the SSAN model (see our AAAI2021 paper: Entity Structure Within and Throughout: Modeling Menti

benfeng 69 Nov 15, 2022
A Quick and Dirty Progressive Neural Network written in TensorFlow.

prog_nn .▄▄ · ▄· ▄▌ ▐ ▄ ▄▄▄· ▐ ▄ ▐█ ▀. ▐█▪██▌•█▌▐█▐█ ▄█▪ •█▌▐█ ▄▀▀▀█▄▐█▌▐█▪▐█▐▐▌ ██▀

SynPon 53 Dec 12, 2022
FindFunc is an IDA PRO plugin to find code functions that contain a certain assembly or byte pattern, reference a certain name or string, or conform to various other constraints.

FindFunc: Advanced Filtering/Finding of Functions in IDA Pro FindFunc is an IDA Pro plugin to find code functions that contain a certain assembly or b

213 Dec 17, 2022
Linescanning - Package for (pre)processing of anatomical and (linescanning) fMRI data

line scanning repository This repository contains all of the tools used during the acquisition and postprocessing of line scanning data at the Spinoza

Jurjen Heij 4 Sep 14, 2022
Code for the paper "Attention Approximates Sparse Distributed Memory"

Attention Approximates Sparse Distributed Memory - Codebase This is all of the code used to run analyses in the paper "Attention Approximates Sparse D

Trenton Bricken 14 Dec 05, 2022
Code & Data for Enhancing Photorealism Enhancement

Code & Data for Enhancing Photorealism Enhancement

Intel ISL (Intel Intelligent Systems Lab) 1.1k Jan 08, 2023
TensorFlow Implementation of Unsupervised Cross-Domain Image Generation

Domain Transfer Network (DTN) TensorFlow implementation of Unsupervised Cross-Domain Image Generation. Requirements Python 2.7 TensorFlow 0.12 Pickle

Yunjey Choi 864 Dec 30, 2022
Pytorch implementation of SimSiam Architecture

SimSiam-pytorch A simple pytorch implementation of Exploring Simple Siamese Representation Learning which is developed by Facebook AI Research (FAIR)

Saeed Shurrab 1 Oct 20, 2021
Measuring Coding Challenge Competence With APPS

Measuring Coding Challenge Competence With APPS This is the repository for Measuring Coding Challenge Competence With APPS by Dan Hendrycks*, Steven B

Dan Hendrycks 218 Dec 27, 2022
Adaptation through prediction: multisensory active inference torque control

Adaptation through prediction: multisensory active inference torque control Submitted to IEEE Transactions on Cognitive and Developmental Systems Abst

Cristian Meo 1 Nov 07, 2022
Binary Passage Retriever (BPR) - an efficient passage retriever for open-domain question answering

BPR Binary Passage Retriever (BPR) is an efficient neural retrieval model for open-domain question answering. BPR integrates a learning-to-hash techni

Studio Ousia 147 Dec 07, 2022
A minimalist environment for decision-making in autonomous driving

highway-env A collection of environments for autonomous driving and tactical decision-making tasks An episode of one of the environments available in

Edouard Leurent 1.6k Jan 07, 2023
Official implementation of "Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection" (ICCV Workshops 2021: RSL-CV).

Official PyTorch implementation of "Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection" This is the implementation of the paper "Syn

Marcella Astrid 11 Oct 07, 2022
N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting

N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting Recent progress in neural forecasting instigated significant improvements in the

Cristian Challu 82 Jan 04, 2023
TensorFlow implementation of "Attention is all you need (Transformer)"

[TensorFlow 2] Attention is all you need (Transformer) TensorFlow implementation of "Attention is all you need (Transformer)" Dataset The MNIST datase

YeongHyeon Park 4 Jan 05, 2022