https://github.com/eigenzy/cmpso
Coevolutionary Multi-Objective Particle Swarm Optimization [MATLAB]
https://github.com/eigenzy/cmpso
matlab multiobjective-optimization optimization particle-swarm-optimization
Last synced: 11 months ago
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Coevolutionary Multi-Objective Particle Swarm Optimization [MATLAB]
- Host: GitHub
- URL: https://github.com/eigenzy/cmpso
- Owner: eigenzy
- License: mit
- Created: 2024-12-16T03:42:14.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-04T21:47:09.000Z (about 1 year ago)
- Last Synced: 2025-01-04T22:25:28.412Z (about 1 year ago)
- Topics: matlab, multiobjective-optimization, optimization, particle-swarm-optimization
- Language: MATLAB
- Homepage:
- Size: 17.6 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Coevolutionary Multiobjective Particle Swarm Optimization (CMPSO) [1]
## Disclaimer:
- This repository acts as an implementation of the authors' [1] work in MATLAB.
- Ensure to download all MATLAB files as they are essential *functions* for the execution of *CMPSO.m*.
## Introduction:
Inspired by Zhan et al., the CMPSO algorithm runs a regular particle swarm optimization scheme on multiple swarms (swarm size = number of objectives) and introduces an information sharing algorithm which outputs a set of non-dominated solutions in the ***Archive*** matrix in the code.
Further information about the methodology of the algorithm can be found in the reference paper.
## Table of Variables
| **Variable** | **Definition** |
|----------|----------|
| var | Number of optimization variables |
| varsize | Row vector form of solution |
| swarms | Number of objectives |
| NA | Max Archive size |
| xmax | Upper Limit of Search Space |
| xmin | Lower Limit of Search Space |
| vlim | Velocity Limit |
| iterations | Number of Iterations |
| particles | Number of Particles |
| wmin | Minimum Inertia |
| wmax | Maximum Inertia |
| co1 | Personal Acceleration Coeff |
| co2 | Social Acceleration Coeff |
| co3 | Archive Acceleration Coeff |
| particle | Cell template of swarms |
| GlobalBestEF | Global Best Evaluation for Isolated Objectives |
| GlobalBestPos | Global Best Position for Isolated Objectives |
| BestEF | Best Evaluation at Each Iteration for Isolated Objectives |
| Archive | Pareto Set |
| ArchEF | Pareto Front |
| BP | Stores Best Positions across all swarms for the algorithm |
| BP_EF | Stores Best Positions' Evaluation across all swarms for the algorithm |
| wasemptyArch | Checks if the Archive is empty |
## Table of Functions
| **Function** | **Definition** |
|----------|----------|
| sat | Limit a matrix of solutions to **upperlimit** and **lowerlimit** which must be row vectors |
| els | Elitist Learning Strategy [1] |
| nondom_sol | Extracts Non-dominated Solutions and their Positions |
| dbs | Density-Based Selection [1] |
## ***Evaluation Function***:
Line 32 of CMPSO.m must be set to the evaluation function, create your MATLAB function that takes in **positions** and **swarms** and returns an evaluation of said **positions**.
- **positions**: matrix of input solutions (each row is a solution)
A *for* loop should be used to analyse the evaluation for each row of the **positions** input.
## Example:
***cone_ef.m*** showcases the evaluation function for this example.
*Reference*: https://www.math.unipd.it/~marcuzzi/DIDATTICA/LEZ&ESE_PIAI_Matematica/3_cones.pdf
*Problem Statement*:
- Input variables: radius and height of cone [r,h]
- Objectives: minimize Lateral Surface Area (S) and Total Area (T) [S,T]
- Constraint: Volume>200 cm^3
*Results*:
- Using 10 particles and 10 iterations:
## Reference:
[1] Multiple Populations for Multiple Objectives: A Coevolutionary Technique for Solving Multiobjective Optimization Problems (Zhi-Hui Zhan, Jingjing Li, Jiannong Cao, Jun Zhang, Henry Shu-Hung Chung and Yu-Hui Shi) 2013