mopso

projjal1/MOPSO-WSN

Finding optimal no of clusters in MOPSO implementation of Wireless Sensor Networks.

MohammadRDelavar/RF_MOPSO

These functions are included the "Random Forest" and the hybrid Random Forest and Multi-Objective Particle Swarm Optimization ("RF_MOPSO") to predict the targets as learning approach and find the optimal parameters of a multi-feature process, respectively. The example of this version is a drilling process prediction and optimization. Instruction to use the codes: The Random Forest and hybrid RF_MOPSO algorithms are run via "Random_Forest.m" and "example_final.m" files, respectively. There is a dataset to implement each method, "data1.csv" is for Random Forest and "data.csv" is belonged to RF_MOPSO. These algorithms indicate the results of the prediction and optimization in MATLAB software. The MOPSO algorithm was obtained via: Víctor Martínez-Cagigal (2022). Multi-Objective Particle Swarm Optimization (MOPSO) (https://www.mathworks.com/matlabcentral/fileexchange/62074-multi-objective-particle-swarm-optimization-mopso), MATLAB Central File Exchange. Retrieved July 27, 2022. Besides, the MOPSO implementation is based on the paper of Coello et al. (2004), "Handling multiple objectives with particle swarm optimization". Cite As: Mohammdad Reza Delavar (2022). Optimization of Drilling Parameters Using Combined Multi-Objective Method and Presenting a Practical Factor. Computers & Geosciences.

i2a-k/MOPSO

Multi-Objective PSO Algorithm

Pixelatory/Metaheuristic-Implementations

Korogba/pso

A multi-objective swarm optimizer based on SMPSO that uses CDAS as the primary discriminator instead of Pareto dominance and a secondary selection metric based on shift-based density estimators

abolfazlaghdaee/AI_Project

A repository for Artificiall Intelligence Course Under supervision of Dr. Shamaee