GitHub

ArCNiX696/Genetic-Algorithms-NSGA-II-SPEA2-and-Simple-GA-for-Multi-Objective-Optimization

📢 Project Introduction This repository features custom implementations of three genetic algorithms for multi-objective optimization: NSGA-II (Non-dominated Sorting Genetic Algorithm II) SPEA2 ...
IEEE

A Hybrid Algorithm Based on NSGA-II and MOPSO for Multi-Objective Designs of Electromagnetic Devices

Abstract: In this article, a hybrid algorithm is proposed by combining the non-dominated sorting genetic algorithm (NSGA-II) with multi-objective particle swarm optimization (MOPSO) algorithm. The ...
GitHub

Implementation of NSGA-II algorithm in form of a python library.

This implementation can be used to solve multivariate (more than one dimensions) multi-objective optimization problem. The number of objectives and dimensions are not limited. Some critical operators ...
Scientific Research Publishing

Modeling and Optimization of Electrical Discharge Machining of SiC Parameters, Using Neural Network and Non-Dominating Sorting Genetic Algorithm (NSGA II) ()

Silicon Carbide (SiC) machining by traditional methods with regards to its high hardness is not possible. Electro Discharge Machining, among non-traditional machining methods, is used for machining of ...
Frontiers

Optimization of water and fertilizer regime for greenhouse radish using BBD response surface method coupled with NSGA-II algorithm

1 Xinjiang Agricultural University, Urumqi, China 2 Engineering Research Centre of Cotton, Ministry of Education / College of Agriculture,, Xinjiang Agricultural University,, Urumqi, China The final, ...
Scientific Research Publishing

K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, “A Fast and Elitist Multiobjective Genetic Algorithm: NSGA II,” IEEE Transactions on Evolutionary Computation, Vol. 6, No. 2 ...

ABSTRACT: Mixed-model U-shaped assembly line balancing problems (MMUALBP) is known to be NP-hard resulting in it being nearly impossible to obtain an optimal solution for practical problems with ...