yilmazmehmet's starred repositories
Complete-Python-3-Bootcamp
Course Files for Complete Python 3 Bootcamp Course on Udemy
Project-Euler-solutions
Runnable code for solving Project Euler problems in Java, Python, Mathematica, Haskell.
GA_fuzzyPID
Optimization of fuzzy control rules by genetic algorithm
CSO_Matlab
The Matlab Source code of the Competitive Swarm Optimizer (CSO)
Ensemble-learning-deep-beam
Implementing ensemble learning methods for shear strength prediction of RC deep beams with/without web reinforcements
Neural-Network-weight-improvement-using-optimization-algorithms
Gray Wolf Optimization (GWO), Imperialist Competitive Algorithm (ICA) and Particle Swarm Optimization (PSO) are used to improve the weights achieved by a Neural Network trained with Gradient Descent method
EvolutionaryAlgorithm_Codes
EA codes from CIAM Group at SUSTech, Shenzhen, China
Constrained-MOPSO
Use MOPSO to deal with constrained MOPs
opp-op-pop-init
PyPI package containing opposition learning operators and population initializers for evolutionary algorithms
Runge-Kutta-Optimization-RUN-
The optimization field suffers from the metaphor-based “pseudo-novel” or “fancy” optimizers. Most of these cliché methods mimic animals' searching trends and possess a small contribution to the optimization process itself. Most of these cliché methods suffer from the locally efficient performance, biased verification methods on easy problems, and high similarity between their components' interactions. This study attempts to go beyond the traps of metaphors and introduce a novel metaphor-free population-based optimization method based on the mathematical foundations and ideas of the Runge Kutta (RK) method widely well-known in mathematics. The proposed RUNge Kutta optimizer (RUN) was developed to deal with various types of optimization problems in the future. The RUN utilizes the logic of slope variations computed by the RK method as a promising and logical searching mechanism for global optimization. This search mechanism benefits from two active exploration and exploitation phases for exploring the promising regions in the feature space and constructive movement toward the global best solution. Furthermore, an enhanced solution quality (ESQ) mechanism is employed to avoid the local optimal solutions and increase convergence speed. The RUN algorithm's efficiency was evaluated by comparing with other metaheuristic algorithms in 50 mathematical test functions and four real-world engineering problems. The RUN provided very promising and competitive results, showing superior exploration and exploitation tendencies, fast convergence rate, and local optima avoidance. In optimizing the constrained engineering problems, the metaphor-free RUN demonstrated its suitable performance as well. The authors invite the community for extensive evaluations of this deep-rooted optimizer as a promising tool for real-world optimization. The source codes, supplementary materials, and guidance for the developed method will be publicly available at different hubs at http://imanahmadianfar.com and http://aliasgharheidari.com/RUN.html.
Mating-Selection-based-on-Modified-Strengthened-Dominance-Relation-for-NSGA-III
Matlab file of Mating Selection based on Modified Strengthened Dominance Relation for NSGA-III
RevitLibrary
SimulEICon Revit Add-on
differential_evolution
Library with various differential evolution algorithms and testing functions.
942_Bar_Truss
This contains the objective function file to optimize the weight of 942 bar tower truss also known as 26 storey tower truss.