optimization-algorithm-library
There are 0 repository under optimization-algorithm-library topic.
- occamypyLanguage:Jupyter Notebook 55
tomitomi3 / LibOptimization
LibOptimization is numerical optimization algorithm library for .NET Framework. / .NET用の数値計算、最適化ライブラリ
Language:Visual Basic .NET 55- Language:C++ 38
- Language:Jupyter Notebook 25
- Duelist-Algorithm-Python
tsyet12 / Duelist-Algorithm-Python
A Python implementation of the paper "Duelist Algorithm: An Algorithm Inspired by How Duelist Improve Their Capabilities in a Duel" https://arxiv.org/abs/1512.00708
genetic-algorithmgenetic-optimization-algorithmmulti-objective-optimizationevolutionary-algorithmsevolutionary-algorithmevolutionary-computationmetaheuristicsmetaheuristics-frameworkmetaheuristic-optimisationduelist-algorithmartificial-intelligence-algorithmsartificial-intelligencepython3pythoncomputational-intelligenceoptimizationoptimization-algorithmsoptimization-toolsoptimization-methodsoptimization-algorithm-libraryLanguage:Python 7 - RUN-Beyond-the-Metaphor-An-Efficient-Optimization-Algorithm-Based-on-Runge-Kutta-Method
aliasgharheidaricom / RUN-Beyond-the-Metaphor-An-Efficient-Optimization-Algorithm-Based-on-Runge-Kutta-Method
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 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://aliasgharheidari.com/RUN.html.
optimizationoptimization-algorithmsswarm-intelligence-algorithmsswarm-intelligencebenchmarkingrunge-kutta-methodsrunge-kuttamodelinggenetic-algorithmparticle-swarm-optimizationevolutionary-algorithmsevolutionary-computationevolutionary-strategydifferential-equationsdifferential-evolutionoptimization-methodsoptimization-toolsoptimization-algorithm-libraryLanguage:MATLAB 5 husk214 / stopt
implementations of optimization algorithms for regularized empirical risk minimization
Language:C++ 3DavisDevelopment / hx-pmdb-querylang
Query Language module for PmDB
Language:Haxe 1mdabrowski1990 / optimization
Python package for executing optimization algorithm.
Language:Python 1Mhmd-Hisham / SmartAntsGA
A genetic algorithm simulation game using OptivolutionPy & Processing3.
Language:Python 1
