metaheuristic-algorithms
There are 7 repositories under metaheuristic-algorithms topic.
emad-deilam-salehi / Path-Planning-using-Gray-Wolf-Optimization
Regenerated a state-of-the-art meta-heuristic algorithm for the UAV path planning problem, proposed by Qu, Gai, and Zhong.
Language:MATLAB 85- Harris-Hawks-Optimization-Algorithm-and-Applications
aliasgharheidaricom / Harris-Hawks-Optimization-Algorithm-and-Applications
Harris Hawks Optimization (HHO) is a nature-inspired metaheuristic algorithm that simulates the cooperative hunting behavior of Harris' hawks. Widely used in engineering, machine learning, and resource allocation, HHO is renowned for its simplicity, versatility, and effectiveness in finding global optima.
Language:MATLAB 63 Computational-Intelligence-Fall18 / Computational-Intelligence-Tutorials
This is the repository of codes written in class.
Language:Jupyter Notebook 43kkg1999 / MetaheuristicOptimization
Different meta-heuristic optimization techniques for feature selection
optimizationoptimization-algorithmsparticle-swarm-optimizationmetaheuristic-optimisationmetaheuristicsmetaheuristic-algorithmsmetaheuristic-frameworkmetaheuristicgenetic-algorithmatom-search-optimizationsailfish-optimizergolden-ratio-optimizationequilibrium-optimizersocial-mimic-optimizationmanta-ray-foraging-optimizationbarnacles-mating-optimizerLanguage:Python 42jcrvz / customhys
Customising optimisation metaheuristics via hyper-heuristic search (CUSTOMHyS). This framework provides tools for solving, but not limited to, continuous optimisation problems using a hyper-heuristic approach for customising metaheuristics. Such an approach is powered by a strategy based on Simulated Annealing. Also, several search operators serve as building blocks for tailoring metaheuristics. They were extracted from ten well-known metaheuristics in the literature.
Language:Python 25MustafaLotfi / Optimized-Inverse-Dynamics-Controller
Obtaining the best coefficients of Inverse Dynamics Controller, for a dynamical system, with Optimization Algorithms.
Language:MATLAB 18thieu1995 / IntelELM
IntelELM: A Python Framework for Intelligent Metaheuristic-based Extreme Learning Machine
classification-modelselmevolutionary-computationextreme-learning-machinegenetic-algorithmmachine-learningmetaheuristic-algorithmsnature-inspired-algorithmsneural-networksparticle-swarm-optimizationregression-modelsscikit-learnswarm-intelligence-algorithmswhale-optimization-algorithmmetaheuristic-based-extreme-learning-machinemha-elmLanguage:Jupyter Notebook 18gabrielegilardi / PSO
Metaheuristic Minimization Using Particle Swarm Optimization.
Language:Python 14X-ANFIS: An Extensible and Cross-Learning ANFIS Framework for Machine Learning Tasks
Language:Python 14modeling-inversion-lab / awesome-metaheuristic-algorithms
:chart_with_upwards_trend: Collect and archive the metaheuristic algorithms and their applications in geophysics.
MetaPerceptron: A Standardized Framework For Metaheuristic-Driven Multi-layer Perceptron Optimization
classification-modelsgenetic-algorithmglobal-searchmetaheuristic-algorithmsmetaheuristicsmlpmulti-layer-perceptronnature-inspired-optimizationneural-networkparticle-swarm-optimizationregression-modelswhale-optimization-algorithmgradient-free-based-multi-layer-perceptronmetaheuristic-based-multi-layer-perceptronadagrad-approachadam-optimizersgd-optimizeradelta-optimizerLanguage:Python 12ali-ece / Design-of-optimal-CMOS-ring-oscillator-using-an-intelligent-optimization-tool
This paper presents an intelligent sizing method to improve the performance and efficiency of a CMOS Ring Oscillator (RO). The proposed approach is based on the simultaneous utilization of powerful and new multi-objective optimization techniques along with a circuit simulator under a data link. The proposed optimizing tool creates a perfect tradeoff between the contradictory objective functions in CMOS RO optimal design. This tool is applied for intelligent estimation of the circuit parameters (channel width of transistors), which have a decisive influence on RO specifications. Along the optimal RO design in an specified range of oscillaton frequency, the Power Consumption, Phase Noise, Figure of Merit (FoM), Integration Index, Design Cycle Time are considered as objective functions. Also, in generation of Pareto front some important issues, i.e. Overall Nondominated Vector Generation (ONVG), and Spacing (S) are considered for more effectiveness of the obtained feasible solutions in application. Four optimization algorithms called Multi-Objective Genetic Algorithm (MOGA), Multi-Objective Inclined Planes system Optimization (MOIPO), Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Modified Inclined Planes System Optimization (MOMIPO) are utilized for 0.18-mm CMOS technology with supply voltage of 1-V. Baesd on our extensive simulations and experimental results MOMIPO outperforms the best performance among other multi-objective algorithms in presented RO designing tool.
multi-objective-optimizationpareto-optimalitypareto-frontoptimizationoptimization-algorithmsswarm-intelligenceevolutionary-algorithmsevolutionary-computationengineering-optimisationbio-inspired-optimizationheuristic-search-algorithmsmetaheuristic-optimisationmetaheuristic-algorithmsvlsi-circuitshspicematlabAssetMatrix500 / Portfolio-Optimization_Enhanced
Stock Portfolio Optimization with Bayesian shrinkage, DRIP, Projected vs Actual perfomenace and Blume-Adjusted Betas
Language:Jupyter Notebook 9reflame: Revolutionizing Functional Link Neural Network by Metaheuristic Optimization
flnnfunctional-link-neural-networkclassificationevolutionary-algorithmsgenetic-algorithmhigher-order-functionsmachine-learningmetaheuristic-algorithmsnature-inspired-algorithmsneural-networkparticle-swarm-optimizationpytorch-modelregressionscikit-learnfunctional-link-artificial-neural-networkhigher-order-neural-networkfeed-forward-neural-networksoptimization-algorithmsshadewhale-optimization-algorithmLanguage:Python 9FTVarna / HIDMS-PSO
Heterogeneous Improved Dynamic Multi-Swarm PSO (HIDMS-PSO) algorithm. Source code for the paper: IEEE SSCI https://ieeexplore.ieee.org/document/9308313
Language:MATLAB 8- Language:Jupyter Notebook 8
EvoRBF: A Nature-inspired Algorithmic Framework for Evolving Radial Basis Function Networks
classificationevolutionary-algorithmsgenetic-algorithmglobal-searchgradient-free-optimizationlocal-searchmachine-learningmealpymetaheuristic-algorithmsnature-inspired-algorithmsneural-networkparticle-swarm-optimizationradial-basis-functionradial-basis-function-networkregressionswarm-intelligencewhale-optimization-algorithmgradient-free-rbfLanguage:Python 8amirabbasasadi / Babai
C++ Optimization Library
Language:C++ 7Adaptive Heterogeneous Improved Dynamic Multi-Swarm PSO (A-HIDMS-PSO) Algorithm. Source code for the paper: IEEE SSCI https://ieeexplore.ieee.org/document/9660115
Language:MATLAB 7- Language:Python 7
toohidsharifi / Optimal-design-of-Permanent-Magnet-Synchronous-Motor
Optimal Design of a Permanent Magnet Synchronous Motor Using the Cultural Algorithm
ansys-electronics-desktopcultural-algorithmdesign-optimizationmatlabmetaheuristic-algorithmsmetaheuristicsoptimizationoptimization-algorithmspermanent-magnet-synchronousansys-maxwellcoupled-simulationelectric-motor-optimizationinterface-ansys-matlabsynchronous-motortorque-densitytorque-improvementtorque-performanceconstrained-optimizationelectric-vehiclestransportation-electrificationWith the aim of create a powerful trade-off between the concepts of exploitation and exploration, and rectify the complexity of their structural parameters in the standard IPO, a modified version of IPO (called MIPO) is introduced as an efficient optimization algorithm for digital infinite-impulse-response (IIR) filters model identification. The MIPO utilizes an appropriate mechanism based on the executive steps of algorithm with the constant damp factors.
Language:MATLAB 6A new optimization method based on the dynamic of sliding motion along a frictionless inclined plane. In IPO, a collection of agents cooperate with each other and move toward better positions in the search space by employing Newton’s second law and equations of motion. The standard version of the IPO is presented by Mozafari et al. in 2016. Powerful improved versions of it called MIPO and SIPO along with its multi-objective version of MOIPO were presented in 2016, 2017 and 2019 by Dr. Ali Mohammadi (myself) and colleagues at the University of Birjand, respectively. This powerful algorithm has also been used in many applications, which has provided very good outputs. In the following, the standard version of the IPO algorithm along with the benchmark functions reviewed in its reference article, and its improved versions are attached.
Language:MATLAB 6jonasgrebe / bees-bats-fireflies
Implementation of three nature-inspired search algorithms: Bees Algorithm, Bat Algorithm, and Firefly Algorithm
Language:HTML 6SafwanAlselwi / RNN-LSTM-SLR
Excel file and Python code used in the published SLR paper: RNN-LSTM: From Applications to Modeling Techniques and Beyond - Systematic Review
Language:Jupyter Notebook 6vkotiyal / thesis-ecs-codes
Python code for ECS-NL.
Language:Jupyter Notebook 6bghojogh / Metaheuristic-Optimization
The codes for metaheuristic optimization algorithms
Language:Python 5BoutainaELYAZIJI / TSP_project
The traveling salesman problem (TSP) is a well-known problem in theoretical computer science and operations research. The standard version of the TSP is a hard problem and belongs to the NP-Hard class. In this project, I build an application to implement the TSP by the dynamic approach and the GVNS approach .
Language:Python 5K-BiabaniHamedani / Improved-Hybrid-Growth-Optimizer
This repository contains the source codes for our paper published in Computers & Structures: A hybridization of growth optimizer and improved arithmetic optimization algorithm and its application to discrete structural optimization.
Language:MATLAB 4prateekshyap / CPP-in-SDN
MATLAB simulations for Controller Placement Problem in Software Defined Networks
Language:MATLAB 4Shrinidhi1 / PSO-Kmeans-Hybrid-for-High-Dimensional-Data-Clustering-with-Autoencoder
Enhancing the performance of high dimensional automatic data clustering using Particle Swarm Optimization (PSO) algorithm employing Autoencoder in Stock Market data.
Language:Python 4boatzaa1456 / Project-Set-Based-Mayfly-Algorithm-for-Solving-Order-Batching-Problems
This research proposes a novel order batching approach for warehouses to minimize total tardiness, considering category, weight, and fragility constraints. A Set-based Mayfly Algorithm (SBMA) is developed, adapting the Mayfly Algorithm to the discrete problem and leveraging swarming/mating behaviors to avoid local optima.
Language:Python 3chasebk / code_ISLO_ELM
A study on swarm intelligence optimizing neural networks for workload elasticity prediction
Language:Jupyter Notebook 3ches-001 / random-algorithms-go
A repository for random algorithms. I append to this repository when I have free time (I have very little free time)
Language:C++ 3FarshidKeivanian / A-novel-hybrid-fuzzy-metaheuristic-approach-for-multimodal-single-objective-optimization-problems
FAEICA-Algorithm (Fuzzy adaptive optimisation method)
evolutionary-algorithmevolutionary-algorithmsevolutionary-computationfuzzy-inference-systemfuzzy-logicfuzzy-theoryimperialist-competitive-algorithmimperialistic-competitive-algorithmmetaheuristicmetaheuristic-algorithmsmetaheuristic-optimisationmetaheuristic-optimizationmetaheuristicsoptimisationoptimisation-algorithmsoptimizationoptimization-algorithmssingle-objectivesingle-objective-optimizationfuzzy-adaptive-enhanced-icaLanguage:MATLAB 3xjcheam / portfolio-optimization-qts-trend-ratio
This is a project of portfolio optimization using Quantum-inspired Tabu Search and Trend Ratio
Language:Python 3
