metaheuristic-algorithms

There are 101 repositories under metaheuristic-algorithms topic.

  • Emad-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:MATLAB721312
  • 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:MATLAB624011

  • Computational-Intelligence-Fall18/Computational-Intelligence-Tutorials

    This is the repository of codes written in class.

    Language:Jupyter Notebook433416

  • kkg1999/MetaheuristicOptimization

    Different meta-heuristic optimization techniques for feature selection

    Language:Python420124

  • jcrvz/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:Python225312

  • MustafaLotfi/Optimized-Inverse-Dynamics-Controller

    Obtaining the best coefficients of Inverse Dynamics Controller, for a dynamical system, with Optimization Algorithms.

    Language:MATLAB18103

  • thieu1995/IntelELM

    IntelELM: A Python Framework for Intelligent Metaheuristic-based Extreme Learning Machine

    Language:Jupyter Notebook16234

  • gabrielegilardi/PSO

    Metaheuristic Minimization Using Particle Swarm Optimization.

    Language:Python14103

  • modeling-inversion-lab/awesome-metaheuristic-algorithms

    :chart_with_upwards_trend: Collect and archive the metaheuristic algorithms and their applications in geophysics.

    12201

  • thieu1995/MetaPerceptron

    MetaPerceptron: A Standardized Framework For Metaheuristic-Driven Multi-layer Perceptron Optimization

    Language:Python12101

  • ali-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.

    10103

  • thieu1995/reflame

    reflame: Revolutionizing Functional Link Neural Network by Metaheuristic Optimization

    Language:Python9300

  • thieu1995/EvoRBF

    EvoRBF: A Nature-inspired Algorithmic Framework for Evolving Radial Basis Function Networks

    Language:Python8101

  • amirabbasasadi/Babai

    C++ Optimization Library

    Language:C++7110

  • FTVarna/Adaptive-HIDMS-PSO

    Adaptive Heterogeneous Improved Dynamic Multi-Swarm PSO (A-HIDMS-PSO) Algorithm. Source code for the paper: IEEE SSCI https://ieeexplore.ieee.org/document/9660115

    Language:MATLAB7101

  • FTVarna/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:MATLAB7100

  • rayzxli/AMPO

    Adaptive Multi-Population Optimization Algorithm

    Language:Python7100

  • ali-ece/A-Modified-Inclined-Planes-system-Optimization-MIPO-Algorithm

    With 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:MATLAB6101

  • ali-ece/IPO-Inclined-Planes-system-Optimization-Algorithm

    A 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:MATLAB6111

  • jonasgrebe/bees-bats-fireflies

    Implementation of three nature-inspired search algorithms: Bees Algorithm, Bat Algorithm, and Firefly Algorithm

    Language:HTML6113

  • minailkhani/Some-Bio-Inspired-Algorithms-Projects

    Language:Jupyter Notebook6100

  • vkotiyal/thesis-ecs-codes

    Python code for ECS-NL.

    Language:Jupyter Notebook6201

  • bghojogh/Metaheuristic-Optimization

    The codes for metaheuristic optimization algorithms

    Language:Python5107

  • BoutainaELYAZIJI/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:Python5100

  • thieu1995/BIO-ANFIS

    BIO-ANFIS: A Bio-inspired Optimization Framework for Improving ANFIS Networks

    Language:Python5101

  • toohidsharifi/Optimal-design-of-Permanent-Magnet-Synchronous-Motor

    Optimal Design of a Permanent Magnet Synchronous Motor Using the Cultural Algorithm

    5100

  • K-BiabaniHamedani/Improved-Hybrid-Growth-Optimizer

    This repository contains the Matlab source codes of the Improved Hybrid Growth Optimizer (IHGO) algorithm, an improved variant of the recently-developed Growth Optimizer (GO) algorithm.

    Language:MATLAB4101

  • prateekshyap/CPP-in-SDN

    MATLAB simulations for Controller Placement Problem in Software Defined Networks

    Language:MATLAB4112

  • Shrinidhi1/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:Python4100

  • boatzaa1456/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:Python3100

  • chasebk/code_ISLO_ELM

    A study on swarm intelligence optimizing neural networks for workload elasticity prediction

    Language:Jupyter Notebook3112

  • ches-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++310

  • FarshidKeivanian/A-novel-hybrid-fuzzy-metaheuristic-approach-for-multimodal-single-objective-optimization-problems

    FAEICA-Algorithm (Fuzzy adaptive optimisation method)

    Language:MATLAB3101

  • xjcheam/portfolio-optimization-qts-trend-ratio

    This is a project of portfolio optimization using Quantum-inspired Tabu Search and Trend Ratio

    Language:Python3102

  • xtenzQ/Uni-AI

    🧠 Homework repository for AI course at Inha University

    Language:Java300

  • ADNANE-RBT/metaheuristics

    This project implements two nature-inspired optimization algorithms: Moth Flame Optimization (MFO) and Honey Badger Optimization (HBO). Both algorithms are designed to solve complex optimization problems by mimicking behaviors observed in nature. also it includes a path finding algorithm, A-star

    Language:Python2100