bio-inspired-optimization
There are 1 repository under bio-inspired-optimization topic.
thieu1995 / mealpy
A Collection Of The State-of-the-art Metaheuristic Algorithms In Python (Metaheuristic/Optimizer/Nature-inspired/Biology)
genetic-algorithmparticle-swarm-optimizationwhale-optimizationgrey-wolf-optimizerharris-hawks-optimizationdifferential-evolutionharris-hawks-optimizationghenry-gas-solubility-optimizationarithmetic-optimization-algorithmequilibrium-optimizerslime-mould-algorithmhunger-games-searchbio-inspired-optimizationnature-inspired-algorithmsswarm-intelligencelocation-optimizationproduction-planningschedule-optimizationsupply-chain-optimizationmetaheuristicsLanguage:Python 783Evolutionary-Intelligence / pypop
PyPop7: A Pure-Python Library for POPulation-based Black-Box Optimization (BBO), especially their *Large-Scale* versions/variants. https://pypop.rtfd.io/
global-optimizationblack-box-optimizationlarge-scale-optimizationpopulation-based-optimizationevolutionary-algorithmsrandom-searchzeroth-order-optimizationgradient-free-optimizationderivative-free-optimizationcontinuous-optimizationdirect-searchoptimization-algorithmsnumerical-optimizationstochastic-searchevolution-strategiesmetaheuristicsnonlinear-optimizationbio-inspired-optimizationoptimizationparticle-swarm-optimizationLanguage:Python 171shahind / Nature-Inspired-Algorithms
Sample Code Collection of Nature-Inspired Computational Methods
Language:MATLAB 43Evolutionary Computation: A Modern Perspective ---> This is a free online book, which is actively updated now (from 2023 to 2027).
artificial-intelligenceevolutionary-algorithmsevolutionary-computationmachine-learningswarm-intelligencebio-inspired-computingbio-inspired-optimizationevolution-strategiesevolutionary-learningevolutionary-optimizationevolutionary-roboticsgenetic-algorithmsgenetic-programmingneuroevolutionquality-diversity- Language:Python 11
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.
multi-objective-optimizationpareto-optimalitypareto-frontoptimizationoptimization-algorithmsswarm-intelligenceevolutionary-algorithmsevolutionary-computationengineering-optimisationbio-inspired-optimizationheuristic-search-algorithmsmetaheuristic-optimisationmetaheuristic-algorithmsvlsi-circuitshspicematlabShahul-Rahman / SPGD-Search-Party-Gradient-Descent-algorithm
SPGD: Search Party Gradient Descent algorithm, a Simple Gradient-Based Parallel Algorithm for Bound-Constrained Optimization. Link: https://www.mdpi.com/2227-7390/10/5/800
Language:Jupyter Notebook 7A 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 6vkotiyal / thesis-ecs-codes
Python code for ECS-NL.
Language:Jupyter Notebook 6With 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 4mbalchanowski / Krill-Herd
Simple console implementation of the Krill Herd (KH) algorithm
Language:C# 2adlyZaroui / metaheuristics-benchmark
Python3 framework for comparative study of recent metaheuristics
Language:Jupyter Notebook 1A simplified and effective version of IPO (called SIPO) with the aim of simplifying the main IPO equations, creating a powerful trade-off between the concepts of exploitation and exploration, and modifying the complexity of their structural parameters.
bio-inspired-optimizationoptimization-algorithmssoft-computingsoft-computing-techniquesoptimizationengineering-optimisationintelligent-systemsartificial-intelligenceevolutionary-algorithmsevolutionary-computationswarm-intelligencematlabbenchmarksheuristic-search-algorithmsmetaheuristic-optimisationmetaheuristic-algorithmscomputer-aided-designLanguage:MATLAB 1- Language:MATLAB 1
George614 / BioAlgorithms
Coursework for CSCI-633 Bio-Inspired Intelligent Systems
Language:Python 1Impelon / ant-image-seg
An ant colony optimization program for solving image segmentation created in the context of IT3708 at NTNU.
Language:Rust 1srlucasromulo / Bioinspired-Algorithms
Study and implementation of bioinspired metaheuristics (ACO, CLONALG, GA, PSO)
Language:Python 1zaha2020 / Bio_Inspired_Computing
This repository contains my Bio-Inspired Computing Projects during University and the projects that I've implemented due to my interest in Bio-Inspired Computing. These projects include l-systems development and bio-inspired optimization methods.
Language:Jupyter Notebook 1Multi-objective optimization based on sloping plate optimization algorithm called Multi-objective Inclined Planes system optimization algorithm (MOIPO) is presented in this link. The proposed method uses the concept of Pareto optimization to identify non-dominant positions and an external tank to maintain these positions.
soft-computingartifical-intelligenseevolutionary-algorithmsmulti-objective-optimizationevolutionary-computationswarm-intelligenceoptimizationbio-inspired-optimizationoptimization-algorithmsengineering-optimisationmatlabhspiceheuristic-search-algorithmsmetaheuristic-optimisationmetaheuristic-algorithmsparticle-swarm-optimizationpareto-optimalitypareto-front0- Language:Python 0
harshal-vaze / Ant-Colony-Optimization-for-Path-Planning-using-Rule-based-in-Dynamic-and-Complex-Environment
In this project, a new ACO algorithm is proposed which ensures to solve the problems encountered in traditional ACO algorithms. This algorithm was tested on number of environments to examine efficiency, error margin and count computational time. The results ensured that the proposed ACO algorithm is completely efficient in small-scale environments and remarkably similar results were observed on testing it on the bigger-scale environment. The evaluations prove that the Ant Colony Optimization algorithm for path planning can provide rapid path planning with acceptable results and for future development can be integrated with the robot system to test in the real world scenarios.
0Martasil / Genetic_alg_generalizations_EMP_knapsack
Source code for the thesis entitled "Teoría de la aproximabilidad: análisis teórico y resolución práctica mediante algoritmos genéticos."
Language:Python 0- Language:Elixir 0
albertoxamin / bio-slicing-for-3d-printing
Since 3d printing is a long process, using bio-inspired algorithms to obtain the model that requires less support material and/or prints in less time
Language:Python
