BEPSO: Biased Eavesdropping Particle Swarm Optimisation Algorithm . Source code for the paper: IEEE SSCI https://ieeexplore.ieee.org/document/9660113
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Updated
Sep 4, 2024 - MATLAB
BEPSO: Biased Eavesdropping Particle Swarm Optimisation Algorithm . Source code for the paper: IEEE SSCI https://ieeexplore.ieee.org/document/9660113
A Collection Of The State-of-the-art Metaheuristic Algorithms In Python (Metaheuristic/Optimizer/Nature-inspired/Biology)
An implementation of multi objective PSO for UAV path planning
Evolutionary Computation: A Modern Perspective ---> This is a free online book, which is actively updated now (from 2023 to 2027).
Coursework for CSCI-633 Bio-Inspired Intelligent Systems
Projects in IT3708 bio-inspired AI, a second degree level subject at NTNU, 2024
🧬This repository contains implementations of various bio-inspired optimization algorithms, along with example notebooks and resources for demonstration.
Bio inspired algorithms in Elixir
Python3 framework for comparative study of recent metaheuristics
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
An ant colony optimization program for solving image segmentation created in the context of IT3708 at NTNU.
Sample Code Collection of Nature-Inspired Computational Methods
Bio-inspired Computing
Study and implementation of bioinspired metaheuristics (ACO, CLONALG, GA, PSO)
AHPSO: Altruistic Heterogeneous Particle Swarm Optimisation Algorithm. Source code for the paper: IEEE SSCI https://ieeexplore.ieee.org/document/9660149
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.
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 environmen…
A 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.
Python code for ECS-NL.
Simple console implementation of the Krill Herd (KH) algorithm
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