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This MATLAB project implements a hybrid optimization algorithm that combines Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The algorithm is designed to optimize a set of parameters (genes) for various problems, making it flexible and adaptable to different optimization scenarios.
A cloud simulation library based on cloudsim, it aims to provide all known cloud scheduling algorithms with the help from community developers, as well as automate various simulation scenarios.
Genetic Algorithm Assisted HIDMS-PSO: A Novel GA-PSO Hybrid Algorithm for Global Optimisation. Source code for the paper: IEEE Congress on Evolutionary Computation (CEC) https://ieeexplore.ieee.org/document/9504852
Applied three different techniques⚡ to tackle the most efficient solution of finding the Subarray of an array, which holds the maximum sum of the whole array.
Proof how a Hybrid sorting algorithm with respect to a specific threshold value is a better solution any other sorting algorithm individually, in terms of time complexity.