A Python platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model.

dnn_opt is a machine learning library to perform optimization and neural network training by using meta-heuristics with multi-core and GPU support

A ray-based library of Distributed POPulation-based OPtimization for Large-Scale Black-Box Optimization.

(Code) A new workload prediction model using extreme learning machine and enhanced tug of war optimization

Implementation of a solution to Jdata GOC

VNS-SOLVER : implementation of the VNS meta-heuristic using the C programming language

Machine Learning Hyper-parameter Tuning processes

HGJO: An improved golden jackal optimization for Otsu’s method based multilevel image thresholding

Techniques and applications of optimisation in operations research, including linear programming, integer programming, dynamic programming and meta-heuristics. Use of Python and the Gurobi optimisation package for linear and integer programming.

Julia library for the Determinant-Optimality Problem (D-OPT)

Efficient Time-series Forecasting using Neural Network and Opposition-based Coral Reefs Optimization

Online selection hyper-heuristic with generic parameter control in low-level heuristics (meta-heuristic).

(Code IQSO-MLP) nQSV-Net: a novel queuing search variant for global space search and workload modeling

Meta-heuristic approaches to select important features only for performance enhancement in effective Intrusion Detection System.

Multi-Objective Agent-Based Hyper-Heuristic

Master Thesis for M.Sc. Business Education - Pre-Trained Denoising Autoencoders Long Short-Term Memory Networks as probabilistic Models for Estimation of Distribution Genetic Programming

A C++ program that resolve a TSP problem using Local Search and Tabu Search metaheuristics with small variants. Download report here https://github.com/EduBic/TSP-MetaheuristicSolvers-Documentation/releases

A project @ Production System Analysis.

Functional Link Adaptive Bacterial Foraging using Life Cycle and Social Learning