The EOA research group maintains and utilizes a suite of specialized software libraries and platforms for the development and testing of optimization algorithms:
MATLAB Metaheuristic Toolbox: A comprehensive suite for prototyping and comparing various Optimization Algorithms.
PyGMO (Python Global Multiobjective Optimizer): A Python library designed for parallel optimization of complex, multi-objective problems.
DEAP (Distributed Evolutionary Algorithms in Python): A flexible evolutionary computation framework used for rapid prototyping of custom algorithms.
To support large-scale experimentation and simulation, the EOA group leverages:
High-Performance Computing Cluster (HPC): A multi-core, parallel processing environment with GPU nodes, enabling high-volume algorithm testing.
Dedicated Workstations: Local systems with high-performance CPUs and CUDA-enabled GPUs for deep learning-based optimization models and simulations.
Version-Controlled Codebase: A private GitHub is maintained for internal code sharing, algorithm benchmarking, and collaborative development.