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Customising optimisation metaheuristics via hyper-heuristic search (CUSTOMHyS). This framework provides tools for solving, but not limited to, continuous optimisation problems using a hyper-heuristic approach for customising metaheuristics. Such an approach is powered by a strategy based on Simulated Annealing. Also, several search operators ser…
The P-Median Problem project uses metaheuristic optimization to solve the p-median location problem, with Jupyter notebooks implementing random sampling and local search algorithms to minimize service distances.
This repository contains the Matlab source codes of the Improved Hybrid Growth Optimizer (IHGO) algorithm, an improved variant of the recently-developed Growth Optimizer (GO) algorithm.
This work introduces Differentiated Creative Search (DCS), a groundbreaking optimization algorithm that revolutionizes traditional decision-making systems in complex environments.
This project implements two nature-inspired optimization algorithms: Moth Flame Optimization (MFO) and Honey Badger Optimization (HBO). Both algorithms are designed to solve complex optimization problems by mimicking behaviors observed in nature. also it includes a path finding algorithm, A-star
This research proposes a novel order batching approach for warehouses to minimize total tardiness, considering category, weight, and fragility constraints. A Set-based Mayfly Algorithm (SBMA) is developed, adapting the Mayfly Algorithm to the discrete problem and leveraging swarming/mating behaviors to avoid local optima.