A Hybrid Meta-Heuristic Algorithm to Solve Four-Dimensional Transportation Problems
Abstract
This paper presents the development of a hybrid meta-heuristic algorithm that combines the capabilities of a Genetic Algorithm and an Ant Colony Algorithm to address the complex challenges of non-classical four-dimensional transport problems. The study aims to improve transportation efficiency and reduce associated costs through an optimization strategy that uses genetic diversity and optimization mechanisms derived from Ants' behavior. The experimental approach methodology is designed and implemented using a hybrid algorithm on a test data set derived from realistic transportation scenarios, specifically focusing on measuring cost improvements. The main results revealed the ability of the hybrid algorithm to determine the optimal strategy and provide more efficient solutions at a lower cost than traditional algorithms. The research's impact on the field of meta-heuristic algorithms is significant, as it introduces a new approach that enhances the efficiency of transportation and logistics in various sectors, thereby opening new horizons for research and development in this area. The study also recommends further research, particularly the exploration of integrating the hybrid algorithm with machine learning techniques to increase its ability to predict and adapt to changes in transportation data.
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