Badawi, U.A. and Alsmadi, M.K.S. (2013) A Hybrid Memetic Algorithm (Genetic Algorithm and Great Deluge Local Search) with Back-Propagation Classifier for Fish Recognition. International Journal of Computer Science Issues, 10, 348-356.

has been cited by the following article:

• TITLE: Optimizing a Transportation System Using Metaheuristics Approaches (EGD/GA/ACO): A Forest Vehicle Routing Case Study

  AUTHORS: Hossein Havaeji, Thien-My Dao, Tony Wong

  KEYWORDS: Metaheuristics Algorithms, Transportation Costs, Optimization Approach, Cost Minimisation

  JOURNAL NAME: World Journal of Engineering and Technology, Vol.12 No.1, February 8, 2024

  ABSTRACT: The large-scale optimization problem requires some optimization techniques, and the Metaheuristics approach is highly useful for solving difficult optimization problems in practice. The purpose of the research is to optimize the transportation system with the help of this approach. We selected forest vehicle routing data as the case study to minimize the total cost and the distance of the forest transportation system. Matlab software helps us find the best solution for this case by applying three algorithms of Metaheuristics: Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Extended Great Deluge (EGD). The results show that GA, compared to ACO and EGD, provides the best solution for the cost and the length of our case study. EGD is the second preferred approach, and ACO offers the last solution.