Abstract
Evolutionary techniques such as Genetic algorithms (GA) are used to perform approximation for real-world problems and provide optimized solutions. Most of the time, these techniques provide desirable solutions and. The GAs depend on various operations such as selection criteria, crossover, and mutation operators. The GA is a useful technique for challenging (polynomial-time hardness and non-deterministic) problems because of their robustness and flexibility. Traveling Salesman Problem (TSP) is one of the popular problems that includes many real-world applications such as cutting wallpaper, computer wiring, vehicle routing, and job sequencing. This paper proposes an improved GA to solve the TSP of a real-world water line system. A multi-agent system supports the improved GA to handle and improve the mutation process. The agents’ role is to break down the population into smaller parts and update several versions of the populations instead of updating one population in the mutation phase of the GA run cycle. The improved GA is used for the applied TSP to minimize the total distance and reduce the cost of the water line system. We generate ten different random coordinates to formulate the water line network of the system. The result indicates that the GA efficiently reduces the total cost by minimizing the distance. The GA scores a total profit of 760.874, and the percentage of distance decrease is 6.03% to the water line system data as compared with the k-NN as a benchmark algorithm. For the ten randomly generated coordinates, the expected saving is as large as 10.002% (average of ten) compared to the results of the K-NN algorithm.
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This research was supported by Universiti Tun Hussein Onn Malaysia (UTHM) through Tier 1 vot. H938.
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Appendix A: Samples of the Coordinates of the Used Dataset
Appendix A: Samples of the Coordinates of the Used Dataset
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Mostafa, S.A., Juman, Z.A.M.S., Nawi, N.M., Mahdin, H., Mohammed, M.A. (2022). Improving Genetic Algorithm to Attain Better Routing Solutions for Real-World Water Line System. In: Ghazali, R., Mohd Nawi, N., Deris, M.M., Abawajy, J.H., Arbaiy, N. (eds) Recent Advances in Soft Computing and Data Mining. SCDM 2022. Lecture Notes in Networks and Systems, vol 457. Springer, Cham. https://doi.org/10.1007/978-3-031-00828-3_29
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