Enhanced Genetic Algorithm Optimization Models for Vehicular Routing Problems
K. Premkumar1, R. Baskaran2, M. Shanmugam3

1K. Premkumar*, Computer Science and Engineering, Manonmaniam Sundaranar University, (Tamil Nadu), India.
2R. Baskaran, 3Department of Computer Science and Engineering, Anna University, Chennai,(Tamil Nadu), India.
3M. Shanmugam, Computer Science & Engineering, Vignan’s Foundation for Science, Technology, and Research, Guntur, India.
Manuscript received on November 22, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 3140-3149 | Volume-9 Issue-2, December, 2019. | Retrieval Number:  B4410129219/2019©BEIESP | DOI: 10.35940/ijeat.B4410.129219
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The Vehicle Routing Problem (VRP) is one of the most studied combinatorial optimization problems because of its practical relevance and complexity. Though there are several techniques have been proposed to solve the VRPs and its variants effectively, each technique has its own tradeoff values in terms of the performance factors. From this perspective, the work presented in this paper proposed an intelligent routing strategy for VRP based on distance values between the cities. The proposed strategy uses an enhanced model of Genetic Algorithm to find the optimal tour paths among the cities under distance based optimized tour path estimation scenarios. For distance-based optimization approach, experiments were performed on the standard benchmark TSP instances obtained from TSPLIB. A set of fine-grained result analyses demonstrated that the proposed model of routing strategies performed comparatively better w.r.t. the existing relevant approaches. By considering this problem as the base, a distinct model was developed as a set of assistive modules for Genetic Algorithms (GA), which are aimed at improving the overall efficiency of the typical GA, particularly for optimization problems. The capability of the proposed optimization models for VRP is demonstrated at various levels, particularly at the population initialization stage, using a set of well-defined experiments.
Keywords:  Genetic Algorithm, Vehicle Routing, TSP