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Fuzzy Based Parameter Adaptation in ACO for Solving VRP

Author

Listed:
  • Sandhya

    (Department of Computer Science and Engineering, Maharishi Markandeswar Deemed to be University Mullana, Ambala, India)

  • Rajiv Goel

    (Department of Computer Science, Government College, Naraingarh, Ambala, India)

Abstract

Ant Colony Optimization, a popular class of metaheuristics, have been widely applied for solving optimization problems like Vehicle Routing Problem. The performance of ACO is affected by the values of parameters used. However, in literature, few methods are proposed for parameter adaptation of ACO. In this article, a fuzzy-based parameter control mechanism for ACO has been developed. Three adaptive strategies FACO-1, FACO-2, FACO-3 are proposed for determining values of parameters alpha and beta, and evaporation factor separately as well as for all three parameters simultaneously. The performance of proposed strategies is compared with standard ACS on TSP and VRP benchmarks. Computational results on standard benchmark problems shows the effectiveness of the strategies.

Suggested Citation

  • Sandhya & Rajiv Goel, 2019. "Fuzzy Based Parameter Adaptation in ACO for Solving VRP," International Journal of Operations Research and Information Systems (IJORIS), IGI Global, vol. 10(2), pages 65-81, April.
    • Handle: RePEc:igg:joris0:v:10:y:2019:i:2:p:65-81
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