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Aspiration level-based non-dominated sorting genetic algorithm-II and III for multi-objective shortest path problem in a trapezoidal environment

Author

Listed:
  • Aniket Todkar
  • Jayesh M. Dhodiya

Abstract

The present article provides aspiration level (AL)-based non-dominated sorting genetic algorithm (NSGA)-II and AL-based NSGA-III utilising an exponential membership function (EMF) with possibility distribution to tackle fuzzy multi-objective shortest path problem (FMOSPP). In this study, fuzzy judgement for trapezoidal fuzzy number is classified for the decision-maker (DM) to optimise fuzzy objective function scenarios like optimistic, most likely lower, most likely upper, and pessimistic at the same time, utilising α-level sets. A numerical demonstration and a dataset have been offered to portray the application of the recommended methodologies. This study suggests that AL-based NSGA-II and AL-based NSGA-III can handle FMOSPP effectively and efficiently with optimal outputs. These methods provide solutions as per DM's AL. Thus it is very effective to manage real-world multi-objective shortest path problems (MOSPPs).

Suggested Citation

  • Aniket Todkar & Jayesh M. Dhodiya, 2024. "Aspiration level-based non-dominated sorting genetic algorithm-II and III for multi-objective shortest path problem in a trapezoidal environment," International Journal of Mathematics in Operational Research, Inderscience Enterprises Ltd, vol. 27(2), pages 223-253.
    • Handle: RePEc:ids:ijmore:v:27:y:2024:i:2:p:223-253
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