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An enhanced genetic algorithm solution for itinerary recommendation considering various constraints.

Muhammed Şehab1,2, Metin Turan2

  • 1Computer Technology Department, Avrupa Vocational School, Kocaeli Health and Technology University, Kocaeli, Turkey.

Peerj. Computer Science
|December 9, 2024
PubMed
Summary

This study introduces an enhanced genetic algorithm (GA) for optimizing tourist itineraries, considering factors like cost and time. The improved GA offers more efficient and accurate travel recommendations for popular destinations.

Keywords:
Crossover methodFitness functionGenetic algorithmItinerary recommendationMutation ratioOptimal itinerary

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Operations Research

Background:

  • Itinerary recommendation is a complex optimization problem.
  • Existing methods often fail to account for diverse tourist constraints and preferences.
  • Efficient and personalized travel planning is crucial for the tourism industry.

Purpose of the Study:

  • To develop an enhanced genetic algorithm (GA) for optimizing tourist itineraries.
  • To incorporate multiple constraints such as distance, time, cost, budget, and desired activities.
  • To improve the efficiency and accuracy of itinerary recommendation systems.

Main Methods:

  • An enhanced genetic algorithm (GA) was developed.
  • A customized fitness function was designed to handle tourism-specific complexities.
  • Novel methods including "Copy Order Crossover" and modified tournament selection were implemented.
  • Swap mutation method was enhanced for greater efficiency and adaptability.

Main Results:

  • The enhanced GA achieved significant improvement rates: 43.89% for the Istanbul dataset and 56.60% for the Burma dataset.
  • The proposed approach demonstrated superiority over alternative methods in terms of time, efficiency, and accuracy.
  • Experimental evaluations validated the effectiveness of the enhanced GA on benchmark and custom datasets.

Conclusions:

  • The enhanced GA provides a superior solution for the tourist itinerary recommendation problem.
  • The approach effectively balances multiple constraints and preferences for optimal itinerary generation.
  • This research offers a promising direction for future advancements in personalized travel planning systems.