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Multiple Ant Colony Algorithm Combining Community Relationship Network.

Jiabo Zhao1, Xiaoming You1, Qianqian Duan1

  • 1College of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620 China.

Arabian Journal for Science and Engineering
|February 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces the community-based ant colony algorithm (CACO) to improve solutions for large-scale Traveling Salesperson Problems (TSP). CACO enhances accuracy and convergence by analyzing all ant routes and community structures, outperforming existing methods.

Keywords:
Ant colony algorithmCommunity detectionModularityRoute relation networkTSP

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Area of Science:

  • Artificial Intelligence
  • Optimization Algorithms
  • Computational Intelligence

Background:

  • The Ant Colony Algorithm (ACA) is effective for combinatorial optimization but struggles with balancing solution accuracy and convergence speed in large-scale Traveling Salesperson Problems (TSP).
  • Existing ACA improvements often overlook the route information from the majority of ants, focusing instead on elite ants.

Purpose of the Study:

  • To propose a novel algorithm, the multiple ant colony algorithm combining community relationship network (CACO), to enhance solution accuracy for large-scale TSP.
  • To leverage the collective intelligence of all ants by constructing a route relationship network and utilizing community detection.

Main Methods:

  • CACO collects route information from all ants to build a route relationship network.
  • Community detection with modularity is employed to divide the network into communities, reflecting ant-city affinities.
  • High-quality route segments within communities are identified, and their pheromones are integrated for feedback, guiding better route exploration.
  • A mutual assistance strategy is incorporated to improve exploration by complementary interactions between superior and inferior ant populations.

Main Results:

  • CACO demonstrated superior performance compared to well-known improved algorithms across 28 TSP instances.
  • The algorithm showed significant improvements, particularly in solving large-scale TSP instances.
  • The feedback loop of route information collection, community detection, and pheromone integration effectively drives results closer to the optimal solution.

Conclusions:

  • The proposed CACO algorithm effectively addresses the limitations of traditional ACA for large-scale TSP.
  • By incorporating community structure and all ant information, CACO achieves better solution accuracy and convergence speed.
  • CACO represents a significant advancement in ant colony optimization for complex routing problems.