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A Carbon Aware Ant Colony System (CAACS)

Marina Lin, Laura P. Schaposnik

TL;DR

This novel approach leverages the natural efficiency of ant colony pheromone trails to find optimal routes, balancing both environmental and economic objectives, and provides a powerful tool for real-world applications, including network design, delivery route planning, and commercial aircraft logistics.

Abstract

In an era where sustainability is becoming increasingly crucial, we introduce a new Carbon-Aware Ant Colony System (CAACS) Algorithm that addresses the Generalized Traveling Salesman Problem (GTSP) while minimizing carbon emissions. This novel approach leverages the natural efficiency of ant colony pheromone trails to find optimal routes, balancing both environmental and economic objectives. By integrating sustainability into transportation models, CAACS provides a powerful tool for real-world applications, including network design, delivery route planning, and commercial aircraft logistics. Our algorithm's unique bi-objective optimization advances the study of sustainable transportation solutions.

A Carbon Aware Ant Colony System (CAACS)

TL;DR

This novel approach leverages the natural efficiency of ant colony pheromone trails to find optimal routes, balancing both environmental and economic objectives, and provides a powerful tool for real-world applications, including network design, delivery route planning, and commercial aircraft logistics.

Abstract

In an era where sustainability is becoming increasingly crucial, we introduce a new Carbon-Aware Ant Colony System (CAACS) Algorithm that addresses the Generalized Traveling Salesman Problem (GTSP) while minimizing carbon emissions. This novel approach leverages the natural efficiency of ant colony pheromone trails to find optimal routes, balancing both environmental and economic objectives. By integrating sustainability into transportation models, CAACS provides a powerful tool for real-world applications, including network design, delivery route planning, and commercial aircraft logistics. Our algorithm's unique bi-objective optimization advances the study of sustainable transportation solutions.
Paper Structure (20 sections, 34 equations, 30 figures, 2 tables, 5 algorithms)

This paper contains 20 sections, 34 equations, 30 figures, 2 tables, 5 algorithms.

Table of Contents

  1. Introduction
  2. Background
  3. The Generalized Traveling Salesman Problem
  4. Approaches to the GTSP
  5. Ant Colony Optimization
  6. Carbon Emission Function
  7. CAACS: A Carbon Aware Ant Colony System Algorithm
  8. Stage I: Finding a Path
  9. Stage II: Updating the graph
  10. Scaling Emission Factor
  11. The Effect of the Number of Ants
  12. Solution Quality
  13. Run Time
  14. Empirical Analysis
  15. Time Complexity
  16. ...and 5 more sections

Figures (30)

  • Figure 1: Example of a Valid Solution for a GTSP Problem with 3 clusters and 7 nodes.
  • Figure 2: Example of path construction in a 4-node graph. Note that in reality, for ACO, the next node selected isn't always the closest, but the closer nodes have a higher probability of being selected. The thick edges represent the path selected.
  • Figure 3: (a) Initial Pheromone buildup on a 4 node graph after multiple ants have found valid paths. (b) The removal of excess pheromones on the graph so the following ants can still explore new nodes.
  • Figure 4: (a) Initial Pheromone distribution on a 4 node graph. (b) Pheromone Reinforcement on a 4 node graph of the shortest paths in Stage 3 of Figure \ref{['fig:example1']}.
  • Figure 5: Sample grid, with cities represented as 2x2 diamonds and clusters as different colors. In this grid, there are 20 clusters and 100 nodes. Overlapping diamonds visually do not represent overlapping cities, but it is due to the scale of the figure and does not make a difference in the algorithm.
  • ...and 25 more figures

Theorems & Definitions (6)

  • Definition 1
  • Definition 2
  • Definition 3
  • Example 1
  • Example 2
  • Example 3