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Hybrid Algorithm Based on Ant Colony Optimization and Simulated Annealing Applied to the Dynamic Traveling Salesman

Petr Stodola¹, Karel Michenka¹, Jan Nohel¹

¹Department of Intelligence Support, University of Defence, Kounicova 65, 662 10 Brno, Czech Republic.

Entropy (Basel, Switzerland)

|December 8, 2020

Summary

This summary is machine-generated.

A new hybrid algorithm combining Ant Colony Optimization (ACO) and Simulated Annealing (SA) effectively solves the dynamic traveling salesman problem (DTSP). This approach significantly outperforms existing methods by leveraging past information for dynamic optimization.

Keywords:

ant colony optimization combinatorial dynamic optimization problem dynamic traveling salesman problem hybridization metaheuristic algorithm simulated annealing

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Area of Science:

Operations Research
Computer Science
Artificial Intelligence

Background:

The dynamic traveling salesman problem (DTSP) is a complex combinatorial optimization challenge.
Existing methods struggle with the evolving nature of DTSP instances.
Dynamic optimization requires algorithms that adapt to changing environments.

Purpose of the Study:

To propose a novel hybrid metaheuristic algorithm for the DTSP.
To enhance optimization performance by combining Ant Colony Optimization (ACO) and Simulated Annealing (SA).
To investigate the benefits of utilizing historical data in dynamic optimization.

Main Methods:

Developed a hybrid metaheuristic algorithm integrating ACO and SA principles.
Implemented a pheromone matrix to transfer knowledge across dynamic iterations.
Validated the algorithm on benchmark DTSP instances of varying sizes.

Main Results:

The proposed hybrid algorithm significantly outperformed four state-of-the-art metaheuristic approaches.
Demonstrated superior performance across small, medium, and large DTSP benchmark instances.
Analysis revealed favorable convergence speed, population diversity, and computational efficiency.

Conclusions:

The hybrid ACO-SA algorithm is highly effective for solving the DTSP.
Leveraging dynamic environmental knowledge through pheromone transfer is crucial for performance.
The proposed method represents a significant advancement in dynamic combinatorial optimization.