Performance Investigation of Adaptive Ant Colony Optimization with 2-Opt Method

Performance Investigation of Adaptive Ant Colony Optimization with 2-Opt Method

An adaptive ant colony optimization with node clustering (AACO-NC) has been proposed as a method for solving the Traveling Salesman Problem (TSP). The AACO-NC first constructs a route and then improves the route by using a modified k-Opt method. However, the modified k-Opt method increases the compu...

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Veröffentlicht in:Journal of Signal Processing 2024/07/01, Vol.28(4), pp.115-118
Hauptverfasser: Kotake, Nozomi, Shibutani, Rikuto, Nakajima, Kazuma, Matsuura, Takafumi, Kimura, Takayuki
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container_end_page 118
container_issue 4
container_start_page 115
container_title Journal of Signal Processing
container_volume 28
creator Kotake, Nozomi
Shibutani, Rikuto
Nakajima, Kazuma
Matsuura, Takafumi
Kimura, Takayuki
description An adaptive ant colony optimization with node clustering (AACO-NC) has been proposed as a method for solving the Traveling Salesman Problem (TSP). The AACO-NC first constructs a route and then improves the route by using a modified k-Opt method. However, the modified k-Opt method increases the computational complexity for searching neighborhood solutions. In this work, we investigate the performance of AACO-NC when applying the 2-Opt method instead of the modified k-Opt method to reduce the computational complexity.
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