Self-Adaptive Ant System with Hierarchical Clustering for the Thief Orienteering Problem
Pages 723 - 730
Abstract
Thief Orienteering Problem (ThOP) is a multi-component problem with two interdependent sub-problems Knapsack Problem and Orienteering Problem. ACO++, a state-of-the-art metaheuristic for ThOP, combines the MAX-MIN Ant System (MMAS) algorithm for route construction, a randomized heuristic for packing plan creation, and the 2-opt method for local search. The excellent reported performance of ACO++, however, is obtained using different sets of parameter values that have been extensively fine-tuned for each specific group of problem instances. In this paper, we present a novel self-adaptive variant of ACO++. Without requiring a cumbersome tuning process, our approach employs adaptive mechanisms to adjust the parameters to each particular problem instance during the algorithm runtime. We also use a lazy evaporation technique and a hierarchical clustering procedure to improve the efficiency of ants exploring the search space. Among the 432 benchmark instances, our proposed Self-Adaptive Ant System with Hierarchical Clustering (SAAS-HC) produces superior results compared to previous state-of-the-art approaches. The source code is available at https://github.com/ELO-Lab/SAAS-HC.
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Published In
December 2023
1058 pages
ISBN:9798400708916
DOI:10.1145/3628797
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Published: 07 December 2023
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- Refereed limited
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- University of Information Technology - Vietnam National University Ho Chi Minh City
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SOICT 2023
SOICT 2023: The 12th International Symposium on Information and Communication Technology
December 7 - 8, 2023
Ho Chi Minh, Vietnam
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Overall Acceptance Rate 147 of 318 submissions, 46%
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