Open Access

ARTICLE

Enhancing Hierarchical Task Network Planning through Ant Colony Optimization in Refinement Process

Mohamed Elkawkagy¹, Ibrahim A. Elgendy^2,*, Ammar Muthanna^3,4, Reem Ibrahim Alkanhel⁵, Heba Elbeh¹

1 Department of Computer Science, Faculty of Computers and Information, Menoufia University, Shibin Elkom, Menoufia, 32511, Egypt
2 IRC for Finance and Digital Economy, KFUPM Business School, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia
3 Department of Telecommunication Networks and Data Transmission, The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, Saint Petersburg, 193232, Russia
4 Department of Probability Theory and Cyber Security, Peoples’ Friendship University of Russia (RUDN University), Moscow, 117198, Russia
5 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia

* Corresponding Author: Ibrahim A. Elgendy. Email:

Computers, Materials & Continua 2025, 84(1), 393-415. https://doi.org/10.32604/cmc.2025.063766

Received 23 January 2025; Accepted 08 April 2025; Issue published 09 June 2025

Abstract

Hierarchical Task Network (HTN) planning is a powerful technique in artificial intelligence for handling complex problems by decomposing them into hierarchical task structures. However, achieving optimal solutions in HTN planning remains a challenge, especially in scenarios where traditional search algorithms struggle to navigate the vast solution space efficiently. This research proposes a novel technique to enhance HTN planning by integrating the Ant Colony Optimization (ACO) algorithm into the refinement process. The Ant System algorithm, inspired by the foraging behavior of ants, is well-suited for addressing optimization problems by efficiently exploring solution spaces. By incorporating ACO into the refinement phase of HTN planning, the authors aim to leverage its adaptive nature and decentralized decision-making to improve plan generation. This paper involves the development of a hybrid strategy called ACO-HTN, which combines HTN planning with ACO-based plan selection. This technique enables the system to adaptively refine plans by guiding the search towards optimal solutions. To evaluate the effectiveness of the proposed technique, this paper conducts empirical experiments on various domains and benchmark datasets. Our results demonstrate that the ACO-HTN strategy enhances the efficiency and effectiveness of HTN planning, outperforming traditional methods in terms of solution quality and computational performance.

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Keywords

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