Search Diversification in ACO Algorithms and Its Application

• Published in: Cybernetics and systems analysis Vol. 61; no. 1; pp. 21 - 33
• Main Author: Hulianytskyi, L. F.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.01.2025; Springer Nature B.V
• Subjects: Algorithms; Ant colony optimization; Apexes; Artificial Intelligence; Combinatorial analysis; Control; Graph theory; Heuristic methods; Mathematics; Mathematics and Statistics; Optimization algorithms; Processor Architectures; Search strategies; Shortest path algorithms; Software Engineering/Programming and Operating Systems; Systems Analysis; Systems Theory; Ant Colony Optimization; combinatorial optimization; routing; traveling salesman problem; optimization of flight routes; computational experiment; UAV
• ISSN: 1060-0396; 1573-8337
• DOI: 10.1007/s10559-025-00741-7

The author presents an approach to developing diversified ant colony optimization (ACO) algorithms, one of the most widely used combinatorial optimization methods. The proposed diversification in ACO algorithms is based on considering multiple options for extending the current solution fragment by incorporating several vertices of the problem graph into the route instead of just one, as is typically done. The ability of ants to foresee multiple search steps ahead increases the likelihood of avoiding suboptimal solutions and finding more accurate ones. This approach is applied to create metaheuristic algorithms for solving various combinatorial optimization problems. The results of computational experiments on a series of applied combinatorial optimization problems across different classes demonstrate the successful modification of the known ACO algorithms.