New results of ant algorithms for the Linear Ordering Problem
Camelia-M. Pintea, Camelia Chira, D. Dumitrescu
TL;DR
This paper presents new results using ant colony algorithms, specifically ACS and SB-SAM, to solve instances of the Linear Ordering Problem, demonstrating their effectiveness on complex combinatorial problems.
Contribution
It introduces new results for LOP instances utilizing advanced ant algorithms, enhancing understanding of their applicability and performance.
Findings
Successful application of ACS and SB-SAM to LOP instances
Improved solution quality on selected LOP problems
Demonstrated effectiveness of ant algorithms for complex combinatorial optimization
Abstract
Ant-based algorithms are successful tools for solving complex problems. One of these problems is the Linear Ordering Problem (LOP). The paper shows new results on some LOP instances, using Ant Colony System (ACS) and the Step-Back Sensitive Ant Model (SB-SAM).
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Taxonomy
TopicsMetaheuristic Optimization Algorithms Research · Constraint Satisfaction and Optimization · Neural Networks and Applications