Refining a Pheromone Trail Graph by Negative Feedback for Constraint Satisfaction Problems

Masukane, Takuya; Mizuno, Kazunori

doi:10.1109/taai48200.2019.8959905

Cited by 4 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the most successful strand of work on using negative learning in ACO deals with the application to constraint satisfaction problems (CSPs). Independently of each other, Ye et al [27] and Masukane and Mizuno [28,29] proposed negative feedback strategies for ACO algorithms in the context of CSPs. Both approaches make use of negative pheromone values in addition to the standard pheromone values.…”

Section: Existing Approachesmentioning

confidence: 99%

Adding Negative Learning to Ant Colony Optimization: A Comprehensive Study

Nurcahyadi

Blum

2021

Mathematics

View full text Add to dashboard Cite

Ant colony optimization is a metaheuristic that is mainly used for solving hard combinatorial optimization problems. The distinctive feature of ant colony optimization is a learning mechanism that is based on learning from positive examples. This is also the case in other learning-based metaheuristics such as evolutionary algorithms and particle swarm optimization. Examples from nature, however, indicate that negative learning—in addition to positive learning—can beneficially be used for certain purposes. Several research papers have explored this topic over the last decades in the context of ant colony optimization, mostly with limited success. In this work we present and study an alternative mechanism making use of mathematical programming for the incorporation of negative learning in ant colony optimization. Moreover, we compare our proposal to some well-known existing negative learning approaches from the related literature. Our study considers two classical combinatorial optimization problems: the minimum dominating set problem and the multi dimensional knapsack problem. In both cases we are able to show that our approach significantly improves over standard ant colony optimization and over the competing negative learning mechanisms from the literature.

show abstract

Section: Existing Approachesmentioning

confidence: 99%