Application of CMSA to the Minimum Positive Influence Dominating Set Problem

Akbay, Mehmet Anıl; Blum, Christian

doi:10.3233/faia210112

Cited by 5 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From the experiment results, it is demonstrated that CMSA can achieve similar performance compared to the exact solver for small to medium sized instances, while its performance is significantly better than the exact solver when it came to large instances. See [3,19,5,18] for recent research using CMSA.…”

Section: Hybrid Heuristicsmentioning

confidence: 99%

A K-Prototype Clustering Assisted Hybrid Heuristic Approach for Train Unit Scheduling

Copado-Méndez

Lin

Barrena

et al. 2022

Communications in Computer and Information Science

View full text Add to dashboard Cite

This paper presents a K-Prototype assisted hybrid heuristic approach called SLIM+KP for solving large instances of the Train Unit Scheduling Optimization (TUSO) problem. TUSO is modelled as an Integer Multi-commodity Flow Problem (IMCFP) based on a Directed Acyclic Graph (DAG). When the problem size goes large, the exact solver is unable to solve it in reasonable time. Our method uses hybrid heuristics by iteratively solving reduced instances of the original problem where only a subset of the arcs in the DAG are heuristically chosen to be optimised by the same exact solver. K-Prototype is a clustering method for partitioning. It is an improvement of K-Means and K-Modes to handle clustering with the mixed data types. Our approach is designed such that the arcs of the DAG are clustered by K-prototype and each time only a small fraction of the arcs are selected to form the reduced instances. The capabilities of this framework were tested by realworld cases from UK train operating companies and compared with the results from running an exact integer solver. Preliminary results indicate the the proposed methodology achieves the same optimal solutions as the exact solver for small instances but within shorter time, and yields good solutions for instances that were intractable for the exact solver.

show abstract

Section: Hybrid Heuristicsmentioning

confidence: 99%

A K-Prototype Clustering Assisted Hybrid Heuristic Approach for Train Unit Scheduling

Copado-Méndez

Lin

Barrena

et al. 2022

Communications in Computer and Information Science

View full text Add to dashboard Cite

show abstract

“…More recently, CMSA has been successfully applied in several instances and compared with other heuristics. In Akbay and Blum (2021), it is used to solve an NP-hard problem from the family of dominating set problems in undirected graphs, where the minimum positive influence dominating set problem is studied. The experiments show that CMSA outperforms the current state-of-the-art metaheuristics from the literature.…”

Section: Blum Et Al (2016) Presents a General Hybrid Metaheuristic Fo...mentioning

confidence: 99%

An auxiliary hybrid heuristic approach for objective function design evaluation

Lei

Kwan

Lin

2023

Preprint

View full text Add to dashboard Cite

Real-world combinatorial optimization problems are mostly NP-hard, and often only near-optimal solutions can be obtained practically. To differentiate as fine-grained as possible the near-optimal solutions is therefore desirable. Moreover, a real-world problem may have numerous possible structural properties of concern to the practitioners, too numerous to be all elicited and incorporated as optimization criteria in an objective function. In contrast with pure heuristics, we consider hybrid (meta-)heuristics that utilise an exact solver iteratively to solve a series of significantly reduced problem instances converging to near-optimal solutions within practical time. To avoid the hybrid heuristic being stranded in a “poorly differentiated” solution space, an effective objective function design plays an important role. We propose a methodology to benchmark the effectiveness of alternative objective function designs. The main metric used is the structural similarity between the solutions obtained by the hybrid heuristic and by the exact solver. Several other solution features are also distilled and aggregated in the benchmark. This methodology is explained and demonstrated on a train unit scheduling problem tested with four alternative objective functions. The results show that two of them are significantly more effective than the others in differentiating solutions of different qualities and speeding up the solution process. Moreover, some criteria not modeled explicitly could also be satisfied implicitly in the effective objective designs.

show abstract

“…Unfortunately, such a high sensitivity to parameter values was noticed in some applications of CMSA in the literature. One of the examples concerns the preliminary application of CMSA to an NP-hard CO problem known as the minimum positive influence dominating set (MPIDS) problem [22]. Therefore, in this paper, we propose a self-adaptive variant of CMSA, called Adapt-CMSA, with the aim of obtaining an algorithm less sensitive to parameter values.…”

Section: Contributionmentioning

confidence: 99%

“…The first metaheuristic that was able to improve over [34] is the iterated carousel approach from [37]. Finally, the currently best metaheuristics are our own approaches: a negative learning ant colony optimisation approach from [38] and the preliminary standard CMSA approach from [22]. Both approaches perform on a comparable level.…”

Section: Application To the Mpids Problemmentioning

confidence: 99%

“…Note that the information about average computation times was not provided in [37] for ICG. The authors, however, state that they chose a computation time Table 1 Numerical results for small to medium size instances a These best-known results were obtained by [22] (CA-AstroPh) and [38] limit of |V| ⋅ 30∕1000 , because this assured convergence of their algorithm in the case of all considered problem instances.…”

Section: Experiments Regarding Instances From the Literaturementioning

confidence: 99%

See 1 more Smart Citation

A Self-Adaptive Variant of CMSA: Application to the Minimum Positive Influence Dominating Set Problem

Akbay

Serrano

Blum

2022

Int J Comput Intell Syst

Self Cite

View full text Add to dashboard Cite

Construct, merge, solve and adapt (CMSA) is a recently developed, generic algorithm for combinatorial optimisation. Even though the usefulness of the algorithm has been demonstrated by applications to a range of combinatorial optimisation problems, in some applications, it was observed that the algorithm can be sensitive to parameter settings. In this work, we propose a self-adaptive variant of CMSA, called Adapt-CMSA, with the aim of reducing the parameter sensitivity of the original version of CMSA. The advantages of this new CMSA variant are demonstrated in the context of the application to the so-called minimum positive influence dominating set problem. It is shown that, in contrast to CMSA, Adapt-CMSA does not require a computation time intensive parameter tuning process for subsets of the considered set of problem instances. In fact, after tuning Adapt-CMSA only once for the whole set of benchmark instances, the algorithm already obtains state-of-the-art results. Nevertheless, note that the main objective of this paper is not the tackled problem but the improvement of CMSA.

show abstract

Application of CMSA to the Minimum Positive Influence Dominating Set Problem

Cited by 5 publications

References 15 publications

A K-Prototype Clustering Assisted Hybrid Heuristic Approach for Train Unit Scheduling

A K-Prototype Clustering Assisted Hybrid Heuristic Approach for Train Unit Scheduling

An auxiliary hybrid heuristic approach for objective function design evaluation

A Self-Adaptive Variant of CMSA: Application to the Minimum Positive Influence Dominating Set Problem

Contact Info

Product

Resources

About