Abstract Dominating set is a set of vertices of a graph such that all other vertices have a neighbour in the dominating set. We propose a new order-based randomised local search (RLS o ) algorithm to solve minimum dominating set problem in large graphs. Experimental evaluation is presented for multiple types of problem instances. These instances include unit disk graphs, which represent a model of wireless networks, random scale-free networks, as well as samples from two social networks and real-world graphs studied in network science. Our experiments indicate that RLS o performs better than both a classical greedy approximation algorithm and two metaheuristic algorithms based on ant colony optimisation and local search. The order-based algorithm is able to find small dominating sets for graphs with tens of thousands of vertices. In addition, we propose a multi-start variant of RLS o that is suitable for solving the minimum weight dominating set problem. The application of RLS o in graph mining is also briefly demonstrated.
In contrast to weighted and connected dominating set problems, greedy and distributed @cite_31 approximation algorithms are still among the most popular in MDS. An experimental study of heuristics for MDS was conducted by Sanchis @cite_45 . Performance of algorithms for MDS in real-world networks has been compared by Neh ' e @cite_40 . There has been a recent surge in heuristic algorithms for MWDS @cite_30 @cite_21 @cite_12 . These algorithms are usually applied to classical benchmarks consisting of graphs inspired by the applications in wireless networks, with up to around @math vertices and with strong results in terms of numerical performance. However, the focus on scalability in the current experimental research on MDS is still somewhat limited.
@cite_30 @cite_21 @cite_40 @cite_45 @cite_31 @cite_12
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Minimum weight dominating set (MWDS) finds many uses in solving problems as varied as clustering in wireless networks, multi-document summarization in information retrieval and so on. It is proven to be NP-hard, even for unit disk graphs. Many centralized and distributed, greedy and approximation algorithms have been proposed for the MWDS problem. However, all the approximation algorithms are limited to unit disk graphs which are primarily used to model wireless networks. This assumption fails when applied to other domains. In this paper, we present two metaheuristic algorithms - a hybrid genetic algorithm and a hybrid ant colony optimization algorithm - for the problem of computing minimum weight dominating set. We compare our results with that of a greedy heuristic as well as the only other metaheuristic algorithm proposed so far in the literature and show that our algorithms are far better than these algorithms. The Minimum Weight Dominating Set (MWDS) problem is an important generalization of the Minimum Dominating Set (MDS) problem with extensive applications. This paper proposes a new local search algorithm for the MWDS problem, which is based on two new ideas. The first idea is a heuristic called two-level configuration checking (CC2), which is a new variant of a recent powerful configuration checking strategy (CC) for effectively avoiding the recent search paths. The second idea is a novel scoring function based on the frequency of being uncovered of vertices. Our algorithm is called CC2FS, according to the names of the two ideas. The experimental results show that, CC2FS performs much better than some state-of-the-art algorithms in terms of solution quality on a broad range of MWDS benchmarks. The minimum weight-dominating set (MWDS) problem is NP-hard and has a lot of applications in the real world. Several metaheuristic methods have been developed for solving the problem effectively, but suffering from high CPU time on large-scale instances. In this paper, we design an effective hybrid memetic algorithm (HMA) for the MWDS problem. First, the MWDS problem is formulated as a constrained 0–1 programming problem and is converted to an equivalent unconstrained 0–1 problem using an adaptive penalty function. Then, we develop a memetic algorithm for the resulting problem, which contains a greedy randomized adaptive construction procedure, a tabu local search procedure, a crossover operator, a population-updating method, and a path-relinking procedure. These strategies make a good tradeoff between intensification and diversification. A number of experiments were carried out on three types of instances from the literature. Compared with existing algorithms, HMA is able to find high-quality solutions in much less CPU time. Specifically, HMA is at least six times faster than existing algorithms on the tested instances. With increasing instance size, the CPU time required by HMA increases much more slowly than required by existing algorithms. The minimum dominating set (MDS) problem is one of the central problems of algorithmic graph theory and has numerous applications especially in graph mining. In this paper, we propose a new hybrid method based on genetic algorithm (GA) to solve the MDS problem, called shortly HGA-MDS. The proposed method invokes a new fitness function to effectively measure the solution qualities. The search process in HGA-MDS uses local search and intensification schemes beside the GA search methodology in order to achieve faster performance. Finally, the performance of the HGA-MDS is compared with the standard GA. The new invoked design elements in HGA-MDS show its promising performance compared with standard GA.