The weighted independent domination problem: ILP model and algorithmic approaches

This work deals with the so-called weighted independent domination problem, which is an N P -hard combinatorial optimization problem in graphs. In contrast to previous theoretical work from the liter- ature, this paper considers the problem from an algorithmic perspective. The first contribution consists in the development of an integer linear programming model and a heuristic that makes use of this model. Sec- ond, two greedy heuristics are proposed. Finally, the last contribution is a population-based iterated greedy algorithm that takes profit from the better one of the two developed greedy heuristics. The results of the compared algorithmic approaches show that small problem instances based on random graphs are best solved by an efficient integer linear programming solver such as CPLEX. Larger problem instances are best tackled by the population-based iterated greedy algorithm. The experimental evaluation considers random graphs of different sizes, densities, and ways of generating the node and edge weights

The Weighted Independent Domination Problem: ILP Model and Algorithmic Approaches

This work deals with the so-called weighted independent domination problem, which is an $NP$-hard combinatorial optimization problem in graphs. In contrast to previous work, this paper considers the problem from a non-theoretical perspective. The first contribution consists in the development of three integer linear programming models. Second, two greedy heuristics are proposed. Finally, the last contribution is a population-based iterated greedy metaheuristic which is applied in two different ways: (1) the metaheuristic is applied directly to each problem instance, and (2) the metaheuristic is applied at each iteration of a higher-level framework---known as construct, merge, solve \& adapt---to sub-instances of the tackled problem instances. The results of the considered algorithmic approaches show that integer linear programming approaches can only compete with the developed metaheuristics in the context of graphs with up to 100 nodes. When larger graphs are concerned, the application of the populated-based iterated greedy algorithm within the higher-level framework works generally best. The experimental evaluation considers graphs of different types, sizes, densities, and ways of generating the node and edge weights

Bond graph based multiphysic modelling of anion exchange membrane water electrolysis cell

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordThis work is an attempt to develop and validate a graphical dynamical model of an AEM electrolysis cell based on Bond Graphs, an energy based tool that allows to represent multiphysics systems. The model of the cell lays a foundation for developing a complete representation for AEM electrolysers which can be used for simulation as well as for developing control algorithms and fault diagnosis. Parameter identification and model validation is achieved using experimental data.European Commissio

Construct, Merge, Solve and Adapt Applied to the Maximum Disjoint Dominating Sets Problem

We propose a “construct, merge, solve and adapt” (CMSA) approach for the maximum disjoint dominating sets problem (MDDSP), which is a complex variant of the classical minimum dominating set problem in undirected graphs. The problem requires to find as many vertex-disjoint dominating sets of a given graph as possible. CMSA is a recent metaheuristic approach based on the idea of problem instance reduction. At each iteration of the algorithm, sub-instances of the original problem instance are solved by an exact solver. These sub-instances are obtained by merging the solution components of probabilistically generated solutions. CMSA is the first metaheuristic proposed for solving the MDDSP. The obtained results show that CMSA outperforms all existing greedy heuristics