The minimum common string partition problem is an NP-hard combinatorial optimization problem with applications in computational biology. In this work we propose the first integer linear programming model for solving this problem. Moreover, on the basis of the integer linear programming model we develop a deterministic 2-phase heuristic which is applicable to larger problem instances. The results show that provenly optimal solutions can be obtained for problem instances of small and medium size from the literature by solving the proposed integer linear programming model with CPLEX. Furthermore, new best-known solutions are obtained for all considered problem instances from the literature. Concerning the heuristic, we were able to show that it outperforms heuristic competitors from the related literature.

This work deals with the so-called minimum capacitated dominating set (CAPMDS) problem, which is an NP-Hard combinatorial optimization problem in graphs. In this paper we describe the application of a recently introduced hybrid algorithm known as Construct, Merge, Solve & Adapt (CMSA) to this problem. Moreover, we evaluate the performance of a standalone ILP solver. The results show that both CMSA and the ILP solver outperform current stateof-the-art algorithms from the literature. Moreover, in contrast to the ILP solver, the performance of CMSA does not degrade for the largest problem instances. The experimental evaluation is based on a benchmark dataset containing two different graph topologies and considering graphs with variable and uniform node capacities.

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An artificial bioindicator system is developed in order to solve a network intrusion detection problem. The system, inspired by an ecological approach to biological immune systems, evolves a population of agents that learn to survive in their environment. An adaptation process allows the transformation of the agent population into a bioindicator that is capable of reacting to system anomalies. Two characteristics stand out in our proposal. On the one hand, it is able to discover new, previously unseen attacks, and on the other hand, contrary to most of the existing systems for network intrusion detection, it does not need any previous training. We experimentally compare our proposal with three state-of-the-art algorithms and show that it outperforms the competing approaches on widely used benchmark data.

Limited payload capacity on small unmanned aerial vehicles (UAVs) results in restricted flight time. In order to increase the operational range of UAVs, recent research has focused on the use of mobile ground charging stations. The cooperative route planning for both aerial and ground vehicles (GVs) is strongly coupled due to fuel constraints of the UAV, terrain constraints of the GV and the speed differential of the two vehicles. This problem is, in general, an NP-hard combinatorial optimization problem. Existing polynomialtime solution approaches make a trade-off in solution quality for large-scale scenarios and generate solutions with large relative gaps (up to 50 %) from known lower bounds. In this work, we employ a hybrid metaheuristic known as Construct, Merge, Solve & Adapt (CMSA) in order to develop a scalable and computationally efficient solution approach. We discuss results for large scale scenarios and provide a comparative analysis with the current state-of-the-art.

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 literature, 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. Second, 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.

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