Showing papers by "Christian Blum published in 2015"

Swarm Intelligence in Optimization and Robotics

Abstract: Swarm intelligence is an artificial intelligence discipline, which was created on the basis of the laws that govern the behavior of, for example, social insects, fish schools, and flocks of birds. The organization of these animal societies has always mesmerized humans. Therefore, it is surprising that it has only been in the second half of the last century that some of the most important principles of swarm intelligent behavior have been unraveled. A prime example is stigmergy, which refers to a self-organization of the animal society via changes applied to the environment.

FrogCOL and FrogMIS: new decentralized algorithms for finding large independent sets in graphs

Abstract: Finding large (and generally maximal) independent sets of vertices in a given graph is a fundamental problem in distributed computing. Applications include, for example, facility location and backbone formation in wireless ad hoc networks. In this paper, we study a decentralized (or distributed) algorithm inspired by the calling behavior of male Japanese tree frogs, originally introduced for the graph-coloring problem, for its potential usefulness in the context of finding large independent sets. Moreover, we adapt this algorithm to directly produce maximal independent sets without the necessity of first producing a graph-coloring solution. Both algorithms are compared to a wide range of decentralized algorithms from the literature on a diverse set of benchmark instances for the maximal independent set problem. The results show that both algorithms compare very favorably to their competitors.

A matheuristic for the minimum weight rooted arborescence problem

Abstract: The combinatorial optimization problem tackled in this work is from the family of minimum weight rooted arborescence problems. The problem is NP-hard and has applications, for example, in computer vision and in multistage production planning. We describe an algorithm which makes use of a mathematical programming solver in order to find near-optimal solutions to the problem both in acyclic directed graphs and in directed graphs possibly containing directed circuits. It is shown that the proposed technique compares favorably to competiting approaches published in the related literature. Moreover, the experimental evaluation demonstrates that, although mathematical programming solvers are very powerful for this problem, with growing graph size and density they become unpractical due to excessive memory requirements.

An artificial bioindicator system for network intrusion detection

Abstract: 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.

A randomized population-based iterated greedy algorithm for the minimum weight dominating set problem

Abstract: Iterated greedy algorithms belong to the class of stochastic local search strategies that have been shown to be very successful for solving a considerable number of difficult optimization problems. They are based on the simple and effective principle of generating a sequence of solutions by iterating over a constructive greedy heuristic using destruction and construction phases. This paper presents an efficient randomized iterated greedy approach for the minimum weight dominating set problem, whose goal is to identify a subset of vertices in a vertex-weighted graph with minimum total weight such that each vertex of the graph is either in the subset or has a neighbor in the subset. Our proposed approach works on a population of solutions rather than on a single one. Moreover, it is based on a fast randomized construction procedure making use of two different greedy heuristics. The performance evaluation done on a commonly used set of benchmark instances shows that our proposed algorithm outperforms current state-of-the-art approaches both in term of solution quality and computational time.

On solving the most strings with few bad columns problem: An ILP model and heuristics

Abstract: The most strings with few bad columns problem is an NP-hard combinatorial optimization problem from the bioinformatics field. This paper presents the first integer linear programming model for this problem. Moreover, a simple greedy heuristic and a more sophisticated extension, namely a greedy-based pilot method, are proposed. Experiments show that, as expected, the greedy-based pilot method improves over the greedy strategy. For problem instances of small and medium size the best results were obtained by solving the integer linear programming model by CPLEX, while the greedy-based pilot methods scales much better to large problem instances.

Computational Performance Evaluation of Two Integer Linear Programming Models for the Minimum Common String Partition Problem

Abstract: In the minimum common string partition (MCSP) problem two related input strings are given. "Related" refers to the property that both strings consist of the same set of letters appearing the same number of times in each of the two strings. The MCSP seeks a minimum cardinality partitioning of one string into non-overlapping substrings that is also a valid partitioning for the second string. This problem has applications in bioinformatics e.g. in analyzing related DNA or protein sequences. For strings with lengths less than about 1000 letters, a previously published integer linear programming (ILP) formulation yields, when solved with a state-of-the-art solver such as CPLEX, satisfactory results. In this work, we propose a new, alternative ILP model that is compared to the former one. While a polyhedral study shows the linear programming relaxations of the two models to be equally strong, a comprehensive experimental comparison using real-world as well as artificially created benchmark instances indicates substantial computational advantages of the new formulation.

Ant Colony Optimization for the Minimum-Weight Rooted Arborescence Problem

Abstract: The minimum-weight rooted arborescence problem is an NP-hard combinatorial optimization problem which has important applications, for example, in computer vision. An example of such an application is the automated reconstruction of consistent tree structures from noisy images. In this chapter, we present an ant colony optimization approach to tackle this problem. Ant colony optimization is a metaheuristic which is inspired by the foraging behavior of ant colonies. By means of an extensive computational evaluation, we show that the proposed approach has advantages over an existing heuristic from the literature, especially for what concerns rather dense graphs.