M
Martin Middendorf
Researcher at Leipzig University
Publications - 238
Citations - 11682
Martin Middendorf is an academic researcher from Leipzig University. The author has contributed to research in topics: Ant colony optimization algorithms & Metaheuristic. The author has an hindex of 44, co-authored 233 publications receiving 9974 citations. Previous affiliations of Martin Middendorf include The Catholic University of America & Catholic University of Eichstätt-Ingolstadt.
Papers
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Journal ArticleDOI
MITOS: Improved de novo metazoan mitochondrial genome annotation
Matthias Bernt,Alexander Donath,Frank Jühling,Frank Jühling,Fabian Externbrink,Catherine Florentz,Guido Fritzsch,Joern Pütz,Martin Middendorf,Peter F. Stadler +9 more
TL;DR: The MITOS pipeline is designed to compute a consistent de novo annotation of the mitogenomic sequences and it is shown that the results of MITOS match RefSeq and MitoZoa in terms of annotation coverage and quality.
Proceedings Article
Ant colony optimization for resource-constrained project scheduling
TL;DR: An ant colony optimization approach (ACO) for the resource-constrained project scheduling problem (RCPSP) is presented and Combinations of two pheromone evaluation methods are used by the ants to find new solutions.
Journal ArticleDOI
Ant colony optimization for resource-constrained project scheduling
TL;DR: An ant colony optimization (ACO) approach for the resource-constrained project scheduling problem (RCPSP) is presented in this paper, where several new features that are interesting for ACO are proposed and evaluated.
Journal ArticleDOI
A hierarchical particle swarm optimizer and its adaptive variant
Stefan Janson,Martin Middendorf +1 more
TL;DR: A hierarchical version of the particle swarm optimization (PSO) metaheuristic, in which the shape of the hierarchy is dynamically adapted during the execution of the algorithm, is introduced.
Book ChapterDOI
Bi-Criterion Optimization with Multi Colony Ant Algorithms
TL;DR: This paper introduces two methods for co-operation between the colonies and compares them with a multistart ant algorithm that corresponds to the case of no cooperation.