C
Christian Blum
Researcher at Spanish National Research Council
Publications - 253
Citations - 13596
Christian Blum is an academic researcher from Spanish National Research Council. The author has contributed to research in topics: Metaheuristic & Ant colony optimization algorithms. The author has an hindex of 37, co-authored 227 publications receiving 12281 citations. Previous affiliations of Christian Blum include Ikerbasque & Polytechnic University of Catalonia.
Papers
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Journal ArticleDOI
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
Christian Blum,Andrea Roli +1 more
TL;DR: A survey of the nowadays most important metaheuristics from a conceptual point of view and introduces a framework, that is called the I&D frame, in order to put different intensification and diversification components into relation with each other.
Journal ArticleDOI
Ant colony optimization theory: a survey
Marco Dorigo,Christian Blum +1 more
TL;DR: A survey on theoretical results on ant colony optimization, which highlights some open questions with a certain interest of being solved in the near future and discusses relations between ant colonies optimization algorithms and other approximate methods for optimization.
Journal ArticleDOI
Ant colony optimization: Introduction and recent trends
TL;DR: This work deals with the biological inspiration of ant colony optimization algorithms and shows how this biological inspiration can be transfered into an algorithm for discrete optimization, and presents some of the nowadays best-performing ant colonies optimization variants.
Journal ArticleDOI
Hybrid metaheuristics in combinatorial optimization: A survey
TL;DR: A survey of some of the most important lines of hybridization of metaheuristics with other techniques for optimization, which includes, for example, the combination of exact algorithms and meta heuristics.
Book
Ant Colony Optimization and Swarm Intelligence
Marco Dorigo,Mauro Birattari,Christian Blum,Luca Maria Gambardella,Francesco Mondada,Thomas Stützle +5 more
TL;DR: This paper presents a meta-modelling framework for Swarm Intelligence and Collective Robotics that automates the very labor-intensive and therefore time-heavy and expensive process of designing and deploying swarm-bots.