Technical program

Invited and Tutorials


Venue, Hotels and Social

Paper Submission

Special Sessions

Program Committee

LION 9 Call for Papers: Special Sessions

Special sessions are organized as part of LION9 as a way to focus submissions and encourage more interaction between specific communities. In general, submission and publication rules are the same as for the general call for papers, with the organizers of the special sessions coordinating and helping in identifying competent reviewers.

Special session on Dynamic Optimization (LION-DO)


  • Patrick Siarry, University of Paris-Est Créteil, France
  • Raymond Chiong, University of Newcastle, Australia

In recent years, dynamic optimization has attracted much interest due to its practical relevance. Indeed, many real-world optimization problems are dynamic in nature, i.e. their objective function changes over time. Typical examples include resource allocation, dynamic vehicle routing, scheduling, and object tracking. In other cases, the objective function is uncertain or noisy as a result of simulation/measurement errors or approximation errors.
In addition, the design variables or environmental conditions may also be perturbed or changed over time. The objective of an efficient dynamic metaheuristic algorithm is to locate the global optimum solution, to continuously track the optimum in dynamic environments, and/or to find a robust solution that operates optimally in the presence of uncertainties.

This special session aims at bringing academic researchers and practitioners together to review the latest advances and explore future directions in this field. Topics of interest include, but are not limited, to:

  • Benchmark problems and performance measures
  • Tracking moving optima
  • Dynamic multiobjective optimization
  • Adaptation, learning, and anticipation
  • Handling noisy fitness functions
  • Using fitness approximations
  • Searching for robust optimal solutions
  • Comparative studies
  • Hybrid approaches
  • Theoretical analysis
  • Real-world applications

Papers on all topics related to the session's theme are solicited.
Prospective authors should submit their papers via the online submission system of LION9. Authors are advised to pay careful attention when selecting a paper category.

In addition, an e-mail message including the paper Id, title of paper, author names, and abstract must be sent to: siarry[[at]]u-pec.fr and raymond.chiong[[at]]newcastle.edu.au and all accepted novel and unpublished papers will be published in the post-conference proceedings of LION9.

Special session on Multiobjective Combinatorial Optimization (LION MoCO)


  • Hernan Aguirre - Shinshu University, Japan - ahernan@shinshu-u.ac.jp
  • Kiyoshi Tanaka - Shinshu University, Japan - ktanaka@shinshu-u.ac.jp
  • Arnaud Liefooghe - Université Lille 1, France - arnaud.liefooghe@univ-lille1.fr
  • Sébastien Verel - Université du Littoral Côte d'Opale, France – verel@lisic.univ-littoral.fr

Evolutionary algorithms and other classes of metaheuristics are often used to solve difficult problems arising in multiobjective combinatorial optimization. Such randomized search heuristics include evolutionary algorithms, neighborhood-based search, simulated annealing, tabu search, iterated local search, memetic algorithms, hyperheuristics, etc. Successful applications of metaheuristics for multiobjective combinatorial optimization can be found in fields like scheduling, timetabling, planning, network design, transportation and distribution problems, vehicle routing, traveling salesman, packing, power systems, image processing, and many others.
This special session aims at bringing together researchers working on the design, implementation, theoretical and experimental analysis of metaheuristics for multiobjective combinatorial optimization.
Topics of interest include, but are not limited to:

  • Algorithm performance
  • Algorithm behavior analysis
  • Scalability in the search space (large-scale optimization) and in the objective space (many-objective optimization)
  • Multiobjective problem structure analysis
  • Search space analysis, fitness landscapes
  • Theoretical developments
  • Classification of multiobjective problem structure / algorithm performance
  • Automated tuning and control of parameters, hyperheuristics
  • Neighborhood structures and efficient algorithms for searching them
  • Variation operators for evolutionary and other stochastic search methods
  • Parallel and distributed algorithms
  • Constraint-handling approaches
  • Comparisons between different (also exact) techniques
  • Applications of metaheuristics to multiobjective combinatorial
  • optimization

Special session on Optimisation and learning for smart cities and grids (LION-SMART)


  • El-Ghazali Talbi - INRIA Lille nord Europe/University of Lille 1

Special session on Intelligent optimization in Bioinformatics, Biomedecine and Neuroscience (LION-BIO)


  • Clarisse Dhaenens, University of Lille 1, INRIA Lille
  • Laetitia Jourdan, University of Lille 1, INRIA Lille

Bioinformatics, Biomedecine and Neuroscience represent a great challenge for optimization methods as many problems arizing in these fields can be modelized as large size optimization problems. For example, many bioinformatics problems deal with the manipulation of large sets of variables (SNPs, genes, GWA, proteins ...). Hence, looking for a good combination of these variables require advance search mechanisms. In biomedecine (or medical biology), such optimization problems may also be found by studying molecular interactions. Solving such difficult combinatorial optimization problems require to incorporate knowledge about problems to be solved.

This special session aims at putting together works in which optimization approaches and knowledge discovery are jointly concerned to solve problems coming from bioinformatics, biomedecine and neuroscience.

Topics of interest include, but are not limited to:
Original modeling and solving Bioinformatics, Biomedecine and Neuroscience optimization problems (for example: Folding, docking, protein interaction, network inference etc.) Metaheuristics to solve knowledge discovery problems encountered in problems from these fields, such as classification, clustering, association rules, feature selection... Knowledge discovery approaches embedded in metaheuristics to incorporate knowledge about the problem to be solved