Research

Foundation, design and analysis of algorithms for multi-objective optimization

General-purpose exact methods, stochastic local search (meta)heuristics, and their interactions

Decomposition-, dominance-, indicator- and set-based search paradigms and their design principles

Hybrid, cooperative, adaptive and distributed approaches

Analytics-driven and model-assisted autonomous search for cross-domain and any-objective optimization

Experimental analysis and fundamental understanding of optimization algorithms, benchmarking and performance assessment, statistical and machine learning data analysis

Fitness landscape analysis, problem knowledge, structural properties of the search space

Feature-based performance prediction, algorithm selection, configuration and adaptation, algorithm portfolio

Large-scale and expensive NP-hard problems from combinatorial optimization, including knapsack, assignment, routing and scheduling

ParadisEO: a software framework for the design, implementation and analysis of metaheuristics

• Member of the ParadisEO development team

• Main responsible of the ParadisEO-MOEO module for multi-objective optimization

• Active contributor to the ParadisEO-MO module for local search metaheuristics

MOSAL: multi-objective sequence alignment tools

MoCObench: benchmark instances for multi-objective combinatorial optimization

MOEA/D: repository of the state-of-the-art developments on MOEA/D and decomposition-based EMO