Research
Research Interests
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
Resources, Software, and Benchmark
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