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

Software, benchmark and resources
  • MOEA/D: repository of the state-of-the-art developments on MOEA/D and decomposition-based EMO
  • 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 [ details ]
    • Active contributor to the ParadisEO-MO module for local search metaheuristics
  • VRP-solve: a metaheuristic-based solver for vehicle routing
  • MOSAL: multi-objective sequence alignment tools
  • MoCObench: benchmark instances for multi-objective combinatorial optimization
  • Benchmark instances for multi-objective flowshop scheduling