📐 Optimization
Research on scheduling, combinatorial and multi-objective optimization, fitness landscape analysis, and energy system optimization.
Optimization research focuses on developing methods for solving difficult combinatorial and continuous optimization problems, with particular emphasis on the flexible job-shop scheduling problem, multi-objective optimization, and the analysis of fitness landscapes. A common thread is the use of machine learning—especially reinforcement learning and deep learning—within optimization frameworks.
Scheduling & Combinatorial Optimization
Flexible Job-Shop Scheduling with Deep Reinforcement Learning
An extensive research program on solving the flexible job-shop scheduling problem (FJSP) using deep reinforcement learning. Our work addresses the problem from multiple angles:
- Graph representation: Novel graph representation enabling diverse policy generation via deep RL.
- Real-time solving: Constraint programming integrated with deep learning for dynamic scheduling.
- Offline RL: Learning scheduling policies from historical data without online environment interaction.
- Self-evaluation: Methods for evaluating scheduling decisions without ground truth.
Behavioral Cloning for Scheduling
The Multi-Assignment Scheduler: a new behavioral cloning method for the job-shop scheduling problem. Learning scheduling heuristics from expert demonstrations using imitation learning.
Routing and Branching Strategies in 3D Manufacturing
Application of operations research and mathematical programming to routing problems in 3D design for manufacturing. Branching strategies for efficient exploration of the solution space.
Multi-Objective Optimization
Bi-Objective Combinatorial Optimization
Research on bi-objective combinatorial optimization problems with heterogeneous objectives (e.g., one continuous and one discrete objective). Development of algorithms for Pareto front approximation in such settings.
Benchmarking MOEAs for RL Problems
Systematic comparison of multi-objective evolutionary algorithms (MOEAs) for solving continuous multi-objective reinforcement learning problems. Analysis of NSGA-II, MOEA/D, and other state-of-the-art algorithms on RL benchmark problems.
Fitness Landscape Analysis
Analysis of Problem Structure
Research on characterizing the structure of optimization problems through fitness landscape analysis. Understanding why certain problems are hard for particular algorithms, and how landscape features relate to algorithm performance.
NK Landscapes & Problem Classes
Research on NK landscapes and related benchmark problem classes for understanding the difficulty of combinatorial optimization problems. Analysis of how problem structure affects the behavior of evolutionary algorithms.
Energy Systems Optimization
Geothermal Power Plant Optimization
Application of continuous estimation of distribution algorithms for parametric optimization of geothermal power plants. Optimizing operating parameters to maximize efficiency and reduce environmental impact.
Smart Grid Optimization
AI methods for smart electricity distribution grids: meter-transformer connectivity identification, voltage control, and anomaly detection in low-voltage grids.
Building Energy Management
Intelligent methodology for energy management in buildings and anomaly detection. Prediction of building energy consumption using evolved neural networks and other machine learning approaches.
Selected Publications
- Santana R, Mendiburu A and Lozano JA (2017). A comparison of probabilistic-based optimization approaches for vehicle routing problems. GECCO 2017.
- Santana R, Larrañaga P and Lozano JA (2014). A probabilistic evolutionary optimization approach to compute quasiparticle braids. GECCO 2014.
- Ceberio J, Santana R, Mendiburu A and Lozano JA (2015). Mixtures of Generalized Mallows models for solving the quadratic assignment problem. CEC 2015.
- González-Arenas Z, Jiménez-Sobrino JC, Lozada-Chang L and Santana R (2008). Parameter estimation of diffusion processes using EDAs. CEC 2008.
- Santana R, Mendiburu A and Lozano JA (2016). Evolutionary Approaches to Optimization Problems in Chimera Topologies. GECCO 2016.
- Khargharia HS, Shakya S, Santana R, Oliveira M and Mooney P (2020). Automated hyper-heuristic design using EDA and domain knowledge for nurse scheduling problems. CEC 2020.
- Santana R and Shakya S (2020). Dynamic programming operators for bi-objective TTP problem. GECCO 2020.
- Arenas ZG, Jimenez JC, Lozada-Chang L-V and Santana R (2021). Estimation of distribution algorithms for the computation of innovation estimators of diffusion processes. Mathematics and Computers in Simulation.
- Murua M, Galar D and Santana R (2022). Solving the multi-objective Hamiltonian cycle problem using a Branch-and-Fix based algorithm. Journal of Computational Science.
- Irurozki E, Ceberio J, Santamaria J, Santana R and Mendiburu A (2018). Algorithm 989: perm_mateda: A Matlab Toolbox of Estimation of Distribution Algorithms for Permutation Problems. ACM TOMS.
- Ponce M and Santana R (1997). A hybrid genetic algorithm for a Hamiltonian path problem. CIMAF 1997.
- Ponce-de-Leon M, Ponce M and Santana R (1996). A genetic algorithm for a Hamiltonian path problem. GECCO 1996.