Optimization and Learning Laboratory (OPAL Lab)

Research Center Description

OPAL Research Center will focus its research on optimization and learning, which is one of the core components of artificial intelligence.  Optimization and learning are mutually complementary and solve synergistically the same kind of problems, although with different emphases. Both are essential for the study of artificial intelligence.  In some sense, optimization lays particular emphasis on the final outcome of its problem solving while learning concerns more with the process of problem solving. Optimization is used in all kinds of learning algorithms, while learning facilitates optimization. The research areas of the OPAL Lab include, but are not limited to, the following:

Research Projects and Results

  1. 1.Optimization Techniques - Evolutionary Computation

  2. 2.Optimization Techniques - Swarm Intelligence

  3. 3.Optimization Techniques - Nature Inspired Computation

  4. 4.Learning Techniques - Deep Learning, Reinforcement Learning, Online and Incremental Learning, Supervised Learning, Unsupervised Learning, Semi-supervised Learning, Class Imbalance Learning

  5. 5.Optimization Problem Areas - Single Objective Optimization Problems, Multi-Objective Optimization Problems, Many-Objective Optimization Problems, Discrete Optimization Problems, Combinatorial Optimization Problems, Constrained Optimization Problems, Dynamic Optimization Problems, Uncertain Optimization Problems

  6. 6.Application Areas - Intelligent Logistics and Supply Chain Management

  7. 7.Application Areas - Swarm Robotics

  8. 8.Application Areas - Big Data Analytics

  9. 9.Application Areas - Smart Grid

  10. 10.Application Areas - Intelligent Transportation