Co Optimization

Co-optimization involves simultaneously optimizing multiple interdependent design parameters to achieve superior overall performance, addressing limitations of sequential optimization approaches. Current research focuses on applying this principle to diverse fields, including robotics (optimizing robot morphology and control using reinforcement learning and evolutionary algorithms), energy systems (integrating renewable sources via reinforcement learning), and hardware design (co-optimizing neural networks and accelerators for power efficiency). These advancements offer significant improvements in system efficiency, performance, and design automation across various engineering disciplines.