Multi Task Optimization

Multi-task optimization (MTO) aims to simultaneously solve multiple related tasks, leveraging shared knowledge to improve efficiency and performance compared to solving each task independently. Current research focuses on developing algorithms that effectively handle task conflicts and imbalances, exploring techniques like adaptive weighting of task losses, evolutionary multitasking, and multi-objective optimization methods to find Pareto optimal solutions. These advancements are impacting diverse fields, improving performance in applications ranging from time series forecasting and power system control to image classification and natural language processing. The ongoing debate centers on whether sophisticated MTO algorithms offer significant advantages over simpler, weighted-average approaches.