Multi-task optimization(MTO) based on implicit parallelism of computational intelligence is a research hot-spot and cutting-edge technology nowadays. Compared with the traditional single-task optimization algorithm, optimizing multiple tasks concurrently by mining the internal parallelism and connotation of swarm intelligence can significantly improve the problem solving quality and shorten the task solving time. Firstly, the research trend and progress of the MTO is outlined by combing the English and Chinese related literature. Then, based on the multifactorial optimization(MFO) and multi-population evolution(MPE) information sharing frameworks, the differences and similarities between the MFO and the MPE are summarized from the aspects of multi-task search space design, population number, population size, relying algorithms, information migration nodes, transferred knowledge, time and spatial complexity, complex systems. Moreover, the core theory of MTO is expounded from the angel of information transfer nodes, modes and types. Finally, the future research direction from the aspects of exploring the hierarchical intelligent emergence of the MTO complex system, multi-task population diversity control and expanding application fields are discussed.