Recently, machine learning-based image compression attracts increasing interests and is approaching the state-of-the-art ratio. But unlike traditional codec, it lacks a universal optimization method to seek efficient representation for different images. In this paper, we develop plug-and-play framework seeking higher ratio, which can be flexibly applied existing potential future networks. To ma...

Remote sensing image registration is valuable for image-based navigation system despite posing many challenges. As the search space of usually nonconvex, optimization algorithm, which aims to find optimal parameters in space, a challenging step. Conventional algorithms can hardly reconcile contradiction rapid convergence and global optimization. In this letter, novel learning-based algorithm na...

With the development of computer applications in ship design, optimization, as a powerful approach, has been widely used in the design and analysis process. However, the running time, which often varies from several weeks to months in the current computing environment, has been a bottleneck problem for optimization applications, particularly in the structural design of ships. To speed up the op...

In this paper, a new algorithm which is the result of the combination of cellular learning automata and frog leap algorithm (SFLA) is proposed for optimization in continuous, static environments.At the proposed algorithm, each memeplex of frogs is placed in a cell of cellular learning automata. Learning automata in each cell acts as the brain of memeplex, and will determine the strategy of moti...

Bus timetable optimization is a key issue to reduce operational cost of bus company and improve the transit service quality. Existing methods optimize offline. However, in practice, short-term passenger flow may change dramatically from time time. Timetables generated offline cannot be adjusted real handle changed flow. In this paper, we propose Deep Reinforcement Learning based Timetable dynam...