Flexible Job-shop Scheduling Problem (FJSP) is a widely applied combinatorial optimization problem. Aiming at the problems of multi-objective FJSP that the solution process is complex and the algorithm is easy to fall into the local optimum, a Hybrid Particle Swarm Optimization algorithm with Multi-Region Sampling strategy (HPSO-MRS) was proposed to optimize both the makespan and total machine delay time. The multi-region sampling strategy was able to reorganize the positions of the Pareto frontiers that the particles belonging to, and guide the corresponding moving directions for the particles in multiple regions of the Pareto frontiers after sampling. Thus, the convergence ability of particles in multiple directions was adjusted, and the ability of uniform distribution was improved to a certain extent. In addition, in the encoding and decoding aspect, the decoding strategy with interpolation mechanism was used to eliminate the potential local left shift; in the particle updating aspect, the particle update method of traditional Particle Swarm Optimization (PSO) algorithm was combined with the crossover and mutation operators of Genetic Algorithm (GA), which improved the diversity of search process and avoid the algorithm from falling into the local optimum. The proposed algorithm was tested on benchmark problems Mk01-Mk10 and compared with Hybrid Particle Swarm Optimization algorithm (HPSO), Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-Ⅱ), Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) on algorithm effectiveness and operation efficiency. Experimental results of significance analysis showed that, HPSO-MRS was significantly better than the comparison algorithms on the convergence evaluation indexes Hyper Volume (HV) and Inverted Generational Distance (IGD) in 85% and 77.5% of the control groups, respectively. In 35% of the control groups, the distribution index Spacing of the algorithm was significantly better than those of the comparison algorithms. And there was no situation that the proposed algorithm was significantly worse than the comparison algorithms on the three indexes. It can be seen that, compared with the others, the proposed algorithm has better convergence and distribution performance.

To solve the problem that classical Mutual Information (MI) image registration may lead to local extremum, a registration method for multispectral magnetic resonance images based on Cross Cumulative Residual Entropy (CCRE) was proposed. Firstly, the gray level of reference and floating images were compressed into 5 and 7 bits. Then the Hanning windowed Sinc interpolation was used to calculate the CCRE of 5-bit grayscale images, and the Brent algorithm was used to search the CCRE to get the initial transformation parameters of pre-registration. Finally, the Partial Volume (PV) interpolation was adopted to calculate the CCRE of 7-bit grayscale images, and the Powell algorithm was applied to optimize the CCRE to get final parameters from the pre-registration parameters. The experimental results show that the robustness of the proposed method is improved compared with the CCRE registration of PV interpolation, while the registration time is saved about 90% and accuracy is improved compared with the CCRE of Hanning windowed Sinc interpolation. The presented method ensures robustness, efficiency and accuracy, so it is suitable for multi-spectral image registration.