Aiming at the problems of poor accuracy, easy to fall into local maximum and slow convergence in existing medical image registration methods, based on multi-resolution analysis, a hybrid algorithm of Modified Brain Storm Optimization (MBSO) and Powell algorithm was proposed. MBSO algorithm, the proportion of individuals participating in local and global search was adjusted by changing the way of individual generation, and variable step size was adopted to enhance search ability, to achieve the purpose of accelerating convergence and jumping out of local optimum. Firstly, the MBSO algorithm was used to search globally in the low resolution layer. Then the result was used as the start point of Powell algorithm to search in the high resolution layer. Finally, Powell algorithm was used to search and locate the globally optimal value in the original image layer. Compared with the Particle Swarm Optimization (PSO) algorithm, Ant Colony Optimization (ACO) algorithm, Genetic Algorithm (GA) combined with Powell algorithm, the average root mean square error of the proposed algorithm decreased by 20.89%, 30.46% and 18.54%, and the average registration time reduced by 17.86%, 27.05% and 26.60% with success rate of 100%. The experimental results show that the proposed algorithm has good robustness and can accomplish the medical image registration task quickly and accurately.

Aiming at the problems of the odor source localization algorithms by using multi-robot in indoor turbulent environment, such as the low utilization rate of historical concentration information and the lack of mechanism to adjust the global and local search, a multi-robot cooperative search algorithm combing Brain Storm Optimization (BSO) algorithm and upwind search was proposed. Firstly, the searched location of robot was initialized as an individual and the robot position was taken as the center for clustering, which effectively used the guiding role of historical information. Secondly, the upwind search was defined as an individual mutation operation to dynamically adjust the number of new individuals generated by the fusion of selected individuals in a class or two classes, which effectively adjusted the global and local search methods. Finally, the odor source was confirmed according to the two indexes of concentration and persistence. In the simulation experiments under two environments with and without obstacles, the proposed algorithm was compared with three kinds of swarm intelligent multi-robot odor source localization algorithms. The experimental results show that, the average search time of the proposed algorithm is reduced by more than 33% and the location accuracy is 100%. The proposed algorithm can effectively adjust the global and local search relations of robot, and locate the odor source quickly and accurately.