A Fast Clustering Based Evolutionary Algorithm for Super-Large-Scale Sparse Multi-Objective Optimization

During the last three decades,evolutionary algorithms(EAs)have shown superiority in solving complex optimization problems,especially those with multiple objectives and non-differentiable landscapes.However,due to the stochastic search strategies,the performance of most EAs deteriorates drastically when handling a large number of decision variables.To tackle the curse of dimensionality,this work proposes an efficient EA for solving super-large-scale multi-objective optimization problems with sparse optimal solutions.The proposed algorithm estimates the sparse distribution of optimal solutions by optimizing a binary vector for each solution,and provides a fast clustering method to highly reduce the dimensionality of the search space.More importantly,all the operations related to the decision variables only contain several matrix calculations,which can be directly accelerated by GPUs.While existing EAs are capable of handling fewer than 10000 real variables,the proposed algorithm is verified to be effective in handling 1000000 real variables.Furthermore,since the proposed algorithm handles the large number of variables via accelerated matrix calculations,its runtime can be reduced to less than 10%of the runtime of existing EAs.

Effective approach for outdoor obstacle detection by clustering LIDAR data context

A method of environment mapping using laser-based light detection and ranging （LIDAR） is proposed in this paper. This method not only has a good detection performance in a wide range of detection angles, but also facilitates the detection of dynamic and hollowed-out obstacles. Essentially using this method, an improved clustering algorithm based on fast search and discovery of density peaks （CBFD） is presented to extract various obstacles in the environment map. By comparing with other cluster algorithms, CBFD can obtain a favorable number of clusterings automatically. Furthermore, the experiments show that CBFD is better and more robust in functionality and performance than the K-means and iterative self-organizing data analysis techniques algorithm （ISODATA）.

Fast time-division color electroholography using a multiple-graphics processing unit cluster system with a single spatial light modulator

We demonstrate fast time-division color etectroholography using a multiple-graphics-processing-unit （GPU） cluster system with a spatial light modulator and a controller to switch the color of the reconstructing light. The controller comprises a universal serial bus module to drive the liquid crystal optical shutters. By using the controller, the computer-generated hologram （CGH） display node of the multiple-GPU cluster system synchronizes the display of the CGH with the color switching of the reconstructing light. Fast time-division color electroholography at 20 fps is realized for a three-dimensional object comprising 21,000 points per color when 13 GPUs are used in a multiple-GPU cluster system.

Fluidization science,its development and future

By revisiting the three stage theory for the progress of science proposed by Taketani in 1942, the footmarks of fluidization research are examined. The bubbling and fast fluidization issues were emphasized so that the future offluidization research can be discussed among scientists and engineers in a wider perspective. The first cycle of fluidization research was started in the early 1940s by an initial stage of phenomenology. The second stage of structural studies was kicked off in the early 1950s with the introduction of the two phase theory. The third stage of essential studies occurred in the early 1960s in the form of bubble hydrodynamics. The second cycle, which confirmed the aforementioned three stages closed at the turn of the century, established a general understanding of suspension structures including agglomerating fluidization, bubbling, turbulent and fast fluidizations and pneumatic transport; also established powerful measurement and numerical simulation tools.After a general remark on science, technology and society issues the interactions between fluidization technology and science are revisited. Our future directions are discussed including the tasks in the third cycle, particularly in its phenomenology stage where strong motivation and intention are always necessary, in relation also to the green reforming of the present technology. A generalized definition of ＇fluidization＇ is proposed to extend fluidization principle into much wider scientific fields, which would be effective also for wider collaborations.