Gene data has the characteristics of small sample, high dimensionality and high redundancy, which easily lead to "curse of dimensionality" and "over-fitting" in feature gene selection. To overcome these obstacles, a feature gene selection algorithm, named Mutual Information Maximum Value Filter Criteria-Inertia-Weight Particle Swarm Optimization (MIMVFC-IWPSO), was proposed. Firstly, interaction between genes was calculated by newly defined feature entropies of gene-category and gene-gene, and Feature Gene Candidates Subset (FGCS) was obtained by MIMVFC (Mutual Information Maximum Value Filter Criteria) which reduced the scope of classification operations and improved the probability of feature genes being covered. Secondly, the Particle Swarm Optimization (PSO) algorithm was reconstructed to IWPSO (Inertia Weight Particle Swarm Optimization) by introduction of self-adjusted inertia weight which enabled the algorithm to have strong global optimization ability in the early stage of iteration and strong local search ability in the later stage. Lastly, Core Feature Gene Subset (CFGS) was extracted from FGCS by IWPSO which was exploited in the classification of samples into tumor and normal classes. The experiments were carried out based on three public tumor gene databases. Compared with four popular filter methods, MIMVFC achieved higher correct classification rate than the methods based on Signal-to-Noise Ratio (SNR), t-statistic and Information Gain (IG), and ranked nearly the same as Chi-Square method, but the proposed method still had the optimized step to enhance the results furthermore. For the same FGCS, compared with BPSO-CGA (Binary Partical Swarm Optimization and Combat Genetic Algorithm), an algorithm with good performance, IWPSO gained a smaller CFGS with slightly increased time consumption and a higher accuracy; compared with classic PSO, IWPSO gained a smaller FGCS with less time consumption and a higher accuracy. The simulation results show that MIMVFC-IWPSO has comprehensive classification performance in both the aspects of accuracy and efficiency which proves to be feasible and effective in feature gene selection of multiple types of tumors, and it can be employed in assisting instruction in molecular biology experiment design and validation.

Resources in cloud computing environment are dynamic and heterogeneous. The goal of resource allocation in large-scale tasks is to minimize the completion time and resource occupation while having the best load balancing, which is a Non-deterministic Polynomial (NP) problem. Drawing on the advantages of intelligent swarm optimization, a hybrid swarm intelligence scheduling strategy named Dynamic Random Distribution PSO (DRDPSO) was proposed based on an improved PSO algorithm. Firstly, the inertia weight constant of PSO was modified to be a variable to control the convergence speed of solution process reasonably. Secondly, the search scope of each iteration was shrinked so as to reduce invalid search on the premise of retaining candidate optimal set. Then, selection operation was introduced to select high-quality individuals and pass them on to the next generation. Finally, random disturbance was designed to improve the diversity of candidate solutions and avoid the local optimal trap to some extent. Two kinds of simulation tests were carried out on the CloudSim platform. The experimental results show that, the proposed DRDPSO is better than Simulated Annealing Genetic Algorithm (SAGA) and Genetic Algorithm (GA)+PSO in most cases when dealing with isomorphic tasks. The total execution time of the proposed algorithm is less than SAGA by 13.7%-37.0% and less than GA+PSO by 13.6%-31.6%, the resource consumption of the proposed algorithm is less than SAGA by 9.8%-17.1% and less than GA+PSO by 0.6%-31.1%, the number of iterations of the proposed algorithm is less than SAGA by 15.7%-60.2% and less than GA+PSO by 1.4%-54.7%, the load balance degree of the proposed algorithm is less than SAGA by 8.1%-18.5% and less than GA+PSO by 2.7%-15.3% with the smallest fluctuation amplitude. When dealing with heterogeneous tasks, three algorithms has the similar properties:in aspect of the total execution time consumption, CPU tasks are the most, the mixed tasks take the second place, and IO tasks are the least. The comprehensive performance of DRDPSO is the best, which is the most suitable for dealing with multiple types of heterogeneous tasks. GA+PSO algorithm is suitable for solving hybrid tasks and SAGA algorithm is suitable for solving IO tasks quickly. When dealing with large-scale isomorphic and heterogeneous tasks, the proposed DRDPSO can significantly shorten the total task execution time and improve the utilization of resources in varying degrees with proper load balancing of computing nodes.