Data Analyses and Parallel Optimization of the Regional Marine Ecological Model

Under the joint influence of high-intensity human activities and cli-mate change,the coastal ecological environment is deteriorating,and the ecologi-cal environment security and the sustainable development of the marine economy are seriously threatened.Therefore,it is of great significance to establish a high-resolution ecological environment operational forecasting system.To meet the run time requirements of the ecological operational forecasting system,a vari-ety of parallel optimization methods were proposed to improve the operation efficiency of the model.First,based on the National Marine Environmental Fore-casting Center’s Lenovo cluster,the ROMS benchmark experiment was expanded to the 4000 Processes scale.A good speedup was obtained by the experiment.The ROMS model was analysed with strong scalability.Second,in the hydrodynamic-ecological simulation experiment of the Bohai Sea-Yellow Sea-East China Sea,by optimizing Vector,InfiniBand,and Parallel I/O,the performance of the model can be improved by 270%while maintaining the same computing resources.That computing resources were more reasonably used lay the foundation for the operational forecast.

Research on Grid Planning of Dual Power Distribution Network Based on Parallel Ant Colony Optimization Algorithm

A distribution network plays an extremely important role in the safe and efficient operation of a power grid.As the core part of a power grid’s operation,a distribution network will have a significant impact on the safety and reliability of residential electricity consumption.it is necessary to actively plan and modify the distribution network’s structure in the power grid,improve the quality of the distribution network,and optimize the planning of the distribution network,so that the network can be fully utilized to meet the needs of electricity consumption.In this paper,a distribution network grid planning algorithm based on the reliability of electricity consumption was completed using ant colony algorithm.For the distribution network structure planning of dual power sources,the parallel ant colony algorithm was used to prove that the premise of parallelism is the interactive process of ant colonies,and the dual power distribution network structure model is established based on the principle of the lowest cost.The artificial ants in the algorithm were compared with real ants in nature,and the basic steps and working principle of the ant colony optimization algorithm was studied with the help of the travelling salesman problem(TSP).Then,the limitations of the ant colony algorithm were analyzed,and an improvement strategy was proposed by using python for digital simulation.The results demonstrated the reliability of model-building and algorithm improvement.

Parallel discrete lion swarm optimization algorithm for solving traveling salesman problem

As a typical representative of the NP-complete problem, the traveling salesman problem(TSP) is widely utilized in computer networks, logistics distribution, and other fields. In this paper, a discrete lion swarm optimization(DLSO) algorithm is proposed to solve the TSP. Firstly, we introduce discrete coding and order crossover operators in DLSO. Secondly, we use the complete 2-opt(C2-opt) algorithm to enhance the local search ability.Then in order to enhance the efficiency of the algorithm, a parallel discrete lion swarm optimization(PDLSO) algorithm is proposed.The PDLSO has multiple populations, and each sub-population independently runs the DLSO algorithm in parallel. We use the ring topology to transfer information between sub-populations. Experiments on some benchmarks TSP problems show that the DLSO algorithm has a better accuracy than other algorithms, and the PDLSO algorithm can effectively shorten the running time.

Reconstruction method of irregular seismic data with adaptive thresholds based on different sparse transform bases

Oil and gas seismic exploration have to adopt irregular seismic acquisition due to the increasingly complex exploration conditions to adapt to complex geological conditions and environments.However,the irregular seismic acquisition is accompanied by the lack of acquisition data,which requires high-precision regularization.The sparse signal feature in the transform domain in compressed sensing theory is used in this paper to recover the missing signal,involving sparse transform base optimization and threshold modeling.First,this paper analyzes and compares the effects of six sparse transformation bases on the reconstruction accuracy and efficiency of irregular seismic data and establishes the quantitative relationship between sparse transformation and reconstruction accuracy and efficiency.Second,an adaptive threshold modeling method based on sparse coefficient is provided to improve the reconstruction accuracy.Test results show that the method has good adaptability to different seismic data and sparse transform bases.The f-x domain reconstruction method of effective frequency samples is studied to address the problem of low computational efficiency.The parallel computing strategy of curvelet transform combined with OpenMP is further proposed,which substantially improves the computational efficiency under the premise of ensuring the reconstruction accuracy.Finally,the actual acquisition data are used to verify the proposed method.The results indicate that the proposed method strategy can solve the regularization problem of irregular seismic data in production and improve the imaging quality of the target layer economically and efficiently.

The uncoupled microbial fed-batch fermentation optimization based on state-dependent switched system

In the actual microbial fermentation process,excessive or insufficient substrate can produce inhibitory effects on cells growth.The artificial substrate feeding rules by past experiences have great blindness to keep substrate concentration in a given appropriate range.This paper considers that alkali feed depends on pH value of the solution and glycerol feed depends on glycerol concentration of the solution in the uncoupled microbial fed-hatch fermentation process,and establishes a state-dependent switched system in which the flow rates of glycerol and alkali,the number of mode switches,the mode sequence and the switching times are prior unknown.To maximize the yield of target product 1,3-Propanediol(1,3-PD),we formulate a switching optimal control problem with the flow rates of glycerol and alkali,the number of mode switches,the mode sequence and the switching times as decision variables,which is a mixed-integer dynamic programming problem.For solving the mixed-integer dynamic programming problem,the control parametrization technique,the time scaling transformation and the embedded system technology are used to obtain an approximate parameter optimization problem.By using a parallel optimization algorithm,we obtain the optimal control strategies.Under the obtained optimal control strategies,the 1,3-PD yield at the terminal time is increased significantly compared with the previous results.