Surrogate-Assisted Particle Swarm Optimization Algorithm With Pareto Active Learning for Expensive Multi-Objective Optimization

For multi-objective optimization problems, particle swarm optimization(PSO) algorithm generally needs a large number of fitness evaluations to obtain the Pareto optimal solutions. However, it will become substantially time-consuming when handling computationally expensive fitness functions. In order to save the computational cost, a surrogate-assisted PSO with Pareto active learning is proposed. In real physical space(the objective functions are computationally expensive), PSO is used as an optimizer, and its optimization results are used to construct the surrogate models. In virtual space, objective functions are replaced by the cheaper surrogate models, PSO is viewed as a sampler to produce the candidate solutions. To enhance the quality of candidate solutions, a hybrid mutation sampling method based on the simulated evolution is proposed, which combines the advantage of fast convergence of PSO and implements mutation to increase diversity. Furthermore, ε-Pareto active learning(ε-PAL)method is employed to pre-select candidate solutions to guide PSO in the real physical space. However, little work has considered the method of determining parameter ε. Therefore, a greedy search method is presented to determine the value ofεwhere the number of active sampling is employed as the evaluation criteria of classification cost. Experimental studies involving application on a number of benchmark test problems and parameter determination for multi-input multi-output least squares support vector machines(MLSSVM) are given, in which the results demonstrate promising performance of the proposed algorithm compared with other representative multi-objective particle swarm optimization(MOPSO) algorithms.

Variational Inference Based Kernel Dynamic Bayesian Networks for Construction of Prediction Intervals for Industrial Time Series With Incomplete Input

Prediction intervals(PIs)for industrial time series can provide useful guidance for workers.Given that the failure of industrial sensors may cause the missing point in inputs,the existing kernel dynamic Bayesian networks(KDBN),serving as an effective method for PIs construction,suffer from high computational load using the stochastic algorithm for inference.This study proposes a variational inference method for the KDBN for the purpose of fast inference,which avoids the timeconsuming stochastic sampling.The proposed algorithm contains two stages.The first stage involves the inference of the missing inputs by using a local linearization based variational inference,and based on the computed posterior distributions over the missing inputs the second stage sees a Gaussian approximation for probability over the nodes in future time slices.To verify the effectiveness of the proposed method,a synthetic dataset and a practical dataset of generation flow of blast furnace gas(BFG)are employed with different ratios of missing inputs.The experimental results indicate that the proposed method can provide reliable PIs for the generation flow of BFG and it exhibits shorter computing time than the stochastic based one.

simple and effective aerosol pathogen disinfection test for a flowing air disinfector

Aerosol transmission is an important disease transmission route and has been especially pertinent to hospital and biosafety laboratories during the SARS-CoV-2 pandemic.The thermal resistance of airborne SARS-CoV-2 is lower than that of Bacillus subtilis spores,which are often used to test the effectiveness of SARS-CoV-2 and other pathogen disinfection methods.Herein,we propose a new method to test the disinfection ability of a flowing air disinfector(a digital electromagnetic induction air heater)using B.subtilis spores.The study provides an alternative air disinfection test method.The new test system combined an aerosol generator and a respiratory filter designed in-house and could effectively recover spores on the filter membrane at the air outlet after passing through the flowing air disinfector.The total number of bacterial spores used in the test was within the range of 5×10^(5)–5×10^(6)colony-forming units(CFUs)specified in the technical standard for disinfection.The calculation was based on the calculation method in Air Disinfection Effect Appraisal Test in Technical Standard for Disinfection(2002 Edition).At an air speed of 3.5 m/s,we used a digital electromagnetic induction air heater to disinfect flowing air containing 4.100×10^(6)CFUs of B.subtilis spores and determined that the minimum disinfection temperature was 350℃for a killing rate of 99.99%.At 400℃,additional experiments using higher spore concentrations(4.700×10^(6)±1.871×10^(5)CFU)and a higher airspeed(4 m/s)showed that the killing rate remained>99.99%.B.subtilis spores,as a biological indicator for testing the efficiency of dry-heat sterilization,were killed by the high temperatures used in this system.The proposed method used to test the flowing air disinfector is simple,stable,and effective.This study provides a reference for the development of test systems that can assess the disinfection ability of flowing air disinfectors.

A tandem asymmetric oxidation-oxa-Michael sequence for dearomatization ofβ-naphthols

A catalytic asymmetric hydroxylative dearomatization reaction has been disclosed,and the products can smoothly transform into spiroannulation adducts by simply treated with a base under mild conditions.Novel in-situ generated magnesium catalytic methods are developed by application of combinational ligands.Related concise transformaitons of the spiroannulation adducts have been carried out.