A Hybrid Particle Swarm Optimization to Forecast Implied Volatility Risk

The application of optimization methods to prediction issues is a continually exploring field.In line with this,this paper investigates the connectedness between the infected cases of COVID-19 and US fear index from a forecasting perspective.The complex characteristics of implied volatility risk index such as non-linearity structure,time-varying and nonstationarity motivate us to apply a nonlinear polynomial Hammerstein model with known structure and unknown parameters.We use the Hybrid Particle Swarm Optimization(HPSO)tool to identify the model parameters of nonlinear polynomial Hammerstein model.Findings indicate that,following a nonlinear polynomial behaviour cascaded to an autoregressive with exogenous input(ARX)behaviour,the fear index in US financial market is significantly affected by COVID-19-infected cases in the US,COVID-19-infected cases in the world and COVID-19-infected cases in China,respectively.Statistical performance indicators provided by the developed models show that COVID-19-infected cases in the US are particularly powerful in predicting the Cboe volatility index compared to COVID-19-infected cases in the world and China(MAPE(2.1013%);R2(91.78%)and RMSE(0.6363 percentage points)).The proposed approaches have also shown good convergence characteristics and accurate fits of the data.

Optimization of Thermal Aware VLSI Non-Slicing Floorplanning Using Hybrid Particle Swarm Optimization Algorithm-Harmony Search Algorithm

Floorplanning is a prominent area in the Very Large-Scale Integrated (VLSI) circuit design automation, because it influences the performance, size, yield and reliability of the VLSI chips. It is the process of estimating the positions and shapes of the modules. A high packing density, small feature size and high clock frequency make the Integrated Circuit (IC) to dissipate large amount of heat. So, in this paper, a methodology is presented to distribute the temperature of the module on the layout while simultaneously optimizing the total area and wirelength by using a hybrid Particle Swarm Optimization-Harmony Search (HPSOHS) algorithm. This hybrid algorithm employs diversification technique (PSO) to obtain global optima and intensification strategy (HS) to achieve the best solution at the local level and Modified Corner List algorithm (MCL) for floorplan representation. A thermal modelling tool called hotspot tool is integrated with the proposed algorithm to obtain the temperature at the block level. The proposed algorithm is illustrated using Microelectronics Centre of North Carolina (MCNC) benchmark circuits. The results obtained are compared with the solutions derived from other stochastic algorithms and the proposed algorithm provides better solution.

Influence of Block Copolymer on Formation and Acid Resistant Properties of Hybrid CaCO_3 Particles

Block copolymer polystyrene-b-poly（acrylic acid）（PS-b-PAA） was used as structural template for the synthesis of CaCO3 microparticles. Through this procedure, acid resistant hybrid CaCO3 micro- spheres were obtained. Acid resistant properties of this type of hybrid CaCO3 were studied. Size measurement shows that the acid resistant properties of the hybrid particles are different in different solutions, such as HCl, EDTA, and H2SO4 solutions.

Influence of Particles on the Loading Capacity and the Temperature Rise of Water Film in Ultra-high Speed Hybrid Bearing

Ultra-high speed machining technology enables high efficiency, high precision and high integrity of machined surface. Previous researches of hybrid bearing rarely consider influences of solid particles in lubricant and ultra-high speed of hybrid bearing, which cannot be ignored under the high speed and micro-space conditions of ultra-high speed water-lubricated hybrid bearing. Considering the impact of solid particles in lubricant, turbulence and temperature viscosity effects of lubricant, the influences of particles on pressure distribution, loading capacity and the temperature rise of the lubricant film with four-step-cavity ultra-high speed water-lubricated hybrid bearing are presented in the paper. The results show that loading capacity of the hybrid bearing can be affected by changing the viscosity of the lubricant, and large particles can improve the bearing loading capacity higher. The impact of water film temperature rise produced by solid particles in lubricant is related with particle diameter and minimum film thickness. Compared with the soft particles, hard particles cause the more increasing of water film temperature rise and loading capacity. When the speed of hybrid bearing increases, the impact of solid particles on hybrid bearing becomes increasingly apparent, especially for ultra-high speed water-lubricated hybrid bearing. This research presents influences of solid particles on the loading capacity and the temperature rise of water film in ultra-high speed hybrid bearings, the research conclusions provide a new method to evaluate the influence of solid particles in lubricant of ultra-high speed water-lubricated hybrid bearing, which is important to performance calculation of ultra-high speed hybrid bearings, design of filtration system, and safe operation of ultra-high speed hybrid bearings.

Improved Dynamic Response of DC to DC Converter Using Hybrid PSO Tuned Fuzzy Sliding Mode Controller

DC/DC switching converters are widely used in numerous appliances in modern existence. In this paper, the dynamic and transient response of phase shift series resonant DC/DC converter are improved using hybrid particle swarm optimization tuned fuzzy sliding mode controller under starting and load step change conditions. The aim of the control is to regulate the output voltage beneath the load change. The model of the hybrid particle swarm optimization tuned fuzzy sliding mode controller is implemented using Sim Power Systems toolbox of MATLAB SIMULINK. Performance of the proposed dynamic novel control under step load change condition is investigated.

Synthesis and Characterization of Polystyrene/Nanosilica Organic-Inorganic Hybrid

A polystyrene（PS）/nanosilica organic-inorganic hybrid material was prepared from styrene monomer and commercial aqueous silica sol containing large amounts of Si-OH by means of emulsion polymerization. The nanosilica sol was modified by the addition of the reactive coupling agent methacrylexy propyltrimethoxysilane （ MPS）, and the resulting latex particles were protected by surfactants such as sodium dodecyl sulphonate（ SDS）, hydroxypropyl methyl cellulose （ HMPC）, and poly （vinylpyrrolidone） （PVP）. The effects of the type of surfactant, the amount of surfactant, and the coupling agent on the shape and stability of the resulting latex particles were investigated. The TEM observation indicates that among SDS, HMPC, and PVP, SDS is the best surfactant. When the content of SDS is 0. 5% and the amount of MPS is 7% in the system, the latex with obvious core-shell structure could be obtained. The average diameters of the monodispersed particles range from 182 to 278 nm, and the average number of silica beads for each composite are 1325 and 4409, respectively. The FrIR analysis shows that PS was chemically linked to silica through MPS. The thermal gravimetric analysis shows that when there is a higher silica content, the hybrid composites have a better heat resistance.

Hybrid anti-prematuration optimization algorithm

Heuristic optimization methods provide a robust and efficient approach to solving complex optimization problems.This paper presents a hybrid optimization technique combining two heuristic optimization methods,artificial immune system（AIS） and particle swarm optimization（PSO）,together in searching for the global optima of nonlinear functions.The proposed algorithm,namely hybrid anti-prematuration optimization method,contains four significant operators,i.e.swarm operator,cloning operator,suppression operator,and receptor editing operator.The swarm operator is inspired by the particle swarm intelligence,and the clone operator,suppression operator,and receptor editing operator are gleaned by the artificial immune system.The simulation results of three representative nonlinear test functions demonstrate the superiority of the hybrid optimization algorithm over the conventional methods with regard to both the solution quality and convergence rate.It is also employed to cope with a real-world optimization problem.

Optimal search for moving targets with sensing capabilities using multiple UAVs

This paper studies the problem of using multiple unmanned air vehicles (UAVs) to search for moving targets with sensing capabilities. When multiple UAVs (multi-UAV) search for a number of moving targets in the mission area, the targets can intermittently obtain the position information of the UAVs from sensing devices, and take appropriate actions to increase the distance between themselves and the UAVs. Aiming at this problem, an environment model is established using the search map, and the updating method of the search map is extended by considering the sensing capabilities of the moving targets. A multi-UAV search path planning optimization model based on the model predictive control (MPC) method is constructed, and a hybrid particle swarm optimization algorithm with a crossover operator is designed to solve the model. Simulation results show that the proposed method can effectively improve the cooperative search efficiency and can find more targets per unit time compared with the coverage search method and the random search method.

Thermal Expansion of Al Matrix Composites Reinforced with Hybrid Micro-/nano-sized Al_2O_3 Particles

The thermal expansion behavior of aluminum matrix composites reinforced with hybrid （nanometer and micrometer） Al2O3 particles was measured between 100 and 600℃ and compared to theoretical models. The results revealed that the nanoparticle concentration had significant effect on the thermal expansion behavior of the composites. For the composites with lower nanoparticle concentration, their coefficient of thermal expansion （CTE） is determined by a stress relaxation process. While for the composites with higher nanoparticle concentration, their CTE is determined by a percolation process.

Aeroengine Fault Diagnosis Method Based on Optimized Supervised Kohonen Network

To diagnose the aeroengine faults accurately,the supervised Kohonen(S-Kohonen)network is proposed for fault diagnosis.Via adding the output layer behind competitive layer,the network was modified from the unsupervised structure to the supervised structure.Meanwhile,the hybrid particle swarm optimization(H-PSO)was used to optimize the connection weights,after using adaptive inheritance mode(AIM)based on the elite strategy,and adaptive detecting response mechanism(ADRM),HPSO could guide the particles adaptively jumping out of the local solution space,and ensure obtaining the global optimal solution with higher probability.So the optimized S-Kohonen network could overcome the problems of non-identifiability for recognizing the unknown samples,and the non-uniqueness for classification results existing in traditional Kohonen(T-Kohonen)network.The comparison study on the GE90 engine borescope image texture feature recognition is carried out,the research results show that:the optimized S-Kohonen network has a strong ability of practical application in the classification fault diagnosis;the classification accuracy is higher than the common neural network model.

Delay-area trade-off for MPRM circuits based on hybrid discrete particle swarm optimization

Polarity optimization for mixed polarity Reed-Muller（MPRM） circuits is a combinatorial issue.Based on the study on discrete particle swarm optimization（DPSO） and mixed polarity,the corresponding relation between particle and mixed polarity is established,and the delay-area trade-off of large-scale MPRM circuits is proposed. Firstly,mutation operation and elitist strategy in genetic algorithm are incorporated into DPSO to further develop a hybrid DPSO（HDPSO）.Then the best polarity for delay and area trade-off is searched for large-scale MPRM circuits by combining the HDPSO and a delay estimation model.Finally,the proposed algorithm is testified by MCNC Benchmarks.Experimental results show that HDPSO achieves a better convergence than DPSO in terms of search capability for large-scale MPRM circuits.

Numerical simulation of a dense solid particle flow inside a cyclone separator using the hybrid Euler-Lagrange approach

This paper presents a numerical simulation of the flow inside a cyclone separator at high particle loads. The gas and gas–particle flows were analyzed using a commercial computational fluid dynamics code. The turbulence effects inside the separator were modeled using the Reynolds stress model. The two phase gas–solid particles flow was modeled using a hybrid Euler–Lagrange approach, which accounts for the four-way coupling between phases. The simulations were performed for three inlet velocities of the gaseous phase and several cyclone mass particle loadings. Moreover, the influences of several submodel parameters on the calculated results were investigated. The obtained results were compared against experimental data collected at the in-house experimental rig. The cyclone pressure drop evaluated numerically underpredicts the measured values. The possible reason of this discrepancies was disused.

Extension of the low diffusion particle method for near-continuum two-phase flow simulations

The low diffusion （LD） particle method, proposed by Burt and Boyd, is modified for the near-continuum two-phase flow simulations. The LD method has the advantages of easily coupling with the direct simulation Monte Carlo （DSMC） method for multi-scale flow simulations and dramatically reducing the numerical diffusion error and statistical scatter of the equilibrium particle methods. Liquidor solid-phase particles are introduced in the LD method. Their velocity and temperature updating are respectively, calculated from the motion equation and the temperature equation according to the local gas properties. Coupling effects from condensed phase to gas phase are modeled as momentum and energy sources, which are respectively, equal to the negative values of the total momentum and energy increase in liquid or solid phase. The modified method is compared with theoretical results for unsteady flows, and good agreements are obtained to indicate the reliability of the one-way gas-to-particle coupling models. Hybrid LD-DSMC algorithm is implemented and performed for nozzle discharging gas-liquid flow to show the prospect of the LD-DSMC scheme for multi-scale two-phase flow simulations.

Location layout design of aircraft parts assembly based on MSVR

Location layout of aircraft assembly is an important factor affecting product quality.Most of the existing re-searches use the combination of finite element analysis and intelligent algorithm to optimize the location layout,which are limited by numerical simulation accuracy and the selection and improvement of intelligent algorithms.At present,the analysis and decision-making technology based on field data is gradually applied in aircraft manufacturing.Based on the perception data of intelligent assembly unit of aircraft parts,a regression model of multi-input and multioutput support vector machine with Gauss kernel function as radial basis function is established,and the hyperparameters of the model are optimized by hybrid particle swarm optimization genetic algorithm(PSO-GA).GA-MSVR,PSO-MSVR and PSOGA-MSVR model are constructed respectively,and their results show that PSOGA-MSVR model has the best performance.Finally,the design of the aircraft wing location layout is taken as an example to verify the effectiveness of the method.

A Short-Term Traffic Flow Forecasting Method and Its Applications

Short-term forecast of urban traffic flow is very important to intelligent transportation. Although the conventional methods have some advantages, to some extent, in improving the traffic forecast's precision, it is still hard to achieve high accuracy. In this paper, we propose a short-term traffic flow forecasting method, which is based on the hybrid particle swarm optimization-neural network(HPSO-NN) with error compensation mechanism.In HPSO-NN, the hybrid PSO algorithm is employed to train the structures and parameters of the feed-forward advanced neural network, while the error compensation mechanism is employed to improve the accuracy. HPSONN is used to forecast the vehicle velocity in Shanghai North-South Viaduct. Experimental results show that the HPSO-NN, compared with the auto-regressive and moving average(ARMA) model, can forecast traffic flow with a higher accuracy. What's more, we have also found that HPSO-NN with error compensation mechanism has better performance than that of HPSO-NN alone.

A dynamic tire model based on HPSO-SVM

In order to accurately describe the force mechanism of tires on agricultural roads and improve the life cycle of agricultural tires,a tire-deformable terrain model was established.The effects of tread pattern,wheel spine,tire sidewall elasticity,inflation pressure and soil deformation were considered in the model and fitted with a support vector machine(SVM)model.Hybrid particle swarm optimization(HPSO)was used to optimize the parameters of SVM prediction model,of which inertia weight and learning factor were improved.To verify the performance of the model,a tire force prediction model of agricultural vehicle with the improved SVM method was investigated,which was a complex nonlinear problem affected by many factors.Cross validation(CV)method was used to evaluate the training precision accuracy of the model,and then the improved HPSO was adopted to select parameters.Results showed that the choice randomness of specifying the parameters was avoided and the workload of the parameter selection was reduced.Compared with the dynamic tire model without considering the influence of tread pattern and wheel spine,the improved SVM model achieved a better prediction performance.The empirical results indicate that the HPSO based parameters optimization in SVM is feasible,which provides a practical guidance to tire force prediction of agricultural transport vehicles.

Controlled Cross-linking Strategy for Formation of Hydrogels,Microgels and Nanogels

Hydrogels are a kind of unique cross-linking polymeric materials with three-dimensional networks. Various efforts have been devoted to manipulate the formation of functional hydrogels in situ and enrich the production of hydrogels, microgels and nanogels with improved modulation capacity. However, these methods always fail to tune the gel properties because of the difficulty in achieving the precise control of cross-linking extents once the gel formation is initiated. Therefore, the preparation of tailor-made hydrogels remains a great challenge. Herein, we summarize a controlled cross-linking strategy towards not only fabrication of hydrogels at nano-, micro-and macro-scales, but also achievement of controlled assembly of nanoparticles into multifunctional materials in macroscopic and microscopic scales. The strategy is conducted by controllably activating and terminating the disulfide reshuffling reactions of disulfide-linked core/shell materials with selective core/shell separation using system p H or UV triggers. So it provides a facile approach to producing hydrogels, hydrogel particles and nanoparticle aggregates with tunable structures and properties, opening up the design possibility, flexibility and complexity of hydrogels, microgels/nanogels and nanoparticle aggregates from nanoscopic components to macroscopic objects.