An Improved Whale Optimization Algorithm for Global Optimization and Realized Volatility Prediction

The original whale optimization algorithm(WOA)has a low initial population quality and tends to converge to local optimal solutions.To address these challenges,this paper introduces an improved whale optimization algorithm called OLCHWOA,incorporating a chaos mechanism and an opposition-based learning strategy.This algorithm introduces chaotic initialization and opposition-based initialization operators during the population initialization phase,thereby enhancing the quality of the initial whale population.Additionally,including an elite opposition-based learning operator significantly improves the algorithm’s global search capabilities during iterations.The work and contributions of this paper are primarily reflected in two aspects.Firstly,an improved whale algorithm with enhanced development capabilities and a wide range of application scenarios is proposed.Secondly,the proposed OLCHWOA is used to optimize the hyperparameters of the Long Short-Term Memory(LSTM)networks.Subsequently,a prediction model for Realized Volatility(RV)based on OLCHWOA-LSTM is proposed to optimize hyperparameters automatically.To evaluate the performance of OLCHWOA,a series of comparative experiments were conducted using a variety of advanced algorithms.These experiments included 38 standard test functions from CEC2013 and CEC2019 and three constrained engineering design problems.The experimental results show that OLCHWOA ranks first in accuracy and stability under the same maximum fitness function calls budget.Additionally,the China Securities Index 300(CSI 300)dataset is used to evaluate the effectiveness of the proposed OLCHWOA-LSTM model in predicting RV.The comparison results with the other eight models show that the proposed model has the highest accuracy and goodness of fit in predicting RV.This further confirms that OLCHWOA effectively addresses real-world optimization problems.

Early Warning of Commercial Housing Market Based on Bagging-GWO-SVM

A number of risks exist in commercial housing,and it is critical for the government,the real estate industry,and consumers to establish an objective early warning indicator system for commercial housing risks and to conduct research regarding its measurement and early warning.In this paper,we examine the commodity housing market and construct a risk index for the commodity housing market at three levels:market level,the real estate industry and the national economy.Using the Bootstrap aggregating-grey wolf optimizer-support vector machine(Bagging-GWO-SVM)model after synthesizing the risk index by applying the CRITIC objective weighting method,the commercial housing market can be monitored for risks and early warnings.Based on the empirical study,the following conclusions have been drawn:(1)The commodity housing market risk index accurately reflect the actual risk situation in Tianjin;(2)Based on comparisons with other models,the Bagging-GWO-SVM model provides higher accuracy in early warning.A final set of suggestions is presented based on the empirical study.

Combination of ablative fractional carbon dioxide laser and platelet-rich plasma treatment to improve hypertrophic scars:a retrospective clinical observational study

Background:Hypertrophic scars are one of the main complications that affect the quality of life of patients after burns.Many methods have been shown to be effective in the treatment of hypertrophic scars,such as ablative fractional CO_(2) laser(AFCL)and platelet-rich plasma(PRP).However,there are few studies on the effect of the combined application of these measures.The purpose of this study was to explore the therapeutic effect of AFCL combined with PRP on hypertrophic burn scars.Methods:A retrospective clinical observation study was conducted on 50 patients with hypertrophic burn scars.The AFCL+PRP group included 31 patients who received AFCL combined with PRP treatment;the AFCL group included 19 patients who received AFCL treatment only.The University of North Carolina 4P Scar Scale(UNC4P)and the Vancouver Scar Scale(VSS)scores that were collected before each treatment were used as indicators of the effectiveness of the previous treatment.The scores recorded at the second,fourth and seventh months were analysed.Results:The demographic data of the 2 groups were not significantly different.Before treatment,therewas no difference in the UNC4P and VSS scores between the 2 groups.Therewas a significant decline in the UNC4P and VSS total scores over 6 months in both groups(p<0.05)and scores in the 2 groups were comparable after 3 and 6 months(p<0.05).UNC4P scores in the AFCL+PRP group decreased from a mean of 8.26 to 2.61(p<0.05)with a concomitant drop in VSS scores from a mean of 11.74 to 6.06(p<0.01).In the AFCL group UNC4P and VSS scores decreased from 7.68 to 4.63(p<0.05)and from 10.89 to 8.16(p<0.05),respectively.The sub-items of these 2 assessments were analysed and the results suggest that AFCL combined with PRP can comprehensively improve scarring.Conclusions:This study shows that PRP is an effective adjunct for AFCL in the treatment of hypertrophic burn scars and that the combination of PRP and AFCL proved to be more useful than AFCL alone.This combination may be a new and effective clinical practice for the treatment of scars.However,larger and higher-level clinical studies are still needed to determine its efficacy and possible mechanisms.