Artificial bee colony algorithm with comprehensive search mechanism for numerical optimization

The artificial bee colony （ABC） algorithm is a sim- ple and effective global optimization algorithm which has been successfully applied in practical optimization problems of various fields. However, the algorithm is still insufficient in balancing ex- ploration and exploitation. To solve this problem, we put forward an improved algorithm with a comprehensive search mechanism. The search mechanism contains three main strategies. Firstly, the heuristic Gaussian search strategy composed of three different search equations is proposed for the employed bees, which fully utilizes and balances the exploration and exploitation of the three different search equations by introducing the selectivity probability P,. Secondly, in order to improve the search accuracy, we propose the Gbest-guided neighborhood search strategy for onlooker bees to improve the exploitation performance of ABC. Thirdly, the self- adaptive population perturbation strategy for the current colony is used by random perturbation or Gaussian perturbation to en- hance the diversity of the population. In addition, to improve the quality of the initial population, we introduce the chaotic opposition- based learning method for initialization. The experimental results and Wilcoxon signed ranks test based on 27 benchmark func- tions show that the proposed algorithm, especially for solving high dimensional and complex function optimization problems, has a higher convergence speed and search precision than ABC and three other current ABC-based algorithms.

Effect of aggregate gradation on low temperature performance of asphalt paving mixtures

Low temperature cracking has become one of the important factors that diminish asphalt pavement's ride quality and service life.Especially in cold region,cracking caused by low temperature is the main distress form.This paper discussed the effect of aggregate gradation on the low temperature performance in asphalt paving mixtures.A total of 11 asphalt mixtures with 11 different aggregate gradations and one asphalt binder content were studied.Volumetric properties of the coarse aggregate and asphalt mixtures showed aggregate grading has a significant impact on the degree of aggregate interlock in asphalt mixtures.A trend is existed in the low temperature performance with the change of gradation.With the aid of mathematic statistics,it indicates gradation affects the low temperature performance significantly.The findings also indicate the relationship between the degree of aggregate interlock in asphalt mixtures and the low temperature performance:With the stone-to-stone contact developed,the mixture has a high energy to resist contract and deformation at low temperature.The properties of fine aggregate and asphalt play an important part in resisting low temperature cracking in floating structure.But it provides lower energy to resist low temperature cracking compared to the skeleton structure.

Effects of Large-Scale Climatic Oscillations on the Variability of the Indian Summer Monsoon Rainfall

In India,large-scale climatic oscillations have strong influences on the Indian summer monsoon rainfall(ISMR),which plays a crucial role in India’s agricultural production and economic growth.However,there are limited studies in India that explore the influences of decadal and multidecadal oscillations on the ISMR and associated El Niño–Southern Oscillation(ENSO).Therefore,in this study we carried out a comprehensive and detailed investigation to understand the influences of ENSO,Pacific decadal oscillation(PDO),and Atlantic multidecadal oscillation(AMO)on ISMR across different regions in India.The statistical significance of ISMR associated with different phases(positive/warm and negative/cold)of ENSO,PDO,and AMO(individual analysis),and combined ENSO–AMO,and ENSO–PDO(coupled analysis)were analysed by using the nonparametric Wilcoxon Rank Sum(WRS)test.The individual analysis results indicate that in addition to the ENSO teleconnection,AMO and PDO significantly affect the spatial patterns of ISMR.Coupled analysis was performed to understand how the phase shift of PDO and AMO has modulated the rainfall during El Niño and La Niña phases.The results indicate that the La Niña associated with a positive PDO phase caused excessive precipitation of about 21%–150%in the peninsular,west–central,and hilly regions compared to the individual effect of ENSO/PDO/AMO on ISMR;similarly,the west–central,coastal,and northwest regions received 15%–56%of excessive rainfall.Moreover,during the El Niño combined with PDO positive(AMO positive),above-normal precipitation was observed in hilly,northeast,and coastal(hilly,northeast,west–central,and coastal)regions,opposite to the results obtained from the individual ENSO analysis.This study emphasizes the importance of accounting the decadal and multidecadal forcing when examining variations in the ISMR during different phases of ENSO events.

Effects of drip irrigation on components of water cycle in arid inland areas: A case study of Manas river basin in northwestern China

Compared to either drip irrigation or mulching with plastic film,the two methods together can reduce water requirements of crops grown in arid areas by more than 30%.Such a combination deployed on a large scale(1)reduced the loss of soil water by 31.8%compared to that from drip irrigation alone;(2)narrowed the range of annual evapotranspiration from 1582.4-1780.3 mm,which is average for the basin,to 222.2-294.8 mm;and(3)increased the overall humidity in the central plain of the basin.However,the surrounding regions in which drip irrigation is not combined with mulching are getting more arid;thus,as a result of the water-saving technology,both oases and the desertification of the river basin are increasing at the same time.The results of the study further the understanding of the effects of drip irrigation combined with mulching on water cycles in the basin of the Manas river and suggest ways to protect the ecology and the environment of the basin.

A Hybrid Moth Flame Optimization Algorithm for Global Optimization

The Moth Flame Optimization(MFO)algorithm shows decent performance results compared to other meta-heuristic algorithms for tackling non-linear constrained global optimization problems.However,it still suffers from obtaining quality solution and slow convergence speed.On the other hand,the Butterfly Optimization Algorithm(BOA)is a comparatively new algorithm which is gaining its popularity due to its simplicity,but it also suffers from poor exploitation ability.In this study,a novel hybrid algorithm,h-MFOBOA,is introduced,which integrates BOA with the MFO algorithm to overcome the shortcomings of both the algorithms and at the same time inherit their advantages.For performance evaluation,the proposed h-MFOBOA algorithm is applied on 23 classical benchmark functions with varied complexity.The tested results of the proposed algorithm are compared with some well-known traditional meta-heuristic algorithms as well as MFO variants.Friedman rank test and Wilcoxon signed rank test are employed to measure the performance of the newly introduced algorithm statistically.The computational complexity has been measured.Moreover,the proposed algorithm has been applied to solve one constrained and one unconstrained real-life problems to examine its problem-solving capability of both type of problems.The comparison results of benchmark functions,statistical analysis,real-world problems confirm that the proposed h-MFOBOA algorithm provides superior results compared to the other conventional optimization algorithms.

Statistical inference in the partial functional linear expectile regression model

As extensions of means, expectiles embrace all the distribution information of a random variable.The expectile regression is computationally friendlier because the asymmetric least square loss function is differentiable everywhere. This regression also enables effective estimation of the expectiles of a response variable when potential explanatory variables are given. In this study, we propose the partial functional linear expectile regression model. The slope function and constant coefficients are estimated by using the functional principal component basis. The convergence rate of the slope function and the asymptotic normality of the parameter vector are established. To inspect the effect of the parametric component on the response variable, we develop Wald-type and expectile rank score tests and establish their asymptotic properties. The finite performance of the proposed estimators and test statistics are evaluated through simulation study. Results indicate that the proposed estimators are comparable to competing estimation methods and the newly proposed expectile rank score test is useful. The methodologies are illustrated by using two real data examples.