The artificial bee colony algorithm is a relatively new optimization technique. This paper presents an improved artificial bee colony (IABC) algorithm for global optimization. Inspired by ...differential evolution (DE) and introducing a parameter
M, we propose two improved solution search equations, namely “
ABC/best/1” and “
ABC/rand/1”. Then, in order to take advantage of them and avoid the shortages of them, we use a selective probability
p to control the frequency of introducing “
ABC/rand/1” and “
ABC/best/1” and get a new search mechanism. In addition, to enhance the global convergence speed, when producing the initial population, both the chaotic systems and the opposition-based learning method are employed. Experiments are conducted on a suite of unimodal/multimodal benchmark functions. The results demonstrate the good performance of the IABC algorithm in solving complex numerical optimization problems when compared with thirteen recent algorithms.
► “
ABC/best/1” and “
ABC/rand/1” are proposed. ► A new search mechanism is got by introducing a selective probability
p. ► Both opposition-based learning method and chaotic maps are employed. ► The experiment results demonstrate the good performance of the IABC algorithm.
The artificial bee colony (ABC) algorithm is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, there is still an ...insufficiency in the ABC algorithm regarding its solution search equation, which is good at exploration but poor at exploitation. Inspired by differential evolution (DE), we propose a modified ABC algorithm (denoted as ABC/best), which is based on that each bee searches only around the best solution of the previous iteration in order to improve the exploitation. In addition, to enhance the global convergence, when producing the initial population and scout bees, both chaotic systems and opposition-based learning method are employed. Experiments are conducted on a set of 26 benchmark functions. The results demonstrate good performance of ABC/best in solving complex numerical optimization problems when compared with two ABC based algorithms.
AIMS: The aim of this study was to quantify and understand the driving factors of the spatial variation of soil respiration (R S) in an old-growth mixed broadleaved-Korean pine forest in northeastern ...China. METHODS: All woody stems ≥1 cm diameter at breast height (DBH) were measured in the 9 ha plot. Simultaneous measurements of R S, soil temperature (T S) and soil water content (W S) were conducted for 256 sampling points on a regular 20-m grid refined with 512 additional sampling points randomly placed within each of the 20-m blocks in May, July and September of 2014. RESULTS: The variogram analyses revealed 87–91 % of the sample variance was explained by autocorrelation over a range of 15 to 23 m during the observation periods. The R S were highly correlated among the measurements made in May, July and September. The model indicated that the W S, bulk density (BD) and maximum DBH for trees within 3 m (radius) of the measurement collars explained 46 % of the spatial variation in R S seasonally averaged across three observations. CONCLUSIONS: The spatial patterns of R S remained constant across the three measurement campaigns. The spatial variation in R S was primarily controlled by the W S and forest stand structure.
To assess the skiing economy (SE) and kinematics during double poling (DP) roller skiing between two groups of skiers in a field setting. Five experienced and five novice male skiers performed a SE
...test at 16 km∙h
on an outdoor athletics track. Gas exchange parameters were measured to determine SE
. A two-dimensional video was filmed to measure the kinematics variables. Experienced skiers exhibited a 21% lower oxygen cost than novice skiers (p = 0.016) in DP, indicating a strong association between SE
, cycle length and cycle rate (p < 0.001). Additionally, before the poling phase, experienced skiers manifested significantly greater maximum hip and knee extension angles than novice skiers (p < 0.001). During the poling phase, experienced skiers with a greater pole plant angle (p = 0.001), longer flexion time (p < 0.001) and higher flexion angular velocity in the elbow joint (p < 0.05) demonstrated better SE
. There was an interaction effect of the one-repetition maximum bench press × group in SE
(b = - 0.656, SE = 0.097, t = - 6.78, p = 0.001). Therefore, experienced skiers with better SE
demonstrated more efficient cycles, potentially accomplished using dynamic full-body DP motion to ascertain effective propulsion. Combined upper body strength and ski-specific skill training may enhance SE
in novice skiers.
As an essential step of metaheuristic optimizers, initialization seriously affects the convergence speed and solution accuracy. The main motivation of the state-of-the-art initialization method is to ...generate a small initial population to cover the search space as much as possible uniformly. However, these approaches have suffered from the curse of dimensionality, high computational cost, and sensitivity to parameters, which ultimately reduce the algorithm's convergence speed. In this paper, a new initialization technique named diagonal linear uniform initialization (DLU) is proposed, which follows a novel search view, i.e., adopting the diagonal subspace sampling instead of the whole space. By considering the algorithm's update mechanism, the improved sampling method dramatically improves the convergence speed and solution accuracy of metaheuristic algorithms. Compared with the other eight widely used initialization strategies, the differential evolution (DE) algorithm with DLU obtains the best performance in search accuracy and convergence speed. In the extension experiments, results show that the DLU is still effective for three swarm-based algorithms: particle swarm optimization (PSO), cuckoo search (CS), and artificial bee colony (ABC). Especially for the multi-objective problem, the DLU still demonstrates its powerful performance compared with other strategies.
Bovine viral diarrhea virus (BVDV) is considered to be the most common agent of severe diarrhea in cattle worldwide, causing fever, diarrhea, ulcers, and abortion. Bovine herpesvirus 1 (BoHV-1) is ...also a major bovine respiratory disease agent that spreads worldwide and causes extensive damage to the livestock industry. Recombinase polymerase amplification (RPA) is a novel nucleic acid amplification method with the advantages of high efficiency, rapidity and sensitivity, which has been widely used in the diagnosis of infectious diseases. A dual RPA assay was developed for the simultaneous detection of BVDV and BoHV-1. The assay was completed at a constant temperature of 37 °C for 30 min. It was highly sensitive and had no cross-reactivity with other common bovine viruses. The detection rate of BVDV RPA in clinical samples (36.67%) was higher than that of PCR (33.33%), the detection rate of BoHV-1 RPA and PCR were equal. Therefore, the established dual RPA assay for BVDV and BoHV-1 could be a potential candidate for use as an immediate diagnostic.
Multiobjective multitasking optimization (MTO) has attracted more and more attention because of its ability to solve multiple multiobjective optimization problems simultaneously. By transferring ...knowledge between tasks, MTO can improve the performance of optimization tasks. However, if the way of knowledge transfer is not reasonable, it will have a negative impact on the performance of tasks. To solve this problem and ensure the effectiveness of knowledge transfer, this paper proposes a multiobjective evolutionary multitasking algorithm based on dual transfer learning with generative filtering model namely EMT–DLGM. Specifically, a dual transfer learning mechanism is proposed to reduce the difference between tasks and improve the efficiency of knowledge transfer through the global and local transfer strategies. Moreover, the generative filtering model is designed to generate promising solutions according to the multiple differential evolution operations and filtering model. The experimental results on three MTO test suites demonstrate that EMT–DLGM is superior or comparable to other state-of-the-art multiobjective evolutionary multitasking algorithms.
Sparse precision matrix estimation, also known as the estimation of the inverse covariance matrix in statistical contexts, represents a critical challenge in numerous multivariate analysis ...applications. This challenge becomes notably complex when the dimension of the data is far greater than the capacity of samples. To address this issue, we introduce a convex relaxation model that employs the first-order optimality conditions associated with the lasso-penalized D-trace loss for the purpose of estimating sparse precision matrix. The proposed model is effectively solved through the widely recognized alternating direction method of multipliers. Additionally, we provide closed-form solutions to subproblems in each iteration with a computational complexity of O(np2), and establish the convergence of our proposed algorithm. Numerical investigations demonstrate that our algorithm exhibits the capability to handle large-scale datasets and significantly outperforms the existing methods, particularly when dealing with high-dimensional scenarios characterized by a large dimension p.
We validated the daily formation of increments in otoliths of yellow goosefish
Lophius litulon
using reared individuals to examine the growth in the field. Single, round-shaped core structures were ...observed in 41% of sagittae and in 73% of the lapilli. Therefore, lapillar otoliths were used for further observations of daily increment analysis. The lapillus radius of newly hatched larvae was 15.0 ± 1.4 μm (mean ± standard deviation) forming the hatch check (first check). At 6 days after hatching (DAH), the second check was observed, which may correspond to the energy transition from endogenous to exogenous nutrition, located at 28.1 ± 0.7 μm from the core structures. Thereafter, clear daily increments formed outside the check. To estimate larval growth in the field, we examined the relationship between the notochord length (NL) and lapillus radius by an allometric equation. The mean growth rate was estimated as ca. 0.18 mm in NL/day during 10–40 DAH. The results of this study provide insights into the previously unknown early life history and will enable further understanding of the population dynamics of the genus
Lophius
.
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