Global dynamics of a di usive single species model with periodic delay.

The growth of the species population is greatly influenced by seasonally varying environments. By regarding the maturation age of the species as a periodic developmental process, we propose a time periodic and diffusive model in bounded domain. To analyze this model with periodic delay, we first define the basic reproduction ratio R0 of the spatially homogeneous model and then show that the species population will be extinct when R0≤1 while remains persistent and tends to periodic oscillation if R0>1. Finally, combining the comparison principle with the fact that solutions of the spatially homogeneous model are also solutions of our model subject to Neumann boundary condition, we establish the global dynamics of a threshold type for PDE model in terms of R0.

A new upper bound of geometric constant [Formula: see text].

A new constant [Formula: see text] is introduced into any real [Formula: see text]-dimensional symmetric normed space X. By virtue of this constant, an upper bound of the geometric constant [Formula: see text], which is used to measure the difference between Birkhoff orthogonality and isosceles orthogonality, is obtained and further extended to an arbitrary m-dimensional symmetric normed linear space ([Formula: see text]). As an application, the result is used to prove a special case for the reverse Hölder inequality.

[An auto-extraction method of spectral line for normal galaxy spectra].

Spectral line extraction for normal galaxy spectra is the most difficult task in spectral line auto-extraction of celestial spectra. The present paper presents a novel technique of spectral line auto-extraction for normal galaxy spectra. Firstly, the Max operator of two spectra is defined, and the operator produces a new spectrum whose intensity at each wavelength is the maximum of intensities of the two spectra; Secondly, the continuum is fitted in an iterative way, where in each iteration, the traditional wavelet method is performed for the spectrum obtained by the Max operation of the original spectrum, and the continuum is fitted in the last iteration; Finally, adaptive local thresholding associated with the universal thresholding is used to extract spectral lines. Experiments show that this method is superior to the traditional wavelet method. It will be helpful to the spectral line based automated spectral classification and parameter measurement.

A dual-population differential evolution with coevolution for constrained optimization.

Inspired by the fact that in modern society, team cooperation and the division of labor play important roles in accomplishing a task, this paper proposes a dual-population differential evolution (DPDE) with coevolution for constrained optimization problems (COPs). The COP is treated as a bi-objective optimization problem where the first objective is the actual cost or reward function to be optimized, while the second objective accounts for the degree of constraint violations. At each generation during the evolution process, the whole population is divided into two based on the solution's feasibility to treat the both objectives separately. Each subpopulation focuses on only optimizing the corresponding objective which leads to a clear division of work. Furthermore, DPDE makes use of an information-sharing strategy to exchange search information between the different subpopulations similar to the team cooperation. The comparison of the proposed method on a number of benchmark functions with selected state-of-the-art constraint-handling algorithms indicates that the proposed technique performs competitively and effectively.

Artificial Bee Colony Algorithm Based on Information Learning.

Inspired by the fact that the division of labor and cooperation play extremely important roles in the human history development, this paper develops a novel artificial bee colony algorithm based on information learning (ILABC, for short). In ILABC, at each generation, the whole population is divided into several subpopulations by the clustering partition and the size of subpopulation is dynamically adjusted based on the last search experience, which results in a clear division of labor. Furthermore, the two search mechanisms are designed to facilitate the exchange of information in each subpopulation and between different subpopulations, respectively, which acts as the cooperation. Finally, the comparison results on a number of benchmark functions demonstrate that the proposed method performs competitively and effectively when compared to the selected state-of-the-art algorithms.

A novel artificial bee colony algorithm based on modified search equation and orthogonal learning.

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, ABC has an insufficiency regarding its solution search equation, which is good at exploration but poor at exploitation. To address this concerning issue, we first propose an improved ABC method called as CABC where a modified search equation is applied to generate a candidate solution to improve the search ability of ABC. Furthermore, we use the orthogonal experimental design (OED) to form an orthogonal learning (OL) strategy for variant ABCs to discover more useful information from the search experiences. Owing to OED's good character of sampling a small number of well representative combinations for testing, the OL strategy can construct a more promising and efficient candidate solution. In this paper, the OL strategy is applied to three versions of ABC, i.e., the standard ABC, global-best-guided ABC (GABC), and CABC, which yields OABC, OGABC, and OCABC, respectively. The experimental results on a set of 22 benchmark functions demonstrate the effectiveness and efficiency of the modified search equation and the OL strategy. The comparisons with some other ABCs and several state-of-the-art algorithms show that the proposed algorithms significantly improve the performance of ABC. Moreover, OCABC offers the highest solution quality, fastest global convergence, and strongest robustness among all the contenders on almost all the test functions.