ADAPTIVE RANKS CLONE AND k‐NEAREST NEIGHBOR LIST–BASED IMMUNE MULTI‐OBJECTIVE OPTIMIZATION

Artificial immune systems (AIS) are computational systems inspired by the principles and processes of the vertebrate immune system. The AIS‐based algorithms typically exploit the immune system's characteristics of learning and adaptability to solve some complicated problems. Although, several AIS‐based algorithms have proposed to solve multi‐objective optimization problems (MOPs), little focus have been placed on the issues that adaptively use the online discovered solutions. Here, we proposed an adaptive selection scheme and an adaptive ranks clone scheme by the online discovered solutions in different ranks. Accordingly, the dynamic information of the online antibody population is efficiently exploited, which is beneficial to the search process. Furthermore, it has been widely approved that one‐off deletion could not obtain excellent diversity in the final population; therefore, a k‐nearest neighbor list (where k is the number of objectives) is established and maintained to eliminate the solutions in the archive population. The k‐nearest neighbors of each antibody are founded and stored in a list memory. Once an antibody with minimal product of k‐nearest neighbors is deleted, the neighborhood relations of the remaining antibodies in the list memory are updated. Finally, the proposed algorithm is tested on 10 well‐known and frequently used multi‐objective problems and two many‐objective problems with 4, 6, and 8 objectives. Compared with five other state‐of‐the‐art multi‐objective algorithms, namely NSGA‐II, SPEA2, IBEA, HYPE, and NNIA, our method achieves comparable results in terms of convergence, diversity metrics, and computational time.     

一种基于图像块的Legendre矩高精度快速算法

针对图像的Legendre正交矩计算量大和矩值求解过程中存在离散近似误差等问题,提出一种新的高精度快速计算图像Legendre矩方法.文中首先提出一种最大块优先分块策略,然后在此基础上,根据图像像素灰度值的取值特征将图像进行分块表示,以每个图像块为单位计算图像的Legendre矩.实验结果表明,与现有的快速算法相比,文中方法在保证矩值高精确的前提下,有效地减少了算术运算的次数,降低了计算复杂度,具有较快的计算速度.     

基于多核平台的IPSec VPDN的研究与实现

针对传统的单处理器系统上IPSec VPDN面临越来越多消耗大量计算能力的新型网络应用的困难,提出多核平台下的IPSec VPDN的数据接收和发送的多级并行计算的解决方案。根据多核系统本身的特点,通过多处理器间负载均衡和多核软件可执行代码级兼容性的改进,进一步提高多核系统下IPSec VPDN的处理能力。测试结果验证了多核系统下IPSec VPDN的实现方案的高效性。     

A local construction of the Smith normal form of a matrix polynomial

We present an algorithm for computing a Smith form with multipliers of a regular matrix polynomial over a field. This algorithm differs from previous ones in that it computes a local Smith form for each irreducible factor in the determinant separately and then combines them into a global Smith form, whereas other algorithms apply a sequence of unimodular row and column operations to the original matrix. The performance of the algorithm in exact arithmetic is reported for several test cases.     

fKenzo: A user interface for computations in Algebraic Topology

fKenzo (=friendly Kenzo) is a graphical user interface providing a user-friendly front-end for the Kenzo system, a Common Lisp program devoted to Algebraic Topology. The fKenzo system provides the user interface itself, an XML intermediary generator-translator and, finally the Kenzo kernel. We describe in this paper the main points of fKenzo, and we explain also the advantages and limitations of fKenzo with respect to Kenzo itself. The text is separated into two parts, trying to cover both the user and the developer perspectives.     

A hybrid differential evolution algorithm integrated with an ant system and its application

A new hybrid differential evolution algorithm, in which an ant system is used to select the optimal base vector of mutation operation, named the ant system differential evolution (ASDE), is proposed. In ASDE, each dimension in the feasible solution space is divided into several subspaces evenly, and each subspace is marked with the same initial intensity of pheromone trails. The probability of choosing an individual as the base vector is influenced by the visibility and pheromone quantity of the individual. The trail of the selected base vector’s location subspaces will be reinforced with some pheromones, when the offspring is better than its parent. The experimental results show that the ASDE generally outperforms the other differential evolution algorithms for nine benchmark functions. Furthermore, the ASDE is applied to develop the global kinetic model for SO₂ oxidation on the Cs-Rb-V catalyst, and satisfactory results are obtained.     

A historical perspective of algorithmic lateral inhibition and accumulative computation in computer vision

Certainly, one of the prominent ideas of Professor José Mira was that it is absolutely mandatory to specify the mechanisms and/or processes underlying each task and inference mentioned in an architecture in order to make operational that architecture. The conjecture of the last fifteen years of joint research has been that any bottom-up organization may be made operational using two biologically inspired methods called “algorithmic lateral inhibition”, a generalization of lateral inhibition anatomical circuits, and “accumulative computation”, a working memory related to the temporal evolution of the membrane potential. This paper is dedicated to the computational formulation of both methods. Finally, all of the works of our group related to this methodological approximation are mentioned and summarized, showing that all of them support the validity of this approximation.     

Multiscale computation for bioartificial soft tissues with complex geometries

The mechanical function of soft collagenous tissues is inherently multiscale, with the tissue dimension being in the centimeter length scale and the underlying collagen network being in the micrometer length scale. This paper uses a volume averaging multiscale model to predict the collagen gel mechanics. The model is simulated using a multiscale component toolkit that is capable of dealing with any 3D geometries. Each scale in the multiscale model is treated as an independent component that exchanges the deformation and average stress information through a scale-linking operator. An arterial bifurcation was simulated using the multiscale model, and the results demonstrated that the mechanical response of the soft tissues is strongly sensitive to the network orientation and fiber-to-fiber interactions.     

Global data computation in chordal rings

Existing Global Data Computation (GDC) protocols for asynchronous systems are round-based algorithms designed for fully connected networks. In this paper, we discuss GDC in asynchronous chordal rings, a non-fully connected network. The virtual links approach to solve the consensus problem may be applied to GDC for non-fully connected networks, but it incurs high message overhead. To reduce the overhead, we propose a new non-round-based GDC protocol for asynchronous chordal rings with perfect failure detectors. The main advantage of the protocol is that there is no notion of rounds. Every process creates two messages initially, with one message traversing in a clockwise direction and visiting each and every process in the chordal ring. The second message traverses in a counterclockwise direction. When there is direct connection between two processes, a message is sent directly. Otherwise, the message is sent via virtual links. When the two messages return, the process decides according to the information maintained by the two messages. The perfect failure detector of a process need only detect the crash of neighboring processes, and the crash information is disseminated to all other processes. Analysis and comparison with two virtual links approaches show that our protocol reduces message complexity significantly.     

Range mode and range median queries in constant time and sub-quadratic space

Given a list of n items and a function defined over sub-lists, we study the space required for computing the function for arbitrary sub-lists in constant time.For the function mode we improve the previously known space bound O(n²/logn) to O(n²loglogn/log²n) words.For median the space bound is improved to O(n²loglog²n/log²n) words from O(n²⋅log(k)n/logn), where k is an arbitrary constant and log(k) is the iterated logarithm.