Fault tolerant network design inspired by Physarum polycephalum

Physarum polycephalum, a true slime mould, is a primitive, unicellular organism that creates networks to transport nutrients while foraging. The design of these natural networks proved to be advanced, e.g. the slime mould was able to find the shortest path through a maze. The underlying principles of this design have been mathematically modelled in literature. As in real life the slime mould can design fault tolerant networks, its principles can be applied to the design of man-made networks. In this paper, an existing model and algorithm are adapted and extended with stimulation and migration mechanisms which encourage formation of alternative paths, optimize edge positioning and allow for automated design. The extended model can then be used to better design fault tolerant networks. The extended algorithm is applied to several national and international network configurations. Results show that the extensions allow the model to capture the fault tolerance requirements more accurately. The resulting extended algorithm overcomes weaknesses in geometric graph design and can be used to design fault tolerant networks such as telecommunication networks with varying fault tolerance requirements.     

An anticipation mechanism for the shortest path problem based on Physarum polycephalum

In this paper, we put forward an anticipation mechanism for the existing Physarum-inspired shortest path finding method. The Physarum-based shortest path finding model can be implemented by an iterative algorithm and has wide applications in many fundamental network optimization problems. In this paper, we mainly focus on the Physarum-inspired shortest path tree model. Normally, we stop the program when the difference between two consecutive iterations is less than a predefined threshold. However, we do not know how to set the specific value for the threshold variable. In order to find out the optimal solution, we need to set the threshold as a very small number. This in turn will consume a lot of time. From this point of view, this algorithm lacks an efficient and reliable mechanism to judge when the optimal solution will be found. In this paper, we introduce an anticipation mechanism to address this issue. Numerical examples are used to demonstrate its reliability and efficiency.     

Visualizing language evolution as an emergent phenomenon based on biological evolution and learning

From the perspective of artificial life, language can be viewed as a complex adaptive system emerging from linguistic interactions between individuals. Language and the human brain have evolved in parallel and by interacting with each other. In this study, we propose a model of language evolution based on biological evolution and learning. In our model, the linguistic space is expressed in the polar coordinate system in which each possible language is expressed as a point. We conduct evolutionary experiments based on this model, and visualize the results in linguistic space. The distribution trajectory of innate linguistic abilities shows the diversification and complexity of language growth. In the extended experiment, in which the angular coordinates represent the additional effect of the cost of plasticity, we observe a general tendency that the cost of plasticity evolves to become smaller. However, it never evolves to be zero, which might suggest that some cost of plasticity producing the Baldwin effect is adaptive in language evolution.     

基于生物刺激神经网络的多机器人编队方法

多机器人编队控制是多机器人协作领域的重要研究内容之一,如何实现多机器人朝同一目标移动的同时保持队形是多机器人编队的一个热点和难点问题。针对这一问题,提出一种新的基于生物刺激神经网络的多机器人动态编队方法,采用基于leader-referenced编队模型实时计算各机器人的虚拟目标位置,利用生物刺激神经网络进行机器人导航。最后进行仿真实验,实验结果表明该方法在实现多机器人实时避障并保持队形的同时,朝同一目标移动,而且可以很快实现队形变换,具有较好的实时性和灵活性。     

Optimal design of microfluidic networks using biologically inspired principles

From the earliest of times, Man has sought to replicate ideas that have evolved naturally in plants and animals. Understanding and extracting these “natural” design strategies has opened up a whole new field of research known as biomimetics. Designs formulated using biologically inspired principles range from novel surface treatments that mimic physiological processes to geometrical optimization for improving the performance of a system. In this paper, we will show how biomimetic principles based on the laws that govern biological vascular trees can be used to design artificial microfluidic distribution systems. The study focuses specifically on microfluidic manifolds composed of constant-depth rectangular- or trapezoidal-sectioned channels, as these geometries can readily be fabricated using standard micro-fabrication techniques. We will show that it is possible to introduce a prescribed element of flow control into the system by carefully selecting the branching parameter that governs the change in channel dimension at each bifurcation.     

Two complementary operations inspired by the DNA hairpin formation: Completion and reduction

We consider two complementary operations: Hairpin completion introduced in [D. Cheptea, C. Martin-Vide, V. Mitrana, A new operation on words suggested by DNA biochemistry: Hairpin completion, in: Proc. Transgressive Computing, 2006, pp. 216–228] with motivations coming from DNA biochemistry and hairpin reduction as the inverse operation of the hairpin completion. Both operations are viewed here as formal operations on words and languages. We settle the closure properties of the classes of regular and linear context-free languages under hairpin completion in comparison with hairpin reduction. While the class of linear context-free languages is exactly the weak-code image of the class of the hairpin completion of regular languages, rather surprisingly, the weak-code image of the class of the hairpin completion of linear context-free languages is a class of mildly context-sensitive languages. The closure properties with respect to the hairpin reduction of some time and space complexity classes are also studied. We show that the factors found in the general cases are not necessary for regular and context-free languages. This part of the paper completes the results given in the earlier paper, where a similar investigation was made for hairpin completion. Finally, we briefly discuss the iterated variants of these operations.     

受粒子群和差分进化启发的人工蜂群算法

针对基本人工蜂群算法搜索策略探索能力强而开发能力弱的特点,受粒子群和差分进化思想的启发,提出了两种新的搜索策略：PSO-DE-PABC和PSO-DE-GABC。前者在随机个体附近产生新的候选位置以提高算法的多样性;后者在最优解附近产生新的候选位置以提高算法的收敛速度,并加入差分进化中的差异向量来增加种群的多样性。在此基础上,引入维度因子来控制算法的收敛速度,并且使用一种利用当前种群信息的侦查策略来增强算法的局部搜索能力。通过对10组标准测试函数的实验仿真并与基本ABC、GABC和ABC/best算法相比,结果表明PSO-DE-GABC和PSO-DE-PABC对数值优化具有更高的收敛速度和收敛精度。     

Differential evolution and sparse neural networks

Abstract: The aim of this work is to avoid overfitting by seeking parsimonious neural network models and hence to provide better out-of-sample predictions. The resulting sparse networks are easier to interpret as simple rules which, in turn, could give greater insight into the structure of the data. Fully connected feedforward neural networks are pruned through optimization of an estimated Schwartz model selection criterion using differential evolution to produce a sparse network. A quantity, α , which indicates how close a parameter is to zero is used to estimate the number of model parameters which are being pruned out. The value of α is incorporated into a function of the Schwartz information criterion to form an objective function whose maxima, as α tends to zero, define parsimonious neural network models for a given data set. Since there is a multiplicity of maxima, differential evolution, with its greater capacity for global optimization, is used to optimize this objective function. The value of α is progressively reduced during the evolution of the population of models in the manner of a sequential unconstrained optimization technique. The method is illustrated by results on four sets of data.     

HiveSec: security in resource-constrained wireless networks inspired by beehives and bee swarms

Cryptographic algorithms rely on the strengths of all their fundamental components and expect them to be harmonious in accomplishing desired levels of security in applications. In order for a security solution to be sophisticated and to provide high security (measured in terms of the security goals it satisfies), the solution needs to typically involve complex mathematical operations and/or multiple stages of operation. While these might offer increased security, such solutions might not be applicable to all systems. We refer to resource-constrained wireless networks, such as radio frequency identification and wireless body area networks, where the resources available on-chip are often decided by the balance between device costs, requirements of longevity and usability. The constraints, thus, require designing solutions that use simple logical operations and are based on reuse of functions, while introducing sufficient unpredictability to increase security. In this paper, we present a key management and message signature generation scheme called HiveSec, whose design is inspired by the symmetry in beehives and the nature of bee swarms, and which offers security through unpredictability and reduced resource usage. We validate our work through simulation studies and security analysis.     

动态复杂网络的同步拓扑演化

在定性讨论复杂网络模型的同步性能后,仿真分析了较大网络尺寸的情况,通过数据分析比对、网络拓扑的可视化和拓扑的模拟退火演化,寻找到了同步优化的一定规律,即度分布和平均距离均匀化、集中化;簇系数的适当调节对同步性能影响不大,但能减少网络连接等。结合未来智能电力网发展的实际情况,制定了同步稳定性优化策略,并在美国西部电力网数据上进行实验,探索从拓扑角度优化实际网络的应用价值,满足实时性、稳定性、分布式等要求。通过特征值比这一评价指标的对比证明优化有效。     

CRWO: Clustering and routing in wireless sensor networks using optics inspired optimization

In wireless sensor networks (WSNs), cluster head (CH) selection and routing are two well known optimization problems associated with high computational complexity for large scale networks. Optics inspired optimization (OIO) is one of the recent meta-heuristic techniques used to solve a wide range of NP-Hard problems. In this study, firstly an OIO based CH selection algorithm is proposed by considering various parameters such as energy, distance and node degree. Next, clusters are formed using derived potential function. Finally, an OIO based routing algorithm is proposed to compute the route from each CH to the base station (BS) using the same parameters like energy, distance and node degree. In the performance evaluation, the proposed algorithm (CRWO) is extensively tested and compared with some of the existing algorithms based on both conventional and nature inspired routing techniques. Simulation results depict the superiority of the proposed algorithm over its comparatives.     

Complexity and stochastic evolution of dyadic networks

A strategic model of network formation is developed which permits unreliable links and organizational costs. Finding a connected Nash network which guarantees a given payoff to each player proves to be an NP-hard problem. For the associated evolutionary game with asynchronous updating and logit updating rules, the stochastically stable networks are characterized.     

Influences of variable scales and activation functions on the performances of multilayer feedforward neural networks

This paper gives insight into the methods about how to improve the learning capabilities of multilayer feedforward neural networks with linear basis functions in the case of limited number of patterns according to the basic principles of support vector machine (SVM), namely, about how to get the optimal separating hyperplanes. And furthermore, this paper analyses the characteristics of sigmoid-type activation functions, and investigates the influences of absolute sizes of variables on the convergence rate, classification ability and non-linear fitting accuracy of multilayer feedforward networks, and presents the way of how to select suitable activation functions. As a result, this proposed method effectively enhances the learning abilities of multilayer feedforward neural networks by introducing the sum-of-squares weight term into the networks’ error functions and appropriately enlarging the variable components with the help of the SVM theory. Finally, the effectiveness of the proposed method is verified through three classification examples as well as a non-linear mapping one.     

基于空间引力作用的复杂网络演化模型*

为了研究空间距离、边权演化、节点规模等因素在复杂网络演化过程中的共同作用,借助空间引力模型,采用基尼系数的统计方法,综合分析这三种因素对网络拓扑结构异质性的影响作用。数值仿真研究表明,空间距离、边权演化作用并不改变网络结构的无标度性;演化过程中节点规模的分布同时受空间距离、边权演化因素影响;网络连边距离分布符合幂律函数,其中幂指数与空间距离因素相关,而与边权演化因素无明显关系。     

网络流驱动的复杂输运网络演化模型

传统复杂网络演化模型在网络拓扑结构与边权演化的机制设计中,未考虑网络流对于输运网络演化的驱动作用。引入网络流的动态驱动机制,分析网络流的规模增长、空间距离的制约与最短路径的配流策略三种因素,在输运网络的演化过程中所发挥的作用。发现这三种因素并不足以改变复杂网络的无标度性;基于最短路径的配流机制是网络流分布不均的关键影响因素;空间距离抑制作用对于网络相配性具有关键影响作用。