Nature-inspired Approach Research Articles

Engineering Pervasive Service Ecosystems

Emerging pervasive computing services will typically involve a large number of devices and service components cooperating together in an open and dynamic environment. This calls for suitable models and infrastructures promoting spontaneous, situated, and self-adaptive interactions between components. SAPERE (Self-Aware Pervasive Service Ecosystems) is a general coordination framework aimed at facilitating the decentralized and situated execution of self-organizing and self-adaptive pervasive computing services. SAPERE adopts a nature-inspired approach, in which pervasive services are modeled and deployed as autonomous individuals in an ecosystem of other services and devices, all of which interact in accord to a limited set of coordination laws, or eco-laws. In this article, we present the overall rationale underlying SAPERE and its reference architecture. We introduce the eco-laws--based coordination model and show how it can be used to express and easily enforce general-purpose self-organizing coordination patterns. The middleware infrastructure supporting the SAPERE model is presented and evaluated, and the overall advantages of SAPERE are discussed in the context of exemplary use cases.

ACC-FMD: ant colony clustering for functional module detection in protein-protein interaction networks.

Mining functional modules in Protein-Protein Interaction (PPI) networks is a very important research for revealing the structure-functionality relationships in biological processes. More recently, some swarm intelligence algorithms have been successfully applied in the field. This paper presents a new nature-inspired approach, ACC-FMD, which is based on ant colony clustering to detect functional modules. First, some proteins with the higher clustering coefficients are, respectively, selected as ant seed nodes. And then, the picking and dropping operations based on ant probabilistic models are developed and employed to assign proteins into the corresponding clusters represented by seeds. Finally, the best clustering result in each generation is used to perform the information transmission by updating the similarly function. Experimental results on some benchmarked datasets show that ACC-FMD outperforms the CFinder and MCODE algorithms and has comparative performance with the MINE, COACH, DPClus and Core algorithms in terms of the general evaluation metrics.

An organocatalytic biomimetic approach to α-aminophosphonates.

A novel biomimetic approach to optically active α-aminophosphonates utilizing readily available acylphosphonates and 2-chlorobenzylamine as starting materials has been described. The enantioselective protonation constitutes the main enantiodifferentiating step in the developed strategy. This nature-inspired approach proceeds efficiently and in a highly stereoselective manner.

Optimizing differentiation and commonality levels among models in car line-ups: An empirical application of a nature-inspired heuristic mechanism

The product life cycle of cars is becoming shorter and carmakers constantly introduce new or revised models in their lines, tailored to their customer needs. At the same time, new car model design decisions may have a substantial effect on the cost and revenue drivers. For example, although a new car model configuration with component commonality may lower manufacturing cost, it also hinders increased revenues that could have been achieved through product differentiation. This paper develops and illustrates a state of the art, nature-inspired approach, to design car lines that optimize the degree of differentiation vs commonality among models in the line. More specifically, we apply a swarm intelligence mechanism to stated preference data derived from a large-scale conjoint experiment that measures consumer preferences for passenger cars in a sample of 1164 individuals. The proposed two-step methodology is also incorporated into a prototype system, which has been developed in an attempt to facilitate managerial decision making. Our approach provides interesting insights into how new and existing car models can be combined in a product line and identifies the desired balance between differentiation and commonality levels among models within a product line, which elevates customer satisfaction.

A nature-inspired approach to speed up optimum-path forest clustering and its application to intrusion detection in computer networks

We propose a nature-inspired approach to estimate the probability density function (pdf) used for data clustering based on the optimum-path forest algorithm (OPFC). OPFC interprets a dataset as a graph, whose nodes are the samples and each sample is connected to its k-nearest neighbors in a given feature space (a k-nn graph). The nodes of the graph are weighted by their pdf values and the pdf is computed based on the distances between the samples and their k-nearest neighbors. Once the k-nn graph is defined, OPFC finds one sample (root) at each maximum of the pdf and propagates one optimum-path tree (cluster) from each root to the remaining samples of its dome. Clustering effectiveness will depend on the pdf estimation, and the proposed approach efficiently computes the best value of k for a given application. We validate our approach in the context of intrusion detection in computer networks. First, we compare OPFC with data clustering based on k-means, and self-organization maps. Second, we evaluate several metaheuristic techniques to find the best value of k.

Recent advances in the design of artificial corneas

Artificial corneas are being developed to meet a shortage of donor corneas and to address cases in which allografting is contraindicated. A range of artificial corneas has been developed. Here we review several newer designs and especially those inspired by naturally occurring biomaterials found with the human body and elsewhere. Recent trends in the development of artificial corneas indicate a move towards the use of materials derived from native sources including decellularized corneal tissue and tissue substitutes synthesized by corneal cells in vitro when grown either on their own or in conjunction with novel protein-based scaffolds. Biologically inspired materials are also being considered for implantation on their own with the view to promoting endogenous corneal tissue. More recent attempts at making artificial corneas have taken a more nature-based or nature-inspired approach. Several will in the near future be likely to be available clinically.

Recent advances in the preparation of glycopolymer bioconjugates

The significant progress made in understanding the role of carbohydrates and carbohydrates based therapeutics at molecular level has highlighted the importance of carbohydrate bioconjugates in the field of biology, chemistry and therapeutics. The glycosylation of biomolecules is a nature-inspired approach, to impart structural and functional properties to the biomolecules. The availability of facile techniques to synthesize well-defined glycopolymers of varying molecular weights, compositions and shape and their facile conjugation with biomolecules of interest have helped researchers in understanding many aspects of their biological functions at the molecular level. This review focuses on the development of glycopolymer-bioconjugates and provides a comprehensive overview of the present bioconjugation tools for their synthesis. The glycosylation of biomolecules is achieved by either pre or post-polymerization modification approaches. The review highlights the potential of living radical polymerization for the facile synthesis of glycopolymer bioconjugates using both pre and post-polymerization bioconjugation approaches, and without disrupting the native structure and functions of the biological molecules. Non-covalent carbohydrate–carbohydrate and carbohydrate–protein interactions play a significant role in many biological and pathological events. The non-covalent interactions of synthetic glycopolymers with biomolecules are also discussed in this review.

The Human-Inspired Algorithm: A Hybrid Nature-Inspired Approach to Optimizing Continuous Functions with Constraints

The Human-Inspired Algorithm: A Hybrid Nature-Inspired Approach to Optimizing Continuous Functions with Constraints

Particle swarm optimization with expanding neighborhood topology for the permutation flowshop scheduling problem

This paper introduces a new algorithmic nature-inspired approach that uses particle swarm optimization (PSO) with different neighborhood topologies, for successfully solving one of the most computationally complex problems, the permutation flowshop scheduling problem (PFSP). The PFSP belongs to the class of combinatorial optimization problems characterized as NP-hard and, thus, heuristic and metaheuristic techniques have been used in order to find high quality solutions in reasonable computational time. The proposed algorithm for the solution of the PFSP, the PSO with expanding neighborhood topology, combines a PSO algorithm, the variable neighborhood search strategy and a path relinking strategy. As, in general, the structure of the social network affects strongly a PSO algorithm, the proposed method using an expanding neighborhood topology manages to increase the performance of the algorithm. As the algorithm starts from a small size neighborhood and by increasing (expanding) in each iteration the size of the neighborhood, it ends to a neighborhood that includes all the swarm, and it manages to take advantage of the exploration abilities of a global neighborhood structure and of the exploitation abilities of a local neighborhood structure. In order to test the effectiveness and the efficiency of the proposed method, we use a set of benchmark instances of different sizes and compare the proposed method with a number of other PSO algorithms and other algorithms from the literature.

Intelligent Motion Control for Four-Wheeled Holonomic Mobile Robots Using FPGA-Based Artificial Immune System Algorithm

This paper presents an intelligent motion controller for four-wheeled holonomic mobile robots with four driving omnidirectional wheels equally spaced at 90 degrees from one another by using field-programmable gate array (FPGA)-based artificial immune system (AIS) algorithm. Both the nature-inspired AIS computational approach and motion controller are implemented in one FPGA chip to address the optimal control problem of real-world mobile robotics application. The proposed FPGA-based AIS method takes the advantages of artificial intelligence and FPGA technology by using system-on-a-programmable chip (SoPC) methodology. Experimental results are conducted to show the effectiveness and merit of the proposed FPGA-based AIS intelligent motion controller for four-wheeled omnidirectional mobile robots. This FPGA-based AIS autotuning intelligent controller outperforms the conventional nonoptimal controllers, the genetic algorithm (GA) controller, and the particle swarm optimization (PSO) controller.

Using a Renormalization Group to Create Ideal Hierarchical Network Architecture with Time Scale Dependency

SUMMARY This paper employs the nature-inspired approach to investigate the ideal architecture of communication networks as large-scale and complex systems. Conventional architectures are hierarchical with respect to the functions of network operations due entirely to implementation concerns and not to any fundamental conceptual benefit. In contrast, the large-scale systems found in nature are hierarchical and demonstrate orderly behavior due to their space/time scale dependencies. In this paper, by examining the fundamental requirements inherent in controlling network operations, we clarify the hierarchical structure of network operations with respect to time scale. We also describe an attempt to build a new network architecture based on the structure. In addition, as an example of the hierarchical structure, we apply the quasi-static approach to describe user-system interaction, and we describe a hierarchy model developed on the renormalization group approach.

Programming and evolving physical self-assembling systems in three dimensions

Being able to engineer a set of components and their corresponding environmental conditions such that target entities emerge as the result of self-assembly remains an elusive goal. In particular, understanding how to exploit physical properties to create self-assembling systems in three dimensions (in terms of component movement) with symmetric and asymmetric features is extremely challenging. Furthermore, primarily top-down design methodologies have been used to create physical self-assembling systems. As the sophistication of these systems increases, it will be more challenging to use top-down design due to self-assembly being an algorithmically NP-complete problem. In this work, we first present a nature-inspired approach to demonstrate how physically encoded information can be used to program and direct the self-assembly process in three dimensions. Second, we extend our nature-inspired approach by incorporating evolutionary computing, to couple bottom-up construction (self-assembly) with bottom-up design (evolution). To demonstrate our design approach, we present eight proof-of-concept experiments where virtual component sets either defined (programmed) or generated (evolved) during the design process have their specifications translated and fabricated using rapid prototyping. The resulting mechanical components are placed in a jar of fluid on an orbital shaker, their environment. The energy and physical properties of the environment, along with the physical properties of the components (including complementary shapes and magnetic-bit patterns, created using permanent magnets to attract and repel components) are used to engineer the self-assembly process to create emergent target structures with three-dimensional symmetric and asymmetric features. The successful results demonstrate how physically encoded information can be used with programming and evolving physical self-assembling systems in three dimensions.

A nature-inspired camouflaging approach to protect nuclear secrets

Nuclear weapons laboratories face a frequent challenge when they have to grant access to classified information (secrets) on a need-to-know (NTK) basis. Currently such access decisions are subjective – leading to risks of releasing classified information to non-deserving information-seekers. This paper suggests a nature-inspired simulation model ‘Discerning Nuclear Intentions (DiNI)–NTK’ based on the phenomenon of camouflaging (mimicry) in order to lend objectivity to NTK decision-making. The model uses a combination of information vulnerability and characteristics – factors that help assess possible ‘camouflaging’ intentions of the information-seeker and then accordingly make an informed NTK decision. Based on a live indictment of a nuclear physicist and his wife who were formerly employed in a nuclear weapons laboratory, DiNI–NTK is tested with estimated parameter values and the output of the model has provided the correct non-disclosure decision. This model can be used to inform NTK-based classified information access decisions in an objective manner.

Parsimonious rule generation for a nature-inspired approach to self-assembly

Most construction of artificial, multicomponent structures is based upon an external entity that directs the assembly process, usually following a script/blueprint under centralized control. In contrast, recent research has focused increasingly on an alternative paradigm, inspired largely by the nest building behavior of social insects, in which components “self-assemble” into a given target structure. Adapting such a nature-inspired approach to precisely self-assemble artificial structures (bridge, building, etc.) presents a formidable challenge: one must create a set of local control rules to direct the behavior of the individual components/agents during the self-assembly process. In recent work, we developed a fully automated procedure that generates such rules, allowing a given structure to successfully self-assemble in a simulated environment having constrained, continuous motion; however, the resulting rule sets were typically quite large. In this article, we present a more effective methodology for automatic rule generation, which makes an attempt to parsimoniously capture both the repeating patterns that exist within a structure, and the behaviors necessary for appropriate coordination. We then empirically show that the procedure developed here generates sets of rules that are not only correct, but significantly reduced in size, relative to our earlier approach. Such rule sets allow for simpler agents that are nonetheless still capable of performing complex tasks, and therefore demonstrate the problem-solving potential of self-organized systems.

Coupling-Based Internal Clock Synchronization for Large-Scale Dynamic Distributed Systems

This paper studies the problem of realizing a common software clock among a large set of nodes without an external time reference (i.e., internal clock synchronization), any centralized control, and where nodes can join and leave the distributed system at their will. The paper proposes an internal clock synchronization algorithm which combines the gossip-based paradigm with a nature-inspired approach, coming from the coupled oscillators phenomenon, to cope with scale and churn. The algorithm works on the top of an overlay network and uses a uniform peer sampling service to fulfill each node's local view. Therefore, differently from clock synchronization protocols for small scale and static distributed systems, here, each node synchronizes regularly with only the neighbors in its local view and not with the whole system. An evaluation of the convergence speed and the synchronization error of the coupled-based internal clock synchronization algorithm has been carried out, showing how convergence time and the synchronization error depends on the coupling factor and the local view size. Moreover, the variation of the synchronization error with respect to churn and the impact of a sudden variation of the number of nodes have been analyzed to show the stability of the algorithm. In all these contexts, the algorithm shows nice performance and very good self-organizing properties. Finally, we showed how the assumption on the existence of a uniform peer-sampling service is instrumental for the good behavior of the algorithm and how, in system models where network delays are unbounded, a mean-based convergence function reaches a lower synchronization error than median-based convergence functions exploiting the number of averaged clock values.

Identification of a Non-Linear Landing Gear Model Using Nature-Inspired Optimization

This work deals with the application of a nature-inspired optimization technique to solve an inverse problem represented by the identification of an aircraft landing gear model. The model is described in terms of the landing gear geometry, internal volumes and areas, shock absorber travel, tire type, and gas and oil characteristics of the shock absorber. The solution to this inverse problem can be obtained by using classical gradient-based optimization methods. However, this is a difficult task due to the existence of local minima in the design space and the requirement of an initial guess. These aspects have motivated the authors to explore a nature-inspired approach using a method known as LifeCycle Model. In the present formulation two nature-based methods, namely the Genetic Algorithms and the Particle Swarm Optimization were used. An optimization problem is formulated in which the objective function represents the difference between the measured characteristics of the system and its model counterpart. The polytropic coefficient of the gas and the damping parameter of the shock absorber are assumed as being unknown: they are considered as design variables. As an illustration, experimental drop test data, obtained under zero horizontal speed, were used in the non-linear landing gear model updating of a small aircraft.