Nature-inspired Computing Research Articles

Bee System Based Base Station Cooperation Technique for Mobile Cellular Networks

Mobility with high degree of network-facility is a mandatory attribute for better QoS in cellular networks, but it leads to a huge increment in energy consumption, a significant fraction of which related to the base stations within the service area. In this paper, we are introducing a base station cooperation technique for energy efficiency in mobile communication networks, and it is based on nature-inspired computing, especially the Bee system. All serving base stations within a given geographical area will work together to provide a better network experience to the mobile subscriber. In order to achieve this, base stations will check for availability of unutilized (or underutilized) bandwidth and if it is over a certain threshold, all base stations perform a re-reunion procedure to provide a uniform distribution of the consumed bandwidth (bandwidth load), thereby providing better service to users. We have formulated the proposed technique to determine the effectiveness, efficiency and scalability of work. Further we create a simulation setup for proposed algorithm using OPNET to implement a real time cellular networks. It has been observed that results of proposed work have sufficient improvement in energy efficiency at the base station with respect to traditional schemes.

BRIEF HISTORY OF NATURAL SCIENCES FOR NATURE-INSPIRED COMPUTING IN ENGINEERING

The goal of the authors is adroit integration of three mainstream disciplines of the natural sciences, physics, chemistry and biology to create novel problem solving paradigms. This paper presents a brief history of the develop­ment of the natural sciences and highlights some milestones which subsequently influenced many branches of science, engineering and computing as a prelude to nature-inspired computing which has captured the imagination of computing researchers in the past three decades. The idea is to summarize the massive body of knowledge in a single paper suc­cinctly. The paper is organised into three main sections: developments in physics, developments in chemistry, and de­velopments in biology. Examples of recently-proposed computing approaches inspired by the three branches of natural sciences are provided.

Nature Inspired Computing: An Overview and Some Future Directions.

This paper presents an overview of significant advances made in the emerging field of nature-inspired computing (NIC) with a focus on the physics- and biology-based approaches and algorithms. A parallel development in the past two decades has been the emergence of the field of computational intelligence (CI) consisting primarily of the three fields of neural networks, evolutionary computing and fuzzy logic. It is observed that NIC and CI intersect. The authors advocate and foresee more cross-fertilisation of the two emerging fields.

Review of nature-inspired methods for wake-up scheduling in wireless sensor networks

Over the last few decades, algorithms inspired by nature have matured into a widely used class of computing methods. They have shown the ability to adjust to variety of conditions, and have been frequently employed for solving complex, real-world optimization problems. They are especially suitable for problems that require adaptation, and that involve optimization of complex, distributed systems, operating in dynamic environments. Among other application domains, nature-inspired methods have been extensively used in the areas of networking in general, and wireless sensor networks in particular. Energy management and network lifetime optimization are two great research and implementation challenges for wireless sensor networks. Duty cycle management, synchronization, and wake-up scheduling are complementary approaches that facilitate this complex optimization process. This review focuses on the intersection of nature-inspired computing and wake-up scheduling algorithms for wireless sensor networks. It describes the state-of-the-art in these fields and provides an up-to-date review of the most recent developments in this interdisciplinary domain. It discusses the motivation for using nature-inspired methods for wake-up scheduling, and presents related open issues and research challenges.

In Silico DNA computing

DNA computing, one of the constituents of nature-inspired computing has two forms, namely, in vitro DNA computing and in silico DNA computing. In vitro DNA computing is the most popular one, where the authors deal with the topology of a dynamic structure consists of macromolecules (e.g., DNA) to solve computational problems. It started with the work of Adleman where in vitro DNA strands were synthesized to solve the travelling salesman problem [5]. Ullah et al. [4] have given an account of the usages of in vitro DNA computing. On the other hand, in silico DNA computing, the other form of DNA computing takes inspirations from the central dogma of molecular biology [6] and performs computation in silicon-based computing machines (i.e., in ordinary computers) to solve computational problems. The central dogma of molecular biology means “once (sequential) information has passed into protein it cannot get out again” [6]. In other words, mappings of DNA/RNA to DNA/RNA/protein are possible but not protein to DNA/RNA/protein [3,4].

Uniformity is Weaker than Semi-Uniformity for Some Membrane Systems

We investigate computing models that are presented as families of finite computing devices with a uniformity condition on the entire family. Examples of such models include Boolean circuits, membrane systems, DNA computers, chemical reaction networks and tile assembly systems, and there are many others. However, in such models there are actually two distinct kinds of uniformity condition. The first is the most common and well-understood, where each input length is mapped to a single computing device (e.g. a Boolean circuit) that computes on the finite set of inputs of that length. The second, called semi-uniformity, is where each input is mapped to a computing device for that input (e.g. a circuit with the input encoded as constants). The former notion is well-known and used in Boolean circuit complexity, while the latter notion is frequently found in literature on nature-inspired computation from the past 20 years or so. Are these two notions distinct? For many models it has been found that these notions are in fact the same, in the sense that the choice of uniformity or semi-uniformity leads to characterisations of the same complexity classes. In other related work, we showed that these notions are actually distinct for certain classes of Boolean circuits. Here, we give analogous results for membrane systems by showing that certain classes of uniform membrane systems are strictly weaker than the analogous semi-uniform classes. This solves a known open problem in the theory of membrane systems. We then go on to present results towards characterising the power of these semi-uniform and uniform membrane models in terms of NL and languages reducible to the unary languages in NL, respectively.

Approaching mathematical model of the immune network based DNA Strand Displacement system

One biggest obstacle in molecular programming is that there is still no direct method to compile any existed mathematical model into biochemical reaction in order to solve a computational problem. In this paper, the implementation of DNA Strand Displacement system based on nature-inspired computation is observed. By using the Immune Network Theory and Chemical Reaction Network, the compilation of DNA-based operation is defined and the formulation of its mathematical model is derived. Furthermore, the implementation on this system is compared with the conventional implementation by using silicon-based programming. From the obtained results, we can see a positive correlation between both. One possible application from this DNA-based model is for a decision making scheme of intelligent computer or molecular robot.

Study on Increasing the Accuracy of Classification Based on Ant Colony algorithm

Abstract. The application for GIS advances the ability of data analysis on remote sensing image. The classification and distill of remote sensing image is the primary information source for GIS in LUCC application. How to increase the accuracy of classification is an important content of remote sensing research. Adding features and researching new classification methods are the ways to improve accuracy of classification. Ant colony algorithm based on mode framework defined, agents of the algorithms in nature-inspired computation field can show a kind of uniform intelligent computation mode. It is applied in remote sensing image classification is a new method of preliminary swarm intelligence. Studying the applicability of ant colony algorithm based on more features and exploring the advantages and performance of ant colony algorithm are provided with very important significance. The study takes the outskirts of Fuzhou with complicated land use in Fujian Province as study area. The multi-source database which contains the integration of spectral information (TM1-5, TM7, NDVI, NDBI) and topography characters (DEM, Slope, Aspect) and textural information (Mean, Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Second Moment, Correlation) were built. Classification rules based different characters are discovered from the samples through ant colony algorithm and the classification test is performed based on these rules. At the same time, we compare with traditional maximum likelihood method, C4.5 algorithm and rough sets classifications for checking over the accuracies. The study showed that the accuracy of classification based on the ant colony algorithm is higher than other methods. In addition, the land use and cover changes in Fuzhou for the near term is studied and display the figures by using remote sensing technology based on ant colony algorithm. In addition, the land use and cover changes in Fuzhou for the near term is studied and display the figures by using remote sensing technology based on ant colony algorithm. The causes of LUCC have been analysed and some suggestions to the development of this region were proposed.

Coastal ‘Big Data’ and nature-inspired computation: Prediction potentials, uncertainties, and knowledge derivation of neural networks for an algal metric

Coastal monitoring has become reliant upon automated sensors for data acquisition. Such a technical commitment comes with a cost; particularly, the generation of large, high-dimensional data streams (‘Big Data’) that personnel must search through to identify data structures. Nature-inspired computation, inclusive of artificial neural networks (ANNs), affords the unearthing of complex, recurring patterns within sizable data volumes. In 2009, select meteorological and hydrological data were acquired via autonomous instruments in Sarasota Bay, Florida (USA). ANNs estimated continuous chlorophyll (CHL) a concentrations from abiotic predictors, with correlations between measured:modeled concentrations >0.90 and model efficiencies ranging from 0.80 to 0.90. Salinity and water temperature were the principal influences for modeled CHL within the Bay; concentrations steadily increased at temperatures >28° C and were greatest at salinities <36 (maximizing at ca. 35.3). Categorical ANNs modeled CHL classes of 6.1 and 11 μg CHL L−1 (representative of local and state-imposed constraint thresholds, respectively), with an accuracy of ca. 83% and class precision ranging from 0.79 to 0.91. The occurrence likelihood of concentrations > 6.1 μg CHL L−1 maximized at a salinity of ca. 36.3 and a temperature of ca. 29.5 °C. A 10th-order Chebyshev bivariate polynomial equation was fit (adj. r2 = 0.99, p < 0.001) to a three-dimensional response surface portraying modeled CHL concentrations, conditional to the temperature–salinity interaction. The TREPAN algorithm queried a continuous ANN to extract a decision tree for delineation of CHL classes; turbidity, temperature, and salinity (and to lesser degrees, wind speed, wind/current direction, irradiance, and urea-nitrogen) were key variables for quantitative rules in tree formalisms. Taken together, computations enabled knowledge provision for and quantifiable representations of the non-linear relationships between environmental variables and CHL a.

A Hybrid Bacterial Foraging Algorithm For Solving Job Shop Scheduling Problems

Bio-Inspired computing is the subset of Nature-Inspired computing. Job Shop Scheduling Problem is categorized under popular scheduling problems. In this research work, Bacterial Foraging Optimization was hybridized with Ant Colony Optimization and a new technique Hybrid Bacterial Foraging Optimization for solving Job Shop Scheduling Problem was proposed. The optimal solutions obtained by proposed Hybrid Bacterial Foraging Optimization algorithms are much better when compared with the solutions obtained by Bacterial Foraging Optimization algorithm for well-known test problems of different sizes. From the implementation of this research work, it could be observed that the proposed Hybrid Bacterial Foraging Optimization was effective than Bacterial Foraging Optimization algorithm in solving Job Shop Scheduling Problems. Hybrid Bacterial Foraging Optimization is used to implement real world Job Shop Scheduling Problems.

Ubiquity symposium: Evolutionary computation and the processes of life

In this article evolutionary computation (EC) is considered as a kind of nature-inspired computing (NIC) paradigm. EC not only has great effect on the development of computing methods from structure to process, but also has great effect on many aspects of our society as a ubiquitous or general computational thinking. EC is still one of the best choices for problem solving among all methods when people face more and more complex problems.

A Hybrid Bacterial Foraging Algorithm For Solving Job Shop Scheduling Problems

Bio-Inspired computing is the subset of Nature-Inspired computing. Job Shop Scheduling Problem is categorized under popular scheduling problems. In this research work, Bacterial Foraging Optimization was hybridized with Ant Colony Optimization and a new technique Hybrid Bacterial Foraging Optimization for solving Job Shop Scheduling Problem was proposed. The optimal solutions obtained by proposed Hybrid Bacterial Foraging Optimization algorithms are much better when compared with the solutions obtained by Bacterial Foraging Optimization algorithm for well-known test problems of different sizes. From the implementation of this research work, it could be observed that the proposed Hybrid Bacterial Foraging Optimization was effective than Bacterial Foraging Optimization algorithm in solving Job Shop Scheduling Problems. Hybrid Bacterial Foraging Optimization is used to implement real world Job Shop Scheduling Problems.

A Two Phase Algorithm for Fuzzy Time Series Forecasting using Genetic Algorithm and Particle Swarm Optimization Techniques

ABSTRACT Fuzzy Time series is being used for forecasting since last two decades for forecasting. Nature inspired computing techniques like other domains are now being used for optimization purpose in Fuzzy Time Series forecasting models to get improved results. In this paper we have presented a new algorithm for multivariate fuzzy time series forecasting having two phases. Genetic Algorithm and Particle Swarm Optimization techniques are used in this algorithm for optimization. We applied our algorithm on Taiwan forex Exchange (TAIFEX) index and got better results and minimized error rate as compared to previous methods. General Terms Nature inspired computing; Time Series forecasting Keywords Fuzzy time series; two-factor high-order fuzzy logical relationships; Genetic Algorithm, Particle Swarm Optimization; TAIFEX index. 1. INTRODUCTION Forecasting is prediction of unseen values of some sequence. It holds significance in economic and financial modeling as entrepreneurs use predicted values for business planning and taking key decisions. In the last two decades Fuzzy Time Series (FTS) is being used for forecasting purpose. Song and Chissom [1, 2] used the concept of FTS and forecasted number of enrollments of the University of Alabama. Many other researchers proposed their models for enrollments forecasting of the University of Alabama ([3], [4], [5], [6], [7], [8], [9], [10] and [11]), temperature prediction [12, 13] and car road accidents [14, 15] using fuzzy time series. Huarng [7] presented heuristic based model for fuzzy time series forecasting. Jilani and Burney [14] presented M-factor high order fuzzy time series forecasting model for car road accident data. Jilani and Burney [16] presented new heuristic based approach for frequency density based partitioning for fuzzy time series forecasting of stock market. In the recent years many researchers started applying Nature inspired computation (NIC) techniques for optimization purpose in FTS forecasting. Kuo et al. [17] and Huang et al.[18] proposed Particle Swarm Optimization (PSO) based FTS forecasting models for enrollments data of the University of Alabama. Park et al. [19] proposed a forecasting model for TAIFEX and KOSPI-200 index, which was based on FTS and swarm intelligence. Jilani et. al. [20] proposed a PSO based FTS forecasting model for car road accidents. Jilani et. al. presented a hybrid algorithm based on Genetic Algorithm (GA) and PSO for forecasting TAIFEX Genes are randomly generated initially to make chromosomes and KSE-100 index. Jilani et. al. [22] presented a trend based heuristic approach using GA for forecasting car road accidents. In this paper we have applied genetic algorithm in first phase to optimize weights, and in second phase we used those weights and optimized interval length to get best forecasting result.

Handwritten characters recognition based on nature-inspired computing and neuro-evolution

The enormous services obtainable by bank and postal systems are not 100 % guaranteed due to variability of handwriting styles. Various methods based on neural networks have been suggested to addres...

ARTIFICIAL FISH SWARM OPTMIZATION FOR MULTILAYERNETWORK LEARNING IN CLASSIFICATION PROBLEMS

Nature-Inspired Computing (NIC) has always been a promising tool to enhance neural network learning. Artificial Fish Swarm Algorithm (AFSA) as one of the NIC methods is widely used for optimizing the global searching of ANN. In this study, we applied the AFSA method to improve the Multilayer Perceptron (MLP) learning for promising accuracy in various classification problems. The parameters of AFSA: AFSA prey, AFSA swarm and AFSA follow are implemented on the MLP network for improving the accuracy of various classification datasets from UCI machine learning. The results are compared to other NIC methods, i.e., Particle Swarm Optimization (PSO) and Differential Evolution (DE), in which AFSA gives better accuracy with feasible performance for all datasets.

NATURE-INSPIRED COGNITIVE EVOLUTION TO PLAY MS. PAC-MAN

Recent developments in nature-inspired computation have heightened the need for research into the three main areas of scientific, engineering and industrial applications. Some approaches have reported that it is able to solve dynamic problems and very useful for improving the performance of various complex systems. So far however, there has been little discussion about the effectiveness of the application of these models to computer and video games in particular. The focus of this research is to explore the hybridization of nature-inspired computation methods for optimization of neural network-based cognition in video games, in this case the combination of a neural network with an evolutionary algorithm. In essence, a neural network is an attempt to mimic the extremely complex human brain system, which is building an artificial brain that is able to self-learn intelligently. On the other hand, an evolutionary algorithm is to simulate the biological evolutionary processes that evolve potential solutions in order to solve the problems or tasks by applying the genetic operators such as crossover, mutation and selection into the solutions. This paper investigates the abilities of Evolution Strategies (ES) to evolve feed-forward artificial neural network's internal parameters (i.e. weight and bias values) for automatically generating Ms. Pac-man controllers. The main objective of this game is to clear a maze of dots while avoiding the ghosts and to achieve the highest possible score. The experimental results show that an ES-based system can be successfully applied to automatically generate artificial intelligence for a complex, dynamic and highly stochastic video game environment.

Predator-prey system: an optimiser that solves a linear programming

Nature-inspired computing is becoming an active area of research that focuses on the study of different self-organising processes in nature. It extracts principles of nature and uses them as an inspirational metaphor to propose novel solutions to different daunting scientific problems. Mimicking some rules of the 'game of life' has led to powerful optimum seeking algorithms. This paper attempts to develop a novel bio-inspired optimisation approach. It shows that predator-prey interaction can be inspired to develop an interesting optimum seeking algorithm that acts as a solver for a linear programming problem.

Machine Learning Emulation in Nature-inspired Computation Systems

The whole frame of nature_inspired computation systems is inquired into, the characteristics of machine learning in nature_inspired computation systems are researched, and a particular scheme on machine learning in nature_inspired computation systems is designed with environment being gathered present data; study unit adopting fuzzy optimizatio algorithm based on genetic algorithm; knowledge base adopting fuzzy optimization BP neural networks; executive unit being complicated industry process. The fuzzy optimizatio learning algorithm of fuzzy optimization BP neural networks is built, the flow chart of the algorithm is constructed, and the emulation test is made. At last, the design criteria of flash metal comsuption are obtained, and the stability of the algorithm is verified through this example. The result shows that machine learning makes nature_inspired computation systems be able to gain know; edge automatically, their quality improved, their intelligent level advanced, and machine learning will greatly influence the memory mode, information input mode and system structures of nature_inspired computation systems.

Review: Loose Space: Possibility and Diversity in Urban Life, Growth Management in Florida: Planning for Paradise, Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation, Handbook of Research on Nature-Inspired Computing for Economics and Management, Geocoding Health Data: The Use of Geographic Codes in Cancer Prevention and Control, Research and Practice, Sustainability and Communities of Place, Planning Models,

Review: Loose Space: Possibility and Diversity in Urban Life, Growth Management in Florida: Planning for Paradise, Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation, Handbook of Research on Nature-Inspired Computing for Economics and Management, Geocoding Health Data: The Use of Geographic Codes in Cancer Prevention and Control, Research and Practice, Sustainability and Communities of Place, Planning Models,

Where is evolutionary computation going? A temporal analysis of the EC community

Studying an evolving complex system and drawing some conclusions from it is an integral part of nature-inspired computing; being a part of that complex system, some insight can also be gained from our knowledge of it. In this paper we study the evolution of the evolutionary computation co-authorship network using social network analysis tools, with the aim of extracting some conclusions on its mechanisms. In order to do this, we first examine the evolution of macroscopic properties of the EC co-authorship graph, and then we look at its community structure and its corresponding change along time. The EC network is shown to be in a strongly expansive phase, exhibiting distinctive growth patterns, both at the macroscopic and the mesoscopic level.