Bio and Nature Inspired Algorithms Research Papers (original) (raw)

The network based denial of service attacks (DoS) are still the big challenge to the researchers in the field of network security. This paper handles the popular DoS attack called TCP-SYN flood attack, and presents the design and implementation of an Artificial Immune system for Syn flood Detection, abbreviated by AISD, based on the Dendritic Cell Algorithm (DCA). The AISD system is able to detect the generated SYN flood attack and response to its generator in a real-time. Performance and accuracy of the system have been evaluated through five experiments. Results of the experiments showed the precision of intrusion detection process to the ratio of 100%, with a notable response speed, and this is shows the benefit and suitability of using artificial immune systems to the network security problems.

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- Network Security, Semantic Web, Bio-Inspired Systems, Big Data / Analytics / Data Mining

In Informatics we sometimes come across problems, which can- not be solved by common algorithms. Other times, the devised algorithms are too complex for real use. An alternative solution can often be found in the use of metaheuristics. One of the metaheuristics subgroups are algorithms inspired by nature. Their main inspiration is nature itself. They imitate processes from life. Social insects like ants or bees are good ex- amples. At the rst sight they are very simple organisms. When we look closer, however, their overall behavior is amazing, as is their organization in a swarm when working towards their common goal. Application of these algorithms can lead to interesting solutions in many di erent elds, especially when no other viable solution is available. DNA assembly is one of such problems. The problem is to assemble DNA from fragments read by some DNA sequencing technology, since current technologies are not able to read the whole DNA sequence, only much shorter fragments. We propose a possible solution to the DNA assembly problem by use of a biologically inspired algorithm that imitates relies. We adapted algorithm for this problem and designed new algorithm operators. We implemented the proposed solution in a prototype. Finally, we successfully veri ed the algorithm on GenFrag and DNAgen benchmark instances of DNA problem.

- by Štefan Kasala and +1
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- Bio and Nature Inspired Algorithms

Cuckoo Search Strategy (CSS) is the newly developed strategy based on the Cuckoo Search Algorithm. In order to achieve best performance, a number of parameters in the CuckooSearch Algorithm needs to be tuned namely the nest size, the elitism probability, and the repetition. This paper describes the tuning process for Cuckoo Search Algorithm involving t-way testing, that is, by taking the standard covering array involving CA (N, 2, 46). Our initial experiment results using obtained range of parameter values of CSS demonstration that CSS able to give sufficiently competitive results compared to existing work

This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: http://www.elsevier.com/copyright a b s t r a c t Software testing is an important but complex part of software development life cycle. The optimization of the software testing process is a major challenge, and the generation of the independent test paths remains unsatisfactory. In this paper, we present an approach based on metaheuristic firefly algorithm to generate optimal test paths. In order to optimize the test case paths, we use a modified firefly algorithm by defining appropriate objective function and introducing guidance matrix in traversing the graph. Our simulations and comparison show that the test paths generated are critical and optimal paths.

- by Syed Muhammad Zubair Rehman Gillani and +1
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- Computer Science, Artificial Intelligence, Machine Learning, Data Mining

Spider Monkey Optimization (SMO) technique is most recent member in the family of swarm optimization algorithms.SMO algorithm fall in class of Nature Inspired Algorithm (NIA). SMO algorithm is good in exploration and exploitation of local search space and it is well balanced algorithm most of the times. This paper presents a new strategy to update position of solution during local leader phase using fitness of individuals. The proposed algorithm is named as Fitness based Position Update in SMO (FPSMO) algorithm as it updates position of individuals based on their fitness. The anticipated strategy enhances the rate of convergence. The planned FPSMO approach tested over nineteen benchmark functions and for one real world problem so as to establish superiority of it over basic SMO algorithm.

- by Dr. Sandeep Kumar and +1
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- Swarm Intelligence, Metaheuristics (Operations Research), Particle Swarm Optimization, Engineering Optimization

A sample chapter of the Book on
"Bio-inspired Computation and Applications in Image Processing"
(Elsevier, 2016).

- by Xin-She Yang and +1
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- Image Processing, Swarm Intelligence, Metaheuristics (Operations Research), Bio and Nature Inspired Algorithms

Presently, various governments and organizations are focusing towards digitization of technical and academic documents, which overloads the digital libraries. However, it is difficult to manage a huge amount of data (big data) with current data processing techniques. In literature, bio-inspired algorithms based models and architectures are developed by various industry and academic groups to facilitate data analytics for big data. This chapter depicts a broad methodical literature analysis of bio-inspired algorithms for big data analytics. The current status of bio-inspired algorithms is categorized into three different categories: ecological, swarm-based and evolutionary. This chapter compares the existing models and architectures, explores the current trends and identifies the existing challenges in the development of big data analytical technique. This research work will also help to choose the most appropriate bio-inspired algorithm for big data analytics in a specific type of data along with promising directions for future research.

Grey Wolf Optimizer (GWO) is an intelligent metaheuristic approach which imitates the leadership hierarchy and cooperative hunting behavior of a group of Grey wolves(wolfpack). An augmentation of GWO, named Augmented GWO (AGWO), was recently proposed which possesses greater exploration abilities. Nevertheless, in some cases, AGWO underperforms in the exploitation phase and stagnates at local optimum. The CS algorithm is a nature-inspired optimizing technique that mimics the unique nesting strategy of cuckoo birds and levy-flights. Both the algorithms possess powerful searching capabilities. In our research work, a novel hybrid metaheuristic, termed AGWOCS, is put forth, which combines the merits of both metaheuristics in order to attain global optimum effectively. The proposed algorithm amalgamates the exploring abilities of the AGWO with the exploiting abilities of the Cuckoo Search (CS). For the purpose of testing the proficiency of our proposed hybrid AGWOCS, twenty-three renowned benchmarking functions were used. It is compared with six other existing metaheuristics, including Standard GWO, Particle Swarm Optimization (PSO), Augmented-GWO (AGWO), Enhanced-GWO (EGWO), Hybrid GWO with CS (CS-GWO) and Hybrid PSO and GWO (GWOPSO). The simulation results indicate that AGWOCS surpasses other metaheuristics in terms of rapid convergence rates as well as avoiding local optimum stagnation.

- by Satyam Sharma and +1
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- Evolutionary algorithms, Metaheuristic Algorithms, Bio and Nature Inspired Algorithms, Nature-Inspired Algorithm

This report presents the application of the developed improved artificial fish swarm algorithm (mCAFAC, which was reported and discussed in Seminar I) in determining the weighting matrices (Q and R) of linear quadratic regulator controller. The state and control matrices of the Quadratic Inverted Pendulum (QIP) were used to formulate the optimization problem. An initial matrix (identity) is assigned to the weighting matrices and mCAFAC is then used to determine the optimal values of the weighting matrices and the result is used to stabilize QIP. The results obtained were tested against those of the conventional method of selecting Q and R using the settling time as the figure of merit. The settling times obtained for the first, second, third and fourth pendulum of the QIP using the mCAFAC were 2.6590s, 5.7106s, 7.1137s and 7.1616s respectively as against the following values obtained using the conventional approach, 6.3157s, 7.0590s, 7.3121s and 7.5162s. The results showed that the mCAFAC performed better than the conventional approach in determining the values of Q and R, which are critical in stabilizing and optimally controlling systems, such as the QIP.

- by Salawudeen A Tijani
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- Bio and Nature Inspired Algorithms

Bat algorithm (BA) is a recent metaheuristic optimization algorithm proposed by Yang. In the present study, we have introduced chaos into BA so as to increase its global search mobility for robust global optimization. Detailed studies have been carried out on benchmark problems with different chaotic maps. Here, four different variants of chaotic BA are introduced and thirteen different chaotic maps are utilized for validating each of these four variants. The results show that some variants of chaotic BAs can clearly outperform the standard BA for these benchmarks.

- by Amir H Gandomi and +1
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- Algorithms, Optimization, Metaheuristic Algorithms, Bat Algorithm

In this paper, we reflect on the concept of nature that is presupposed in biomimetic approaches to technology and innovation. Because current practices of biomimicry presuppose a technological model of nature, it is questionable whether its claim of being a more ecosystem friendly approach to technology and innovation is justified. In order to maintain the potentiality of biomimicry as ecological innovation, we explore an alternative to this technological model of nature. To this end, we reflect on the materiality of natural systems and explore a natural model of nature, which is found in the responsive conativity of matter. This natural model of nature enables us to conceptualize biomimicry as conative responsiveness to the conativity of the biosphere.
PLEASE SEND AN EMAIL TO VINCENT.BLOK@WUR.NL IF YOU WANT TO RECEIVE A PDF OF THE EDITED/FORMATTED VERSION

- by Vincent Blok
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- Philosophy of Technology, Plant Ecology, Innovation statistics, Ecological Design

Introduction to Algorithms for Data Mining and Machine Learning (book) introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data.

- by Xin-She Yang
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- Algorithms, Machine Learning, Data Mining, Metaheuristics (Operations Research)

The field of Wireless Sensor Networks (WSNs) is experiencing a resurgence of interest and a continuous
evolution in the scientific and industrial community. The use of this particular type of ad hoc network is
becoming increasingly important in many contexts, regardless of geographical position and so, according
to a set of possible application. WSNs offer interesting low cost and easily deployable solutions to perform
a remote real time monitoring, target tracking and recognition of physical phenomenon. The uses of these
sensors organized into a network continue to reveal a set of research questions according to particularities
target applications. Despite difficulties introduced by sensor resources constraints, research contributions
in this field are growing day by day. In this paper, we present a comprehensive review of most recent
literature of WSNs and outline open research issues in this field.

In order to achieve greater stability and structural efficiency, engineers use
nature as an inspiration for their design. Considering the level of sustainability
in biological systems, they analyze their structural behavior, browse for
compatible analogies with technical systems, abstract the observed physical
and mechanical principles, and implement these principles into the final
product. This process defines the bionic design approach. The term “bionics”
refers to the scientific discipline that addresses the transfer of properties from
biology to technology. This thesis aims to study the theoretical fundamentals
of this concept, explain its methodologies, and demonstrate its applicability in
architecture. Through the assessment of bionically built shell structures, the
different steps of the design process are illustrated, and the elements of each
analogy are evaluated on both biological and technological ends. The chosen
examples in this thesis incorporate concrete, masonry, steel, and timber
structures. Multidisciplinary literature is reviewed to deliver a thorough
explanation for each design project. Based on this research, the structural
benefits that characterize certain biological systems can be transferred into
architectural design, and improve the load bearing capacity of shell structures
in particular, using the bionic approach.

- by Ismail Hmidet
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- Civil Engineering, Design, Structural Engineering, Biomimetics

Computer graphics and animation have has become a key technology in determining future research and development activities in many academic and industrial branches. The aim of this journal is to be an international peer-reviewed open access forum for scientific and technical presentations and discus the latest advances in Computer graphics and animation.

- by Brent Mittelstadt and +4
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- Business Ethics, Computer Science, Algorithms, Parallel Algorithms

Navigation abilities are crucial for survival in nature, and there are a wide range of sophisticated abilities concerning animal navigation and migration. Many applications are related to navigation and routing problems, which are in turn related to optimization problems. This chapter provides an overview of navigation in nature, navigation and routing problems as well as their mathematical formulations. We will then introduce some nature-inspired algorithms for solving optimization problems with discussions about their main characteristics and the ways of solution representations. Citation Detail:

— Stock markets represent complex, nonlinear, ever-changing, trading systems where traders (investors) are competing for profit. In recent years, stock market forecasting has attracted special attention among researchers. However, the irregular nature and the complex behavior of stock data transform forecasting into a very challenging task, and forecasting stock indices has proven to be difficult. Recently bio-inspired algorithms have emerged as an extremely useful tool for forecasting dynamic systems like the stock market. Though considerable research has already been done on various stock markets, relatively less attention has been given to the Dhaka Stock Exchange (DSE) in Bangladesh. This work presents a stock market forecasting system based on hybridized bio-inspired algorithms for the DSE. Experimental results show that the proposed system is useful for forecasting stock market prices. This paper also presents a review of the literature on applications of bio-inspired algorithms for stock market forecasting.

Many optimization problems in science and engineering are challenging to solve, and the current trend is to use swarm intelligence (SI) and SI-based algorithms to tackle such challenging problems. Some significant developments have been made in recent years, though there are still many open problems in this area. This paper provides a short but timely analysis about SI-based algorithms and their links with self-organization. Different characteristics and properties are analyzed here from both mathematical and qualitative perspectives. Future research directions are outlined and open questions are also highlighted.

A revised and further conceptualized presentation of a short section published anonymously within a project-description in "Η Καινοτομία των Ερευνητικών Εργασιών στο Νέο Λύκειο - Βιβλίο Εκπαιδευτικού", Η. Ματσαγγούρας (ed.), Ογανισμός... more

Optimization algorithms are necessary to solve many problems such as parameter tuning. Particle Swarm optimization (PSO) is one of these optimization algorithms. The aim of PSO is to search for the optimal solution in the search space. This paper highlights the basic background needed to understand and implement the PSO algorithm. This paper starts with basic definitions of the PSO algorithm and how the particles are moved in the search space to find the optimal or near optimal solution. Moreover, a numerical example is illustrated to show how the particles are moved in convex optimization problem. Another numerical example is illustrated to show how the PSO trapped in a local minima problem. Two experiments are conducted to show how the PSO searches for the optimal parameters in one-dimensional and two-dimensional spaces to solve machine learning problems.

The firefly algorithm has become an increasingly important tool of Swarm Intelligence that has been applied in almost all areas of optimization, as well as engineering practice. Many problems from various areas have been successfully solved using the firefly algorithm and its variants. In order to use the algorithm to solve diverse problems, the original firefly algorithm needs to be modified or hybridized. This paper carries out a comprehensive review of this living and evolving discipline of Swarm Intelligence, in order to show that the firefly algorithm could be applied to every problem arising in practice. On the other hand, it encourages new researchers and algorithm developers to use this simple and yet very efficient algorithm for problem solving. It often guarantees that the obtained results will meet the expectations.

- by Xin-She Yang
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- Firefly Algorithm, Bio and Nature Inspired Algorithms

Glossary Social Algorithms • Algorithm: An algorithm is a step-by-step, computational procedure or a set of rules to be followed by a computer in calculations or computing an answer to a problem. • Ant colony optimization: Ant colony optimization (ACO) is an algorithm for solving optimization problems such as routing problems using multiple agents. ACO mimics the local interactions of social ant colonies and the use of chemical messenger – pheromone to mark paths. No centralized control is used and the system evolves according to simple local interaction rules. • Bat algorithm: Bat algorithm (BA) is an algorithm for optimization, which uses frequency-tuning to mimic the basic behaviour of echolocation of microbats. BA also uses the variations of loudness and pulse emission rates and a solution vector to a problem corresponds to a position vector of a bat in the search space. Evolution of solutions follow two algorithmic equations for positions and frequencies. • Bees-inspired algorithms: Bees-inspired algorithms are a class of algorithms for optimization using the foraging characteristics of honeybees and their labour division to carry out search. Pheromone may also be used in some variants of bees-inspired algorithms. • Cuckoo Search: Cuckoo search (CS) is an optimization algorithm that mimics the brood parasitism of some cuckoo species. A solution to a problem is considered as an egg laid by a cuckoo. The evolution of solutions is carried out by Lévy flights and the similarity of solutions controlled by a switch probability. • Firefly algorithm: Firefly algorithm is an optimization inspired by the flashing patterns of tropical fireflies. The location of a firefly is equivalent to a solution vector to a problem, and the evolution of fireflies follows a nonlinear equation to simulate the attraction between fireflies of different brightness that is linked to the objective landscape of the problem. • Metaheuristic: Metaheuristic or metaheuristic algorithms are a class of optimization algorithms designed by drawing inspiration from nature. They are thus mostly nature-inspired algorithms, and examples of such metaheuristic algorithms are ant colony optimization, fire-fly algorithm, and particle swarm optimization. These algorithms are often swarm intelligence based algorithms. Nature-Inspired computation: Nature-inspired computation is an area of computer science, concerning the development and application of nature-inspired metaheuristic algorithms for optimization, data mining, machine learning and computational intelligence.

Many optimization algorithms have been developed by drawing inspiration from swarm intelligence (SI). These SI-based algorithms can have some advantages over traditional algorithms. In this paper, we carry out a critical analysis of these SI-based algorithms by analyzing their ways to mimic evolutionary operators. We also analyze the ways of achieving exploration and exploitation in algorithms by using mutation, crossover and selection. In addition, we also look at algorithms using dynamic systems , self-organization and Markov chain framework. Finally, we provide some discussions and topics for further research.

- by Xin-She Yang
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- Cuckoo Search, Bat Algorithm, Firefly Algorithm, Bio and Nature Inspired Algorithms

In October 2016, the White House, the European Parliament, and the UK House of Commons each issued a report outlining their visions on how to prepare society for the widespread use of AI. In this article, we provide a comparative assessment of these three reports in order to facilitate the design of policies favourable to the development of a 'good AI society'. To do so, we examine how each report addresses the following three topics: (a) the development of a 'good AI society'; (b) the role and responsibility of the government, the private sector, and the research community (including academia) in pursuing such a development; and (c) where the recommendations to support such a development may be in need of improvement. Our analysis concludes that the reports address adequately various ethical, social, and economic topics, but come short of providing an overarching political vision and long-term strategy for the development of a 'good AI society'. In order to contribute to fill this gap, in the conclusion we suggest a two-pronged approach.

- by Corinne Cath and +4
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- Engineering, Robotics, Algorithms, Parallel Algorithms

Firefly algorithm (FA) was developed by Xin-She Yang in 2008 and it has 1 become an important tool for solving the hardest optimization problems in almost 2 all areas of optimization as well as engineering practice. The literature has expanded 3 significantly in the last few years. Various FA variants have been developed to suit 4 different applications. This chapter provides a brief review of this expanding and 5 state-of-the-art literature on this dynamic and rapidly evolving domain of swarm 6 intelligence. 7 Keywords Firefly algorithm · Discrete firefly algorithm · Nature-inspired algorithm ·

Deep learning is presently attracting extra ordinary attention from both the industry and the academia. The optimization of deep learning models through nature inspired algorithms is a subject of debate in computer science. In this paper , we present recent progress on the application of nature inspired algorithms in deep learning. The survey pointed out recent development issues, strengths, weaknesses and prospects for future research. A new taxonomy is created based on natured inspired algorithms for deep learning. The trend of the publications in this domain is depicted; it shows the research area is growing but slowly. The deep learning architectures not exploit by the nature inspired algorithms for optimization are unveiled. We believed that the survey can facilitate synergy between the nature inspired algorithms and deep learning research communities. As such, massive attention can be expected in a near future.

This paper presents the state-of-the-art and reviews the state-of-research of acoustic sensors used for a variety of navigation and guidance applications on air and surface vehicles. In particular, this paper focuses on echolocation, which is widely utilized in nature by certain mammals (e.g., cetaceans and bats). Although acoustic sensors have been extensively adopted in various engineering applications, their use in navigation and guidance systems is yet to be fully exploited. This technology has clear potential for applications in air and surface navigation/guidance for intelligent transport systems (ITS), especially considering air and surface operations indoors and in other environments where satellite positioning is not available. Propagation of sound in the atmosphere is discussed in detail, with all potential attenuation sources taken into account. The errors introduced in echolocation measurements due to Doppler, multipath and atmospheric effects are discussed, and an uncertainty analysis method is presented for ranging error budget prediction in acoustic navigation applications. Considering the design challenges associated with monostatic and multi-static sensor implementations and looking at the performance predictions for different possible configurations, acoustic sensors show clear promises in navigation, proximity sensing, as well as obstacle detection and tracking. The integration of acoustic sensors in multi-sensor navigation systems is also considered towards the end of the paper and a low Size, Weight and Power, and Cost (SWaP-C) sensor integration architecture is presented for possible introduction in air and surface navigation systems.

- by Roberto Sabatini and +3
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- Electrical Engineering, Electronic Engineering, Acoustic Engineering, Aerospace Engineering

There are two images of Sir. Patrick Geddes that have been received by subsequent generations, Geddes the Liberal and Geddes the Mystic. This paper examines the ways in which revived sensitivity to the wisdom and limitations of Geddes the Mystic may help facilitate the process of transforming human-nature relations and staving off the cultural and ecological genocide of Modernity.

- by Luke Barnesmoore
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- Critical Theory, Environmental Engineering, Buddhism, Landscape Ecology

Firefly algorithm is a nature-inspired optimization algorithm and there have been significant developments since its appearance about ten years ago. This chapter summarizes the latest developments about the firefly algorithm and its... more

Algorithms inspired by intelligent behavior of simple agents are very popular now a day among researchers. A comparatively young algorithm motivated by extraordinary behavior of Spider Monkeys is Spider Monkey Optimization (SMO) algorithm. SMO algorithm is very successful algorithm to get to the bottom of optimization problems. This work presents a self-adaptive Spider Monkey optimization (SaSMO) algorithm for optimization problems. The proposed strategy is self-adaptive in nature and therefore no manual parameter setting is required. The proposed technique is named as Self-Adaptive Spider Monkey optimization (SaSMO) algorithm. SaSMO gives better results for considered problems. Results are compared with basic SMO and its recent variant MPU-SMO.

- by Dr. Sandeep Kumar and +1
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- Computational Intelligence, Swarm Intelligence, Particle Swarm Optimization, Engineering Optimization