Nature Inspired Research Articles

La espiral biomimética inspiradora de retos actuales de diseño y soluciones sostenibles innovadoras

This paper presents an experience carried out in the field of product design aimed at knowing more in depth what biomimicry is, discovering the possibilities it offers when designing more sustainable products and introducing this inspiration in nature within the usual design thinking and process, thus putting two disciplines such as biology and design to work together. This experience is designed through several collaborative workshops between future product designers and biology consultants belonging to the Biomimicry Granada association. This interdisciplinary collaboration does not seek mastery of many fields, but rather the opening to new disciplines that can offer different, more sustainable and innovative tools to define and solve design challenges. Knowledge and work with the biomimetic design spiral methodology is promoted through a guided application by experts. The actions carried out during the three months of the collaboration will be described in detail, as well as the results obtained in terms of learning and sustainable innovation.

Enhanced Binary Cuckoo Search With Frequent Values and Rough Set Theory for Feature Selection

Redundant and irrelevant features in datasets decrease classification accuracy, and increase computational time of classification algorithms, overfitting problem and complexity of the underlying classification model. Feature selection is a preprocessing technique used in classification algorithms to improve the selection of relevant features. Several approaches that combine Rough Set Theory (RST) with Nature Inspired Algorithms (NIAs) have been used successfully for feature selection. However, due to the inherit limitations of RST for some data types and the inefficient convergence of NIAs for high dimensional datasets, these approaches have mainly focused on a specific type of low dimensional nominal dataset. This paper proposes a new filter feature selection approach based on Binary Cuckoo Search (BCS) and RST, which is more efficient for low and high dimensional nominal, mixed and numerical datasets. It enhances BCS by developing a new initialization and global update mechanisms to increase the efficiency of convergence for high dimensional datasets. It also develops a more efficient objective function for numerical, mixed and nominal datasets. The proposed approach was validated on 16 benchmark datasets; 4 nominal, 4 mixed and 8 numerical drawn from the UCI repository. It was also evaluated against standard BCS; five NIAs with fuzzy RST approaches; two popular traditional FS approaches; and multi objective evolutionary, Genetic, and Particle Swarm Optimization (PSO) algorithms. Decision tree and naive Bayes algorithms were used to measure the classification performance of the proposed approach. The results show that the proposed approach achieved improved classification accuracy while minimizing the number of features compared to other state-of-the-art methods. The code is available at https://github.com/abualia4/EBCS .

Optimized flapping wing dynamics via DMOC approach

Outstanding aerial capabilities that insects present in nature inspire researchers to undertake a challenge to develop a flapping aerial vehicle with performances unmatched by any manmade object. However, the complex aerodynamic phenomena crucial for the insect flight are not easily understood, let alone modeled and utilized for flight. The researchers managed to develop a quasi-steady aerodynamic model capable of capturing the most important aspects of a fruit fly-like insect flight, while still being efficient enough to allow for the usage in the flapping mechanism optimization loop. This experimentally justified quasi-steady model is used in the paper as a building block for creating a novel optimization algorithm, based on the discrete mechanics and optimal control framework. When compared to the conventional approaches to design optimization, this framework includes the natural description of the energy cost function, while incorporating the physical laws in the form of a discrete Lagrange–d’ Alembert equations inherently in optimization constraints. This leads to the discrete description of the inherently continuous problem, allowing the algorithm to search for the optimal solutions in the whole domain. In other words, in contrast to the conventional approaches involving the assumption on the function family and subsequent optimization on the parameters of that function type, this approach is not constrained by the user input and is capable of yielding any solution that respects the physical laws. As presented by the numerical test cases, optimizing the flapping patterns of a fruit fly-like aerial vehicle in standstill hovering leads to both effective and robust optimization tool.

Cost Optimality of an erratic Geo^{X}/G/1 Retrial Queue under J-vacation scheme using Nature Inspired Algorithms

Cost Optimality of an erratic Geo^{X}/G/1 Retrial Queue under J-vacation scheme using Nature Inspired Algorithms

The Red Colobuses Monkey: A New Nature–Inspired Metaheuristic Optimization Algorithm

The Red Colobuses Monkey: A New Nature–Inspired Metaheuristic Optimization Algorithm

Dynamic task scheduling using nature inspired algorithms

In this article; introduces a dynamic task scheduling in distributed system. Many techniques have been proposed for dynamic task scheduling problem. In a distributed system; Nature inspired algorithms have been drawn up for scheduling the heterogeneous tasks on heterogeneous processors dynamically. It utilizes different nature inspired algorithms to minimize and maximize the makespan and average utilization of processors respectively. It deals with the dynamic task scheduling problem. This paper is demonstrated with three phases. In first phase; introduces the dynamic task scheduling problem with the computation of objectives. In the next phase; explaining about the Nature Inspired algorithms applied to this problem. All proposed Nature Inspired algorithms are introduced as a multi-objective optimization algorithm. In the last phase; the experimental results compared with the varied Nature Inspired algorithms to get the better performance in dynamic task scheduling problem. We have accomplished more effective and good outcomes by analyzing all the techniques over a varied scenario with scheduling of 52 tasks on 29 heterogeneous processors.

Nature inspired optimization tools for SVMs - NIOTS

Support Vector Machines (SVMs) technique for achieving classifiers and regressors. However, to obtain models with high accuracy and low complexity, it is necessary to define the kernel parameters as well as the parameters of the training model, which are called hyperparameters. The challenge of defining the more suitable value to hyperparameters is called the Parameter Selection Problem (PSP). However, minimizing the complexity and maximizing the generalization capacity of the SVMs are conflicting criteria. Therefore, we propose the Nature Inspired Optimization Tools for SVMs (NIOTS) that offers a method to automate the search process for the best possible solution for the PSP, allowing the user to quickly obtain several sets of good solutions and choose the one most appropriate for his specific problem.•The PSP has been modeled as a Multi-objective Optimization Problem (MOP) with two objectives: (1) good precision and (2) low complexity (low number of support vectors).•The user can evaluate multiple solutions included in the Pareto front, in terms of precision and low complexity of the model.•Apart from the Adaptive Parameter with Mutant Tournament Multiobjective Differential Evolution (APMT-MODE), the user can choose other metaheuristics and also among several kernel options.

Artificial Neural Synchronization Using Nature Inspired Whale Optimization

In this article, a whale optimization-based neural synchronization has been proposed for the development of the key exchange protocol. At the time of exchange of sensitive information, intruders can effortlessly perform sniffing, spoofing, phishing, or Man-In-The-Middle (MITM) attack to tamper the vital information. Information needs to be secretly transmitted with high level of encryption by preserving the authentication, confidentiality, and integrity factors. Such stated requirements urge the researchers to develop a neural network-based fast and robust security protocol. A special neural network structure called Double Layer Tree Parity Machine (DLTPM) is proposed for neural synchronization. Two DLTPMs accept the common input and different weight vectors and update the weights using neural learning rules by exchanging their output. In some steps, it results in complete synchronization, and the weights of the two DLTMs become identical. These identical weights serve as a secret key. There is, however, hardly any research in the field of neural weight vector optimization using a nature-inspired algorithm for faster neural synchronization. In this article, whale optimization-based DLTPM is proposed. For faster synchronization, this proposed DLTPM model uses a whale algorithm optimized weight vector. This proposed DLTPM model is faster and has better security. This proposed technique has been passed through a series of parametric tests. The results have been compared with some recent techniques. The results of the proposed technique have shown effective and has robust potential.

Nature Inspired Optimization Techniques, A review for FANETs

Flying Ad Hoc Network (FANET) is a revolutionary achievement in the class of ad hoc networks to access areas near calamities. The concept of pilot free system takes place in technological advancement and computations. Networking advancements introduces Unmanned Aerial System (UAS) while the device that makes this system applicable is known  as flying drones or Unmanned Aerial Vehicle (UAV). Single UAV may be employed as individual system, but it is more useful when UAVs in a network cooperate with each other. UAV deployments are rapid and pliant that reduces the tradition ad hoc network limitations. UAV have small operating structure and efficient energy life. Advance flight control mechanism makes FANETs differ from traditional adhoc networks. FANETs can visualized to play key role where humans are in danger or rescue tasks are too dull or dirty. Improving the performance of flying drones leads to ease-of-use for formation of network, high stability and fast deployment. Routing and cooperative task allocation of nodes is complex in adhoc networks. In contrast of the study of traditional schemes, our paper is based on the nature inspired schemes. This review study in on routing protocols of BeeAdHoc, AntHocNet and Moth-Ant inspired routing. In addition, the overview leads to wild life behavior of Hill Myna and Desert Sparrow Optimization comparative to other biological algorithms is presented in terms of task allocation and rendezvous cooperatively. Biological inspired behaviors trend to know the efficient problem solutions for FANETs.

Selective Recognition of Amino Acids and Peptides by Small Supramolecular Receptors.

To this day, the recognition and high affinity binding of biomolecules in water by synthetic receptors remains challenging, while the necessity for systems for their sensing, transport and modulation persists. This problematic is prevalent for the recognition of peptides, which not only have key roles in many biochemical pathways, as well as having pharmacological and biotechnological applications, but also frequently serve as models for the study of proteins. Taking inspiration in nature and on the interactions that occur between several receptors and peptide sequences, many researchers have developed and applied a variety of different synthetic receptors, as is the case of macrocyclic compounds, molecular imprinted polymers, organometallic cages, among others, to bind amino acids, small peptides and proteins. In this critical review, we present and discuss selected examples of synthetic receptors for amino acids and peptides, with a greater focus on supramolecular receptors, which show great promise for the selective recognition of these biomolecules in physiological conditions. We decided to focus preferentially on small synthetic receptors (leaving out of this review high molecular weight polymeric systems) for which more detailed and accurate molecular level information regarding the main structural and thermodynamic features of the receptor biomolecule assemblies is available.

BIOMIMICRY CONCEPT FROM INTERIOR ARCHITECTURE VIEW

Biomimicry is one of the relatively contemporary concepts with substantial success. The scientific community has numerous academic studies and practical application based on biomimicry with varying degrees of participation. Despite the limited usage of biomimicryby architecture and interior architecture as compared to other fields, architectureand interior architecture have recognized features as the direct link with human and the powerful impact to demonstrate messages from nature. This paper aims to make biomimicry concept more intelligible to interior architecture interested parties, through exploring some essential points about how the biomimicry works. The research includes terminologies about biomimicry and some other related fields, concepts of biomimicry, some expressive applications in the field of architecture and interior architecture, models of direct and indirect nature's inspiration, biomimicry between form and function, the relation between biomimicry and environmental compatibility and protection, thus distinguishing cases with practical details about how biomimicry generates new ideas and encourages creativity.

Integer Linear Programming with Ant-Colony Optimized Algorithm to Extend MANET Lifetime

Network which is randomly sorted out remotely and organization that conveys without framework and experiencing low force battery is called Mobile Ad-hoc Network (MANET). Oneof the main objectives isto get thebest route from source to destination to minimize the node energy consumption. Linear Programming (Integer Linear Programming) and nature inspiration technique (Ant Colony Optimization) are two models that enhance energy consumption.The proposed models in our work are considered extension of our previous works with modified version and different measures [13]. We introduced a modified version for two models. In order to make the performance metric, two measures, namely, energy consumption and processing time are calculated.These measures aredone using as an experimental study. The First model is the enhancedLinear Programming model, the best route is selected from all possible combinatorial routes using the minimum total dissipated energy as an objective function and thefeasible region which satisfies a sequence of constraints. The second model is anenhanced ACO version, based on nature the source is the house of ant and the destination is the location of food.In this work routing selection use Ant-Colony with the Integer Linear Programming (ILP). In the research the Integer Linear Programming (ILP) is proved to be with longer lifetime compared with the ACO approach. In the meanwhile the ILP requires longer time to find the best route more than ACO approach. One of the main drawbacks of the ILP is after specific number of requests the packet losses are increasing. So, to get the best performance, the ILP is used till 500 requests then using ACO to reduce the packet losses and enhance the performance.

COVID-19: risk prediction through nature inspired algorithm

PurposeThe purpose of this study to investigate the effects and possible future prediction of COVID-19. The dataset considered in this study to investigate the effects and possible future prediction of COVID-19 is constrained as follows: age, gender, systolic blood pressure, HDL-cholesterol, diabetes and its medication, does the patient suffered from heart disease or took anti-cough agent food or sensitive to cough related issues and any other chronic kidney disease, physical contact with foreign returns and social distance for the prediction of the risk of COVID-19.Design/methodology/approachThis work implemented a meta-heuristic algorithm on the aforementioned dataset for possible analysis of the risk of being infected with COVID-19. The authors proposed a simple yet effective Risk Prediction through Nature Inspired Hybrid Particle Swarm Optimization and Sine Cosine Algorithm (HPSOSCA), particle swarm optimization (PSO), and sine cosine algorithm (SCA) algorithms.FindingsThe simulated results on different cases discussed in the dataset section reveal which category of individuals may happen to have the disease and of what level. The experimental results reveal that the proposed model can predict the percentage of risk with an overall accuracy of 88.63%, sensitivity (87.23%), specificity (89.02%), precision (69.49%), recall (87.23%), f_measure (77.36%) and Gmean (88.12%) with 41 and 146 true positive and negative, 18 and 6 false positive and negative cases, respectively. The proposed model provides a quite stable prediction of risk for COVID-19 on different categories of individuals.Originality/valueThe work for the very first time developed a novel HPSOSCA model based on PSO and SCA for the prediction of COVID-19 disease. The convergence rate of the proposed model is too high as compared to the literature. It also produces a better accuracy in a computationally efficient fashion. The obtained outputs are as follows: accuracy (88.63%), sensitivity (87.23%), specificity (89.02%), precision (69.49%), recall (87.23%), f_measure (77.36%), Gmean (88.12%), Tp (41), Tn (146), Fb (18) and Fn (06). The recommendations to reduce disease outbreaks are as follow: to control this epidemic in various regions, it is important to appropriately manage patients suspected of having the disease, immediately identify and isolate the source of infection, cut off the transmission route and prevent viral transmission from these potential patients or virus carriers.

Fe3O4 nano assembly embedded in 2D-crumpled porous carbon sheets for high energy density supercapacitor

• The waste garlic husk can act as a carbon (2D-CCS) precursor as well as a solvent . • A novel 0D-Fe 3 O 4 nano assembly and 2D-CCS composite was synthesized with zero-waste generation. • The composite shows outstanding power and energy densities (8000 W kg −1 , 115.5 Wh Kg −1 ) of a supercapacitor. • The 2D crumpled nano-sheet assembly affords high specific capacitance in a supercapacitor application. • This work stimulates a new and universal door converting waste materials to valuable materials. Due to the copious development of industries and depletion of fossil fuels generate deteriorating environmental pollution and appalling economic crisis. Thus, there are increasing unmet for developing well-defined nanostructures to mitigate energy and environmental issues. Herein, we firstly report a unique concerted method to convert agro bio-waste into an economical wealth material. By the inspiration of nature, we selected a waste 2D garlic husk ( Allium sativum ) as a torchbearer to make a unique material. The garlic extract was applied for the synthesis of Fe 3 O 4 NAs@2D-CCS composite nano-assemblies (NAs). Interestingly, the small-sized Fe 3 O 4 nanocrystals (<20 nm) underwent nucleation in the presence of biogenic 2D-CCS and formed a pomegranate like a cluster (200 nm) forming a conglomerate on porous carbon layers. Most importantly, no secondary waste was generated in this work. This Fe 3 O 4 NAs@2D-CCS composite affords high specific capacitance of 820F g −1 at a current density of 0.5 A g −1 , which is nearly 5 to 3 times higher than pristine Fe 3 O 4 NPs (<20 nm) electrode with an outstanding power density (3500–8000 W kg −1 ) and specific energy density (115.5 to 65.9 Wh Kg −1 ), which is superior among most of all reported iron oxide and graphitic carbon composites. This simple work stimulates a new door for the generation of waste to well-defined nanostructure materials for energy, catalysis, and environmental applications.

Nature Inspired and Transform Based Image Encryption Techniques: A Comparative Study

Nature Inspired and Transform Based Image Encryption Techniques: A Comparative Study

A Comparative Evaluation of Nature Inspired Algorithms for Telecommunication Network Design

The subject of the study was an application of nature-inspired metaheuristic algorithms to node configuration optimization in optical networks. The main objective of the optimization was to minimize capital expenditure, which includes the costs of optical node resources, such as transponders and amplifiers used in a new generation of optical networks. For this purpose a model that takes into account the physical phenomena in the optical network is proposed. Selected nature-inspired metaheuristic algorithms were implemented and compared with a reference, deterministic algorithm, based on linear integer programming. For the cases studied the obtained results show that there is a large advantage in using metaheuristic algorithms. In particular, the evolutionary algorithm, the bees algorithm and the harmony search algorithm showed superior performance for the considered data-sets corresponding to large optical networks; the integer programming-based algorithm failed to find an acceptable sub-optimal solution within the assumed maximum computational time. All optimization methods were compared for selected instances of realistic teletransmission networks of different dimensions subject to traffic demand sets extracted from real traffic data.

Nature Plus Plus Inspired Computing - The Superset of Nature Inspired Computing

The term "Nature Plus Plus Inspired Computing" is coined by us in this article. The abbreviation for this new term is "N++IC." Just like the C++ programming language is a superset of C programming language, Nature Plus Plus Inspired Computing (N++IC) field is a superset of the Nature Inspired Computing (NIC) field. We defined and introduced "Nature Plus Plus Inspired Computing Field" in this work. Several interesting opportunities in N++IC Field are shown for Artificial Intelligence Field Scientists and Students. We show a literature review of the N++IC Field after showing the definition of Nature Inspired Computing (NIC) Field. The primary purpose of publishing this innovative article is to show a new path to NIC Field Scientists so that they can come up with various innovative algorithms from scratch. As the focus of this article is to introduce N++IC to researchers across the globe, we added N++IC Field concepts to the Particle Swarm Optimization algorithm and created the "Children Cycle Riding Algorithm (CCR Algorithm)." Finally, results obtained by CCR Algorithm are shown, followed by Conclusions.

Nature Inspired Techniques and Applications in Intrusion Detection Systems: Recent Progress and Updated Perspective

Nowadays, it has become a necessity for operational and reliable operation of networks due to our increased dependency over the network services. However, intruders are continuously attempting to break into the networks and disturbing the network services using a variety of attack vectors and technologies. This motivates us to develop the techniques that ensure operational and reliable network, even in changing scenarios. Recently, most of the researchers have focused on the employment of techniques inspired by a natural phenomenon to detect the intrusions effectively. Nature-Inspired Techniques (NITs) have the ability to adapt to a constantly changing environment. Thus, they help to provide in-built resiliency to failures and damages, collaborative, survivable, self-organizing and self-healing capabilities to IDSs. The paper presents an analysis of NITs, and their classification based on the source of their inspiration. A comprehensive review of various NITs employed in intrusion detection is presented. Analysis of prominent research indicates that NITs based IDSs offers high detection rate and low false positive rate in comparison to the conventional IDSs. The NITs enables more flexibility in IDSs because of their employability into hybrid IDSs leading to detection on the basis of anomalies as well as signatures, leading in improving detection results of known and unknown attacks. The paper attempts to identify NITs’ advantages, disadvantages and significant challenges to the successful implementation of NITs in the intrusion detection area. The main intention of this paper is to explore and present a comprehensive review of the application of NITs in intrusion detection, covering a variety of NITs, study of the techniques and architectures used and further the contribution of NITs in the field of intrusion detection. Finally, the paper ends with the conclusion and future aspects.

Innovative solution to enhance the Helmholtz resonator sound absorber in low-frequency noise by nature inspiration.

Sound absorbing elements commonly used to make quieter spaces are very desirable for high-frequency noise control. It is more attractive if it is possible to produce sound absorbing structures that perform well for a wide frequency range, especially in low-frequencies. In this regard, Helmholtz resonator array is a well-known structure but the maximum performance of each resonator unit in an array panel can only be achieved at resonance frequency that is dependent on its size. Therefore, a resonator array panel with equal size units has insufficient performance in a wide frequency range. Hence, the consideration of suitable sizes for each resonator in an array panel consisting of unequal size units is a key point to widen the frequency range of sound absorption. On the other hand, it seems that engineering science can be developed by nature inspiration. In addition, Fibonacci sequence is a famous mathematical model which describes the shape/ratio of different phenomena which exist in nature. In this paper, Fibonacci sequence is utilized to investigate Helmholtz resonator arrangement to increase both the amount of sound absorption coefficient and frequency bandwidth of absorption without changing the total volume of resonator array with equal size units. Analytical solutions based on the modified equivalent electrical circuit theory is used to calculate the sound absorption coefficient of the proposed panel with numerical experiments in MAPLE and COMSOL Multiphysics softwares to validate the proposed overall approach. Results show that the Fibonacci sequence is an optimum candidate to design a resonator-based sound absorber in low-frequency noise.

An Overview of Few Nature Inspired Optimization Techniques and Its Reliability Applications

Optimization has been a hot topic due to its inevitably in the development of new algorithms in almost every applied branch of Mathematics. Despite the broadness of optimization techniques in research fields, there is always an open scope of further refinement. We present here an overview of nature-inspired optimization with a subtle background of fundamentals and classification and their reliability applications. An attempt has been made to exhibit the contrast nature of multi objective optimization as compared to single objective optimization. Though there are various techniques to achieve the optimality in optimization problems but nature inspired algorithms have proved to be very efficient and gained special attention in modern research problems. The purpose of this article is to furnish the foundation of few nature inspired optimization techniques and their reliability applications to an interested researcher.