Nature Inspired Research Articles

Advancing Multi-Robot Path Planning through Nature Inspired Algorithms

In multi-robot systems, robots need to move efficiently without colliding with each other, which is called path planning. The challenge is to find the best way for multiple robots to reach their destinations while avoiding obstacles and each other. Traditional methods can struggle with complex environments, so researchers are turning to nature-inspired computing and machine learning algorithms to solve this problem. Nature-inspired algorithms, like those based on the behaviour of ants, bees, or birds, Firefly Algorithm (FA)[1], Bat Algorithm [2]help robots make decisions similar to how animals navigate in the natural world. These techniques can optimize robot paths by mimicking processes found in nature, such as how ants find the shortest route to food or how birds flock together without crashing [3]. Machine learning algorithms, on the other hand, enable robots to learn from their environment and past experiences [4]. By continuously learning, robots can adapt and improve their path planning over time [5]. Combining these two approaches – nature-inspired computing and machine learning – offers a powerful way to tackle the complex problem of multi-robot path planning. This study explores how these advanced techniques can work together to make robot teams more efficient, reducing travel time, avoiding collisions, and navigating through dynamic environments.

Design Optimization of Two Stage Spur Gear Box with Critical Mechanical and Tribological Constraints by Nature Inspired Algorithms

Design Optimization of Two Stage Spur Gear Box with Critical Mechanical and Tribological Constraints by Nature Inspired Algorithms

A Survey on Parallel Nature Inspired Algorithms

A Survey on Parallel Nature Inspired Algorithms

Mechanical Stretching of Nature Inspired Linear and Branched Polysaccharide Motifs through Steered Molecular Dynamics.

A variety of 10 polysaccharide motifs comprising naturally inspired sequences of monosaccharide building blocks are studied to understand the inter-relation between their structural and mechanical behaviors. Equilibrium and steered molecular dynamics (SMD) simulations are employed to investigate the stress-strain relationships and the associated conformational flips of the pyranose moieties along the polysaccharide chains. The presence of a variety of glycosidic linkages connecting the diverse monosaccharide units along with chain-branching in some cases induce wide diversity in the carbohydrate-Ramachandran plots of the glycosidic dihedrals. Similar variations are observed in the Cremer-Pople ring puckering patterns across the polysaccharide variants. The work further provides a comparison between the experimentally obtained atomic force microscopic data of mechanical stretching for some polysaccharides with the stress-strain curves generated from our SMD simulations. Out of all the systems studied, pectin having an axial-axial orientation of the glycosidic linkage showed maximum stretching potential, while acetan-M, with an equatorial-equatorial disposition of the glycosidic bond, stretched the least. The experimental Young's modulus of the corresponding natural polysaccharides could be reasonably compared to the values obtained from our simulation models. Force distribution analysis is done to understand the propagation of punctual stress in the polysaccharides under SMD conditions. Changes in local electrophilicity or nucleophilicity of atomic centers in puckered pyranose rings are estimated through the condensed Fukui functions. All of this information can help understand the physical behavior and chemical reactivity of complex polysaccharides in a complicated milieu of electronic and steric effects experienced by them.

An Ensemble Model for Predicting Cardiovascular Disease utilizing Nature Inspired Optimization

قدم هذا البحث نموذجًا تنبؤيًا مبتكرًا لأمراض القلب، تم تحسينه من خلال تطبيق منهجية تحسين أفضل التقنيات المتاحة. من المعترف به عالميًا أن انتشار أمراض القلب والأوعية الدموية، خاصة في الدول التي تواجه تحديات اقتصادية، هو مساهم كبير في معدلات الوفيات العالمية.يتم التأكيد على ضرورة التنبؤ الدقيق بالنتائج في الوقت المناسب من خلال ضرورة التخفيف من العواقب الوخيمة المحتملة المرتبطة بهذا التحدي الصحي المحدد. تتضمن المنهجية اختيار الميزات من خلال المعلومات المتبادلة، يليها التخلص من العناصر الخارجية باستخدام نهج Inter Quartile Range أثناء تنقية البيانات.لمعالجة التحيز المحتمل من اختلافات المقياس المتغير، يتم تنفيذ توحيد الميزات باستخدام طريقة Standard Scaler. يتم استخدام تقنية تعزيز التدرج لتطوير النماذج، المعروفة بقدرتها على إدارة البيانات المفقودة وإنشاء تنبؤات دقيقة. لمزيد من تعزيز الأداء، تم تقديم خوارزمية BAT، مع الاستفادة من التحسين المستوحى من الطبيعة. وقد أدى تطبيق أسلوب أفضل التقنيات المتاحة في هذا النموذج بالذات إلى تحسن ملحوظ في الأداء، مما أدى إلى معدل دقة قدره 84.94%. وكانت درجات الدقة والنوعية والحساسية للنموذج 76.47%، 81.88%، و89.65%، على التوالي. وتشير هذه المقاييس مجتمعة إلى أداء متوازن.

Nature Inspired Practices in Super Tall Building Designs: Similarities of Form

Nowadays, competition between cities for super tall buildings is increasing. These buildings exceed the 300-meter height threshold, reaching enormous dimensions and playing a decisive role in the iconic character, visual impact, and prestige, and economic growth, technological and architectural progress of cities. In this context, the aim of the study is to formally analyze super tall buildings inspired by nature. In the study, which is evaluated according to two variables, super tall buildings are analyzed within the scope of the search for inspiration from nature in their form and interior formations. The study involves a content analysis of the ten tallest buildings in the Council on Tall Buildings and Urban Habitat (CTBUH) database. The formal analysis of the buildings revealed that the nature-inspired approach significantly influenced the design of the super tall buildings. This study emphasizes that combining super-tall designs with nature-inspired principles can create a new paradigm in architectural design. This combination improves the quality of life of individuals living in urban areas by enabling them to establish a closer relationship with nature. In this context, nature-inspired super tall buildings promote physical and mental well-being and create spaces that enhance indoor comfort.

Nature Inspired Metaheuristic based Optimization

This paper is a comprehensive study on nature inspired Hyperparameter optimization, with a distinct focus on Honey Badger Algorithm, along with Aquila Optimizer algorithm. The study involves in-depth analysis of the above algorithms, their weaknesses and strengths and comparing them with the theoretical advantages. The implementation of these algorithms, This paper demonstrate the promise of these algorithms on optimization of Hyperparameters like learning rate, number of hidden layers for our various datasets. The findings of this paper show that HBA and Aquila Optimization algorithms offer potential alternatives to the existing approaches, providing more effective and efficient solutions for hyperparameter optimization. This paper contributes to ongoing discourse on the place of nature inspired algorithms and their place in solutions to unconventional places

A Study of Deployable Structures Based on Nature Inspired Curved-Crease Folding.

Fascinating 3D shapes arise when a thin planar sheet is folded without stretching, tearing or cutting. The elegance amplifies when the fold/crease is changed from a straight line to a curve, due to the association of plastic deformation via folding and elastic deformation via bending. This results in the curved crease working as a hinge support providing deployability to the surface which is of significant interest in industrial engineering and architectural design. Consequently, finding a stable form of curved crease becomes pivotal in the development of deployable structures. This work proposes a novel way to evaluate such curves by taking inspiration from biomimicry. For this purpose, growth mechanism in plants was observed and an analogous model was developed to create a discrete curve of fold. A parametric model was developed for digital construction of the folded models. Test cases were formulated to compare the behavior of different folded models under various loading conditions. A simplified way to visualize the obtained results is proposed using visual programming tools. The models were further translated into physical prototypes with the aid of 3D printing, hybrid and cured-composite systems, where different mechanisms were adopted to achieve the folds. The prototypes were further tested under constrained boundary and compressive loading conditions, with results validating the analytical model.

Nature Inspired Discovery and Development of Antibacterials: An Update

The growing prevalence of pathogens resistant to several drugs poses an urgent health concern, which can only be addressed by increasing support for the research and development of newer antibiotics. Drug discovery through the exploration of nature's treasures is a successful and ongoing endeavor. However, the conventional method of discovering new drugs from natural sources has become an adjunct choice of the pharmaceutical corporations since it is time-consuming, complicated, expensive, and fraught with uncertainty. The application of several computational techniques, including combinatorial chemistry, metagenomics, and high throughput screening, has accelerated the drug development process simultaneously reducing costs and time. It is a well-established fact that synthetic drugs generated through computer-aided research are frequently linked to a restricted spectrum of pharmacological activity and adverse side effects as compared to treatments that are either directly or indirectly derived from natural sources. Hence, an innovative drug discovery program may be centered on the virtual design of molecules using a scaffold with a natural origin in order to develop effective and affordable therapies with a wide variety of functionality

Nature Inspired Optimization with Deep Learning Enabled Sustainable Predictive Model for intelligent Systems

Nature Inspired Optimization with Deep Learning Enabled Sustainable Predictive Model for intelligent Systems

Design Optimization of Manifold Microchannel Heat Sink using Evolutionary Algorithms

In today’s world, miniaturized products are proved to be the dis-ruptive technologies contributing to the sustainability through green energy. Microchannel heat sink (MCHS) is an advanced cooling device to accom-plish the cooling requirements for such miniaturized products through sus-tainable approach. In this work, two popular Nature Inspired Swarm Intelli-gence algorithms viz. Teaching Learning Based Optimization (TLBO) and Particle swarm optimization (PSO) are applied for optimizing the perfor-mance of MCHS through maximizing the Thermal resistance & Minimizing the Pumping Power of MCHS. Results are compared with the numerical analysis and GA. For the objective function thermal resistance, results of TLBO and PSO algorithms are improved by 8 % as compared with numeri-cal solutions. For pumping power problem, significant improvement in the results viz. 90.86% is observed with TLBO and PSO algorithm respectively. This optimized design can be directly adopted and it ensures the optimized cooling through equal sharing of thermal load by every channel and thereby minimizing the pump energy consumption. This work demonstrates the ap-plicability of contemporary Nature Inspired Artificial Intelligence (AI) based algorithms in the domain of Heat Sinks and a step towards a green energy.

Advances in biomimetic nanomaterial delivery systems: harnessing nature's inspiration for targeted drug delivery.

The properties of nanomaterials make them promising and advantageous for use in drug delivery systems, but challenges arise from the immune system's recognition of exogenous nanoparticles, leading to their clearance and reduced targeting efficiency. Drawing inspiration from nature, this paper explores biomimetic strategies to transform recognizable nanomaterials into a "camouflaged state." The focal point of this paper is the exploration of bionic nanoparticles, with a focus on cell membrane-coated nanoparticles. These biomimetic structures, particularly those mimicking red blood cells (RBCs), white blood cells (WBCs), platelets, and cancer cells, demonstrate enhanced drug delivery efficiency and prolonged circulation. This article underscores the versatility of these biomimetic structures across diverse diseases and explores the use of hybrid cell membrane-coated nanoparticles as a contemporary trend. This review also investigated exosomes and protein bionic nanoparticles, emphasizing their potential for specific targeting, immune evasion, and improved therapeutic outcomes. We expect that this continued development based on biomimetic nanomaterials will contribute to the efficiency and safety of disease treatment.

Nature Inspired Algorithms for Improving Underwater Object Detection

Nature Inspired Algorithms for Improving Underwater Object Detection

Nature Inspired Optimization in Context-Aware-Based Coronary Artery Disease Prediction: A Novel Hybrid Harris Hawks Approach

Nature Inspired Optimization in Context-Aware-Based Coronary Artery Disease Prediction: A Novel Hybrid Harris Hawks Approach

Adaptive Network Sustainability and Defense Based on Artificial Bees Colony Optimization Algorithm for Nature Inspired Cyber Security

Adaptive Network Sustainability and Defense Based on Artificial Bees Colony Optimization Algorithm for Nature Inspired Cyber Security

Enhancement of Fog Caching Using Nature Inspiration Optimization Technique Based on Cloud Computing

Enhancement of Fog Caching Using Nature Inspiration Optimization Technique Based on Cloud Computing

Differentiating Parkinson's Disease from other Neuro Diseases and Diagnosis using Deep Learning with Nature Inspired Algorithms and Ensemble Learning

A highly accurate automated approach using a deep structured neural network was proposed to detect Parkinson's disease through voice samples. This cost-effective and non-invasive method aims to enhance diagnostic precision and assess the disease's stage of progression. The study addresses two classification problems: binary and multi-category classification. The binary classification deep structured neural network achieved a diagnostic accuracy of 97.6%. For multiclass classification, a Deep Convolution Neural Network (DCNN) and a K- nearest neighbor technique for benchmarking, were utilized using a shared database. Nature Inspired Algorithm (NIA)-Genetic Algorithm was employed to optimize the initial feature set. The suggested deep structure neural network demonstrated a 93.7% accuracy in estimating the disease's stage, showing promising results. Further investigations are encouraged to explore the model's adaptability and pursue improved outcomes.

Nature-Inspired Metaheuristic Algorithm with deep learning for Healthcare Data Analysis

<abstract> <p>Cardiovascular disease (CVD) detection using deep learning (DL) includes leveraging advanced neural network (NN) models to analyze medical data, namely imaging, electrocardiograms (ECGs), and patient records. This study introduces a new Nature Inspired Metaheuristic Algorithm with Deep Learning for Healthcare Data Analysis (NIMADL-HDA) technique. The NIMADL-HDA technique examines healthcare data for the recognition and classification of CVD. In the presented NIMADL-HDA technique, Z-score normalization was initially performed to normalize the input data. In addition, the NIMADL-HDA method made use of a barnacle mating optimizer (BMO) for the feature selection (FS) process. For healthcare data classification, a convolutional long short-term memory (CLSTM) model was employed. At last, the prairie dog optimization (PDO) algorithm was exploited for the optimal hyperparameter selection procedure. The experimentation outcome analysis of the NIMADL-HDA technique was verified on a benchmark healthcare dataset. The obtained outcomes stated that the NIMADL-HDA technique reached an effectual performance over other models. The NIMADL-HDA method provides an adaptable and sophisticated solution for healthcare data analysis, aiming to improve the interpretability and accuracy of the algorithm in terms of medical applications.</p> </abstract>

A Nature Inspired Optimization Algorithm for Parkinson's Disease Classification Through Speech Analysis

This research takes a unique approach to improve the accuracy of Parkinson's disease prediction through speech signal analysis by turning to the natural world for inspiration. This research proposes an optimization algorithm modelled on the hunting strategies of eagles. This approach begins with the extraction of Mel-frequency cepstral coefficients (MFCCs) from the audio signals. Subsequently, the proposed algorithm, inspired by the keen eyesight and hunting strategy of eagles, is employed to select the most relevant MFCC features. This optimization ensures a reduced dimensionality while maintaining the integrity and relevance of the feature set. Eagle's highly effective three-pronged approach to hunting - consisting of soaring (exploring), scanning (assessing), and swooping (taking action) - is the starting point for this method. To put this into practice, the proposed algorithm treats each potential combination of audio signal features as a 'solution', or 'prey'. The usefulness or 'fitness' of each solution is evaluated according to how accurately it can predict Parkinson's disease using a Support Vector Machine (SVM) classifier on a validation dataset. At the start, our algorithm generates an array of solutions. It then takes an 'eagle's eye view', assessing and refining these solutions in successive iterations. This mimics the eagle's hunt, where it identifies its target from a distance, before swooping in to capture it. By testing the proposed eagle-inspired algorithm on the Parkinson's Disease Voice Initiative (PDVI) dataset, it was found that it offers better accuracy in differentiating between those with Parkinson's and those without, compared to traditional methods. Specifically, using the proposed algorithm's selected features, the SVM model achieved an accuracy of 93.2% compared to an 88.5% accuracy when using all MFCC features. Furthermore, the computation time was reduced by 15% due to the dimensionality reduction introduced by the proposed algorithm. In conclusion, this study opens up a promising new direction in the early detection and treatment of Parkinson's disease, demonstrating the value of looking to nature for solving complex problems.

Nature-inspired recycling of a protein mixture into a green fluorescent protein-based hydrogel.

Protein-based materials are biocompatible and have a variety of remarkable properties; consequently, they are finding more and more applications. Nature recycles proteins in multiple ways, ranging from bio-degradation (a slow approach) to fast recycling of protein metabolism. The latter is a wonderful example because a random mixture of proteins gets digested into amino acids (AAs), the fundamental building blocks of proteins. These AAs are then used by cells to produce whichever protein is needed at the time of synthesis. Seen through the lens of recycling, this process transforms a random mixture into something not necessarily present at the start but needed at the moment of recycling. We have recently shown that the process of protein recycling can be performed in vitro and called it NaCRe (Nature Inspired Circular Recycling). In a previous NaCRe proof-of-concept experiment, we started with various protein mixtures but were able to produce only small quantities of recycled protein, in the microgram scale. Here, we show that NaCRe can be used to convert milligrams of a protein mixture containing one of the most common protein materials (silk) into a milligram of an hydrogel made of green fluorescent protein (GFP). We show that in order for NaCRe to be efficient the starting protein mixture must contain a good balance of all AAs and discuss the challenges encountered when scaling up NaCRe.