Nature-inspired Metaheuristic Optimization Algorithm Research Articles

A Review On Early Detection Of Chronic Kidney Disease

Early detection of Chronic Kidney Disease (CKD) is critical for timely intervention and effective treatment. Deep learning algorithms have demonstrated promise in medical applications, including disease detection. In this study, we propose a deep learning-based system for early CKD detection using the Chronic Kidney Disease dataset from Kaggle. Additionally, we incorporate the Grasshopper Optimization Algorithm (GOA) for feature selection to enhance the system's performance and interpretability. Our system employs a convolutional neural network (CNN) architecture to analyze clinical and laboratory attributes from the CKD dataset, obtained from Kaggle, consisting of 4,000 instances with 25 attributes. These attributes encompass patient demographics, blood tests, and medical history, providing a comprehensive representation of CKD-related factors. To improve the system's performance, we integrate the GOA for feature selection. The GOA is a nature-inspired metaheuristic optimization algorithm that mimics the foraging behavior of grasshoppers. It aims to identify the most relevant attributes associated with CKD from the dataset. By selecting a subset of informative features, we enhance the model's predictive accuracy and reduce overfitting. During the training phase, the CNN learns to automatically extract relevant features and patterns associated with CKD from the selected attributes. Additionally, data preprocessing techniques such as normalization and feature scaling are applied to further improve the model's performance and generalizability. To evaluate the system's performance, we conduct experiments using a separate test dataset comprising 1,000 instances from the CKD dataset. The incorporation of the GOA for feature selection in our deep learning system not only improves its performance but also enhances interpretability. By identifying the most relevant attributes associated with CKD, we focus on key biomarkers and risk factors, enhancing the system's accuracy and providing valuable insights into the disease. Our research showcases the potential of deep learning algorithms, coupled with GOA-based feature selection, for early CKD detection. By leveraging the Kaggle CKD dataset and incorporating the GOA, we contribute to improving the accuracy and applicability of the system in real-world clinical settings. To handle Big data we are proposing to implement this problem on Pyspark one of the Big data computational environments for effective learning. In this platform, we can dynamically scale the infrastructure as per the demand of the data. Ultimately, our work aims to advance the early detection and management of CKD, leading to improved patient outcomes and more effective healthcare interventions.

Yellow Ground Squirrel Algorithm (YGSA): A Novel Metaheuristic Algorithm for Global Optimization

In recent years, numerous methods have emerged for optimizing problems inspired by nature. Each of these methods, according to their nature, has applications for solving specific problems with unique characteristics. Given the wide array of optimization problems across various fields, introducing novel algorithms with distinct behaviors can be both appealing and practical. In this article, a nature-inspired meta-heuristic optimization algorithm called Yellow Ground Squirrel Algorithm (YGSA) is introduced. The main idea behind YGSA is to simulate the natural behavior of a yellow ground squirrel in terms of pursuit and evasion from a farmer (the predator) and reaching the nest. The objective of the proposed algorithm is to enhance and attain a more equitable equilibrium between the fundamental stages of exploration and exploitation in contrast to preceding approaches. This balance arises from the fact that the yellow ground squirrel, during its escape, simultaneously tries to increase the distance from the farmer (the predator) and decrease the distance to its nest (burrow). To validate the proposed algorithm, 56 benchmark evaluation functions from various unimodal and multimodal types have been evaluated. The evaluation results are compared with the HBO, EBO-CMAR, FO, SCA, PSO, CS, GSA, MVO, and WSO algorithms. The optimization results for unimodal test functions demonstrate the high exploitation capability of YGSA in approaching the optimal solution, while the optimization results for multimodal test functions indicate the strong exploration ability of YGSA in finding the main optimal region in the search space.

Energy management and capacity planning of photovoltaic-wind-biomass energy system considering hydrogen-battery storage

This article proposed a Salp Swarm nature-inspired metaheuristic optimization algorithm (SSA) for the energy management and capacity planning of a standalone hybrid photovoltaic wind-biomass-hydrogen-battery energy system. The SSA is used to determine the optimum system configuration that will fulfill the demand reliably considering technical (loss of power supply probability (LPSP)) and economical (annualized system cost (ASC)) aspects. The energy management system (EMS) of the energy system is implemented using a rule-based algorithm to effectively manage the power flow of the devised hybrid energy system components. The comparative evaluation of the algorithms shows that EMS-SSA produces a better result as it offers the least levelized cost of energy (LCOE), of $0.939737/kW h, as compared to the EMS-LFA, EMS-GA and HOMER, which offer LCOE of $0.949737/kW h, $0.958660/kW h and $1.075351/kW h, respectively. Similarly, for the optimal system configuration, the annualized system cost (ASC) is found to be 1.887995 M$. This research presents a viable and environmentally sustainable electrification solution, serving as a valuable reference for making electricity investments in the energy-deficient Northeastern part of Nigeria.

Internet of Robotic Things (IoRT) and Metaheuristic Optimization Techniques Applied for Wheel-Legged Robot

This paper presents the operation of a remotely controlled, wheel-legged robot. The developed Wi-Fi connection framework is established on a popular ARM microcontroller board. The implementation provides a low-cost solution that is in congruence with the newest industrial standards. Additionally, the problem of limb structure and motor speed control is solved. The design process of the mechanical structure is enhanced by a nature-inspired metaheuristic optimization algorithm. An FOC-based BLDC motor speed control strategy is selected to guarantee dynamic operation of the drive. The paper provides both the theoretical considerations and the obtained prototype experimental results.

Multi-area load frequency regulation of a stochastic renewable energy-based power system with SMES using enhanced-WOA-tuned PID controller

This paper presents a novel nature-inspired meta-heuristic optimization algorithm known as the Enhanced Whale Optimization Algorithm (EWOA), which imitates humpback whales' social behavior to solve the optimization of multi-area automatic load frequency control (LFC) problems of a stochastic renewable energy-based power system with superconducting magnetic energy storage (SMES). An EWOA algorithm is presented in response to the limitations of the conventional WOA algorithm, including its sluggish convergence time, low accuracy, and propensity to easily enter local optimum. The system model investigated includes some physical constraints such as the time delay (TD), generation rate constraint (GRC), reheat turbine (RT), and the dead band (DB). The impacts of these physical constraints on the dynamic performance of the proposed controller were investigated. The EWOA algorithm is utilized to dynamically optimize the parameters of the PID controller for optimal system performance. The effectiveness and dynamic performance of the proposed controller are compared with the conventional WOA using some performance metrics. The system model also includes superconducting magnetic energy storage (SMES) units in both areas and their impacts on the system performances are also investigated. The effects of the changes of two different parameters of the system (frequency bias parameter, B, and the governor speed regulation, R) on the frequency deviation responses and the controller's robustness are examined. It is evident from the results that the dynamic performance of the proposed controller is better than that of the conventional WOA and it is more robust and stable to changes in system loading, parameters, and step load perturbation.

Red-tailed hawk algorithm for numerical optimization and real-world problems

This study suggests a new nature-inspired metaheuristic optimization algorithm called the red-tailed hawk algorithm (RTH). As a predator, the red-tailed hawk has a hunting strategy from detecting the prey until the swoop stage. There are three stages during the hunting process. In the high soaring stage, the red-tailed hawk explores the search space and determines the area with the prey location. In the low soaring stage, the red-tailed moves inside the selected area around the prey to choose the best position for the hunt. Then, the red-tailed swings and hits its target in the stooping and swooping stages. The proposed algorithm mimics the prey-hunting method of the red-tailed hawk for solving real-world optimization problems. The performance of the proposed RTH algorithm has been evaluated on three classes of problems. The first class includes three specific kinds of optimization problems: 22 standard benchmark functions, including unimodal, multimodal, and fixed-dimensional multimodal functions, IEEE Congress on Evolutionary Computation 2020 (CEC2020), and IEEE CEC2022. The proposed algorithm is compared with eight recent algorithms to confirm its contribution to solving these problems. The considered algorithms are Farmland Fertility Optimizer (FO), African Vultures Optimization Algorithm (AVOA), Mountain Gazelle Optimizer (MGO), Gorilla Troops Optimizer (GTO), COOT algorithm, Hunger Games Search (HGS), Aquila Optimizer (AO), and Harris Hawks optimization (HHO). The results are compared regarding the accuracy, robustness, and convergence speed. The second class includes seven real-world engineering problems that will be considered to investigate the RTH performance compared to other published results profoundly. Finally, the proton exchange membrane fuel cell (PEMFC) extraction parameters will be performed to evaluate the algorithm with a complex problem. The proposed algorithm will be compared with several published papers to approve its performance. The ultimate results for each class confirm the ability of the proposed RTH algorithm to provide higher performance for most cases. For the first class, the RTH mostly got the optimal solutions for most functions with faster convergence speed. The RTH provided better performance for the second and third classes when resolving the real word engineering problems or extracting the PEMFC parameters.

Intrusion detection in internet of things-based smart farming using hybrid deep learning framework

Smart agriculture is a popular domain due to its intensified growth in recent times. This domain aggregates the advantages of several computing technologies, where the IoT is the most popular and beneficial. In this work, a novel and effective deep learning-based framework is developed to detect intrusions in smart farming systems. The architecture is three-tier, with the first tier being the sensor layer, which involves the placement of sensors in agricultural areas. The second tier is the Fog Computing Layer (FCL), which consists of Fog nodes, and the proposed IDS is implemented in each Fog node. The gathered information is transferred to this fog layer for further data analysis. The third tier is the cloud computing layer, which provides data storage and end-to-end services. The proposed model includes a fused CNN model with the bidirectional gated recurrent unit (Bi-GRU) model to detect and classify intruders. An attention mechanism is included within the BiGRU model to find the key features responsible for identifying the DDoS attack. In addition, the accuracy of the classification model is improved by using a nature-inspired meta-heuristic optimization algorithm called the Wild Horse Optimization (WHO) algorithm. The last layer is the cloud layer, which collects data from fog nodes and offers storage services. The proposed system will be implemented in the Python platform, using ToN-IoT and APA-DDoS attack datasets for assessment. The proposed system outperforms the existing methods in accuracy (99.35%), detection rate (98.99%), precision (99.9%) and F-Score (99.08%) for the APA DDoS attack dataset and the achieved accuracy of the ToN-IoT dataset (99.71%), detection rate (99.02%), precision (99.89%) and F-score (99.05%).

Nature-inspired metaheuristic optimization algorithms for urban transit routing problem

Designing urban transportation networks is a technological and social reality defined as a decentralized polynomial NP-Hard problem. In order to efficiently resolve this issue, several nature-inspired metaheuristic approaches have been proposed. Although these methods are crucial to resolving urban transportation network challenges, no comprehensive study has been conducted to address these concerns. Thus, this study provides an in-depth examination of nature-inspired metaheuristic methods applied to urban transmission. The current study aims to demonstrate the significant benefits of optimization algorithms in solving the Urban Transit Routing Problem (UTRP).

White Blood Cells Classification Using Entropy-Controlled Deep Features Optimization.

White blood cells (WBCs) constitute an essential part of the human immune system. The correct identification of WBC subtypes is critical in the diagnosis of leukemia, a kind of blood cancer defined by the aberrant proliferation of malignant leukocytes in the bone marrow. The traditional approach of classifying WBCs, which involves the visual analysis of blood smear images, is labor-intensive and error-prone. Modern approaches based on deep convolutional neural networks provide significant results for this type of image categorization, but have high processing and implementation costs owing to very large feature sets. This paper presents an improved hybrid approach for efficient WBC subtype classification. First, optimum deep features are extracted from enhanced and segmented WBC images using transfer learning on pre-trained deep neural networks, i.e., DenseNet201 and Darknet53. The serially fused feature vector is then filtered using an entropy-controlled marine predator algorithm (ECMPA). This nature-inspired meta-heuristic optimization algorithm selects the most dominant features while discarding the weak ones. The reduced feature vector is classified with multiple baseline classifiers with various kernel settings. The proposed methodology is validated on a public dataset of 5000 synthetic images that correspond to five different subtypes of WBCs. The system achieves an overall average accuracy of 99.9% with more than 95% reduction in the size of the feature vector. The feature selection algorithm also demonstrates better convergence performance as compared to classical meta-heuristic algorithms. The proposed method also demonstrates a comparable performance with several existing works on WBC classification.

Moth Search: Variants, Hybrids, and Applications

Moth search (MS) is a nature-inspired metaheuristic optimization algorithm based on the most representative characteristics of moths, Lévy flights and phototaxis. Phototaxis signifies a movement which organism towards or away from a source of light, which is the representative features for moths. The best moth individual is seen as the light source in Moth search. The moths that have a smaller distance from the best one will fly around the best individual by Lévy flights. For reasons of phototaxis, the moths, far from the fittest one, will fly towards the best one with a big step. These two features, Lévy flights and phototaxis, correspond to the processes of exploitation and exploration for metaheuristic optimization. The superiority of the moth search has been demonstrated in many benchmark problems and various application areas. A comprehensive survey of the moth search was conducted in this paper, which included the three sections: statistical research studies about moth search, different variants of moth search, and engineering optimization/applications. The future insights and development direction in the area of moth search are also discussed.

The application of nature-inspired optimization algorithms on the modern management: A systematic literature review and bibliometric analysis

AbstractWith the expanding adoption of technology and intelligent applications in every aspect of our life, energy, resource, data, and product management are all improving. So, modern management has recently surged to cope with modern societies. Numerous optimization approaches and algorithms are used to effectively optimize the literature while taking into account its many restrictions. With their dependability and superior solution quality for overcoming the numerous barriers to generation, distribution, integration, and management, nature-inspired meta-heuristic optimization algorithms have stood out among these methods. Hence, this article aims to review the application of nature-inspired optimization algorithms to modern management. Besides, the created clusters introduce the top authors in this field. The results showed that nature-inspired optimization algorithms contribute significantly to cost, resource, and energy efficiency. The genetic algorithm is also the most important and widely used method in the previous literature.

Automatic Data Clustering by Hybrid Enhanced Firefly and Particle Swarm Optimization Algorithms

Data clustering is a process of arranging similar data in different groups based on certain characteristics and properties, and each group is considered as a cluster. In the last decades, several nature-inspired optimization algorithms proved to be efficient for several computing problems. Firefly algorithm is one of the nature-inspired metaheuristic optimization algorithms regarded as an optimization tool for many optimization issues in many different areas such as clustering. To overcome the issues of velocity, the firefly algorithm can be integrated with the popular particle swarm optimization algorithm. In this paper, two modified firefly algorithms, namely the crazy firefly algorithm and variable step size firefly algorithm, are hybridized individually with a standard particle swarm optimization algorithm and applied in the domain of clustering. The results obtained by the two planned hybrid algorithms have been compared with the existing hybridized firefly particle swarm optimization algorithm utilizing ten UCI Machine Learning Repository datasets and eight Shape sets for performance evaluation. In addition to this, two clustering validity measures, Compact-separated and David–Bouldin, have been used for analyzing the efficiency of these algorithms. The experimental results show that the two proposed hybrid algorithms outperform the existing hybrid firefly particle swarm optimization algorithm.

Learning Optimized Patterns of Software Vulnerabilities with the Clock-Work Memory Mechanism

It is possible to better provide the security of the codebase and keep testing efforts at a minimum level by detecting vulnerable codes early in the course of software development. We assume that nature-inspired metaheuristic optimization algorithms can obtain “optimized patterns” from vulnerabilities created in an artificial manner. This study aims to use nature-inspired optimization algorithms combining heterogeneous data sources with the objective of learning optimized representations of vulnerable source codes. The chosen vulnerability-relevant data sources are cross-domain, involving historical vulnerability data from variable software projects and data from the Software Assurance Reference Database (SARD) comprising vulnerability examples. The main purpose of this paper is to outline the state-of-the-art and to analyze and discuss open challenges with regard to the most relevant areas in the field of bio-inspired optimization based on the representation of software vulnerability. Empirical research has demonstrated that the optimized representations produced by the suggested nature-inspired optimization algorithms are feasible and efficient and can be transferred for real-world vulnerability detection.

OPTIMAL PROTECTION COORDINATION OF RELAYS IN PRESENCE OF DISTRIBUTED GENERATION USING WHALE ALGORITHM

“This paperis proposes a novel nature-inspired meta-heuristic optimization algorithm, called Whale Optimization Algorithm (WOA), which mimics the social behavior of the humpback whales. The algorithm is inspired by the bubble-net hunting strategy”which is good comparing to others. WOA is tested with 29 mathematical optimization problems and structural design problems. Optimization results prove that the WOA algorithm is very competitive compared to the state the of art meta heuristic algorithms as well as conventional methods

BioWSN: A Bio-Inspired Method for Optimization of Routing in Wireless Sensor Networks

Wireless sensor networks (WSN) have been recently gaining traction for many applications in monitoring and surveillance systems in the physical world specifically in agriculture, healthcare, and smart cities. Many clustering and routing approaches have been introduced to reduce the consumption of energy in WSNs to increase the lifetime of the network. In this study, we propose an improved version of grey wolf optimizer (GWO), a nature-inspired metaheuristic optimization algorithm, to perform cluster head selection and routing in WSN while maximizing the lifetime of WSN. GWO has a propensity to converge to local optima. To overcome this drawback of the conventional GWO, we introduce a balancing factor between the exploration and exploitation phases of the algorithm in addition to a mapping scheme. Comparative simulation and analysis of the proposed algorithm show significant improvement compared to frequently used and well-known approaches namely LEACH and PSO.

Identification of material properties of green laminate composite plates using bio-inspired optimization algorithms

This work proposes a non-destructive method for the identification of material properties of composite materials. The proposed optimization problems have for design variables the material elastic constants and make use of nature-inspired metaheuristic optimization algorithms. The objective functions relate experimental natural frequencies with computationally obtained ones. The nature-inspired metaheuristic optimization algorithms used are the: (1) Genetic algorithm, (2) Particle Swarm Optimization algorithm, (3) Grey Wolf Optimization algorithm, (4) Firefly algorithm, and (5) Cuckoo Search algorithm. The study is focused on laminated composite materials, whether they are synthetic fiber reinforced, such as glass fibers reinforced composites, or natural fibers reinforced like wooden fibers reinforced composites and plywood. The proposed method allows the identification of the elastic constants within an acceptable range compared to other methods, provided that enough natural frequencies are accurately measured. This method presents several advantages in comparison to other methods: (1) it does not require an initial guess of the elastic constants, (2) it does not need the gradient of the objective functions, and (3) it allows the identification of a large range of elastic constants of different materials due to its good adaptability and versatility.

K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

K-means clustering algorithm is a partitional clustering algorithm that has been used widely in many applications for traditional clustering due to its simplicity and low computational complexity. This clustering technique depends on the user specification of the number of clusters generated from the dataset, which affects the clustering results. Moreover, random initialization of cluster centers results in its local minimal convergence. Automatic clustering is a recent approach to clustering where the specification of cluster number is not required. In automatic clustering, natural clusters existing in datasets are identified without any background information of the data objects. Nature-inspired metaheuristic optimization algorithms have been deployed in recent times to overcome the challenges of the traditional clustering algorithm in handling automatic data clustering. Some nature-inspired metaheuristics algorithms have been hybridized with the traditional K-means algorithm to boost its performance and capability to handle automatic data clustering problems. This study aims to identify, retrieve, summarize, and analyze recently proposed studies related to the improvements of the K-means clustering algorithm with nature-inspired optimization techniques. A quest approach for article selection was adopted, which led to the identification and selection of 147 related studies from different reputable academic avenues and databases. More so, the analysis revealed that although the K-means algorithm has been well researched in the literature, its superiority over several well-established state-of-the-art clustering algorithms in terms of speed, accessibility, simplicity of use, and applicability to solve clustering problems with unlabeled and nonlinearly separable datasets has been clearly observed in the study. The current study also evaluated and discussed some of the well-known weaknesses of the K-means clustering algorithm, for which the existing improvement methods were conceptualized. It is noteworthy to mention that the current systematic review and analysis of existing literature on K-means enhancement approaches presents possible perspectives in the clustering analysis research domain and serves as a comprehensive source of information regarding the K-means algorithm and its variants for the research community.

Discrete sizing design of steel truss bridges through teaching-learning-based and biogeography-based optimization algorithms involving dynamic constraints

In this paper, Teaching-Learning Based Optimization (TLBO) and Biogeography-Based Optimization (BBO) algorithms are presented to examine the optimum discrete sizing design of steel truss steel bridges for minimizing the structural weights. Both proposed nature-inspired metaheuristic optimization algorithms are encoded in MATLAB with integration of a structural analysis program (SAP2000) via open application programming interface (OAPI). At the end, optimal steel profiles are selected from available discrete section lists by satisfying the structural restrictions, such as stress and displacement, involved by American Institute of Steel Construction-Allowable Stress Design (AISC-ASD). Additional to these, optimum discrete sizing design process is performed for the cases with and without dynamic constraints, which are adopted from natural periods of the bridge structures with respect to the mode shapes. The algorithmic performance of the proposed algorithms outperforms on both planar and spatial steel truss bridges. To prove this obtained optimal solutions are compared with previously reported optimum designs attaining via different metaheuristics. The final optimum discrete sizing designs of the steel truss bridges reveal that the proposed TLBO and BBO algorithms can easily be applied to discrete nonlinear programming problems.

Control of electric machines using flower pollination algorithm based fractional order PID controller

In this work, fractional order proportional integral derivative controller (FOPIDC) is proposed and implemented for speed control of electric machines using nature-inspired metaheuristic optimization algorithm (MOA) named as flower pollination algorithm (FPA). Investigations are performed by considering the second-order transfer function of D.C Motor. FOPIDC constants KP, KI, KD, λ, and μ are optimized by considering FPA. The desired response of the electric machine is obtained by using tuned parameters of FOPIDC. After that, FOPIDC response using FPA is compared with PIDC response using FPA in the sense of time-domain specifications and performance indices (PI) like ‘integral of square error (ISE), integral of time-weighted square error (ITSE), integral of absolute error (IAE) and integral of time-weighted absolute error (ITAE)’. Finally, the desired response of electric machine using FPA based FOPIDC is compared with desired response of electric machine using firefly algorithm (FA) and particle swarm optimization (PSO) algorithm based FOPIDC.

Prediction of the critical temperature of a superconductor by using the WOA/MARS, Ridge, Lasso and Elastic-net machine learning techniques

This study builds a predictive model capable of estimating the critical temperature of a superconductor from experimentally determined physico-chemical properties of the material (input variables): features extracted from the thermal conductivity, atomic radius, valence, electron affinity and atomic mass. This original model is built using a novel hybrid algorithm relied on the multivariate adaptive regression splines (MARS) technique in combination with a nature-inspired meta-heuristic optimization algorithm termed the whale optimization algorithm (WOA) that mimics the social behavior of humpback whales. Additionally, the Ridge, Lasso and Elastic-net regression models were fitted to the same experimental data for comparison purposes. The results of the current investigation indicate that the critical temperature of a superconductor can be successfully predicted using this proposed hybrid WOA/MARS-based model. Furthermore, the results obtained with the Ridge, Lasso and Elastic-net regression models are clearly worse than those obtained with the WOA/MARS-based model.