Bio-inspired Optimization Research Articles

Adaptive fuzzy integrated LMS based control strategy for voltage power quality enhancement

In this paper, an adaptive Takagi Sugeno (TS) Fuzzy integrated Mixed Order Normalized Least Mean Square/Fourth Algorithm (MONLMS/F) is proposed for the unresolved voltage power quality issues in distribution system. The proposed control utilizes the cost function of mixed order of errors, which are based on second and fourth order and the feature of cost function overcomes the demerit of traditional basic least mean square method, which is based on second-order error. The implemented control technique has two main objectives. Firstly, the fundamental estimation of correct active and reactive signals from polluted grid voltage for computing the reference load voltages and second is the DC link voltage regulation. A correct weight estimation is achieved by Mixed Order Normalized Least Mean Square/Fourth Algorithm (MONLMS/F) and voltage regulation is performed by Takagi Sugeno Fuzzy (TSF) approach. The proposed integrated control enhances the capability of system against voltage deviation from the reference voltage and successfully eliminates the harmonics from polluted grid voltages. A bio-inspired optimizer like Spotted Hyena (SH) is integrated with TSF for self-tuning of the membership functions (MFs) and fuzzy rule to adapt the system variations and regulate fluctuations in the DC link for dynamic conditions. However, auto-tunned TS-Fuzzy have a fast transient response of DC and AC link voltage based on dynamic indicators like settling time (ts=0.12 s), undershoot (Us=3.4 %) and peak overshoot (Mp=5.6 %) are compared to classical PI controller. Nonetheless, the proposed TS-MONLMS/F control strategy outperforms the other classical control with low oscillations at any power quality scenario with improved dynamic response. The significant MATLAB simulation and experimental results are evaluated, and its performance is demonstrated with the dSPACE (DS1202) processor for experimental realization.

Development of optimised software fault prediction model using machine learning

Software fault prediction is a crucial task, especially with the rapid improvements in software technology and increasing complexity of software. As identifying and addressing bugs early in the development process can significantly minimize the costs and enhance the software quality. Software fault prediction using machine learning algorithms has gained significant attention due to its potential to improve software quality and save time in the testing phase. This research paper investigates the impact of classification models on bug prediction performance and explores the use of bio-inspired optimization techniques to enhance model results. Through experiments, it is demonstrated that applying bio-inspired algorithms improves the accuracy of fault prediction models. The evaluation is based on multiple performance metrics and the results show that KNN with BACO (Binary Ant Colony Optimization) generally outperform the other models in terms of accuracy. The BACO-KNN fault prediction model attains the accuracy of 96.39% surpassing the previous work.

Optimal Deployment of Wireless Sensor Nodes using Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO)

Abstract: Wireless Sensor Networks (WSNs) are emerging as a significant area of research due to their potential to autonomously monitor physical and environmental conditions. These networks comprise spatially distributed, low-cost sensor nodes with limited transmission range, processing capabilities, storage, and energy resources. The primary function of these networks is to collect data from various nodes and transmit it to a base station for subsequent processing. WSNs present several challenges, including optimal sensor deployment, node localization, base station placement, target node location, energy-aware clustering, and data aggregation. Recently, global researchers have been employing a bio-inspired optimization algorithm, Particle Swarm Optimization (PSO), to enhance the efficiency of WSNs. This report explores the application of the PSO algorithm for optimal sensor deployment in WSNs, contributing to the ongoing efforts to maximize the potential of these networks.

Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection

Stagnation at local optima represents a significant challenge in bio-inspired optimization algorithms, often leading to suboptimal solutions. This paper addresses this issue by proposing a hybrid model that combines the Orca predator algorithm with deep Q-learning. The Orca predator algorithm is an optimization technique that mimics the hunting behavior of orcas. It solves complex optimization problems by exploring and exploiting search spaces efficiently. Deep Q-learning is a reinforcement learning technique that combines Q-learning with deep neural networks. This integration aims to turn the stagnation problem into an opportunity for more focused and effective exploitation, enhancing the optimization technique’s performance and accuracy. The proposed hybrid model leverages the biomimetic strengths of the Orca predator algorithm to identify promising regions nearby in the search space, complemented by the fine-tuning capabilities of deep Q-learning to navigate these areas precisely. The practical application of this approach is evaluated using the high-dimensional Heartbeat Categorization Dataset, focusing on the feature selection problem. This dataset, comprising complex electrocardiogram signals, provided a robust platform for testing the feature selection capabilities of our hybrid model. Our experimental results are encouraging, showcasing the hybrid strategy’s capability to identify relevant features without significantly compromising the performance metrics of machine learning models. This analysis was performed by comparing the improved method of the Orca predator algorithm against its native version and a set of state-of-the-art algorithms.

Bio-inspired algorithms for feature engineering: analysis, applications and future research directions

Purpose Nature’s evolution has shaped intelligent behaviors in creatures like insects and birds, inspiring the field of Swarm Intelligence. Researchers have developed bio-inspired algorithms to address complex optimization problems efficiently. These algorithms strike a balance between computational efficiency and solution optimality, attracting significant attention across domains. Design/methodology/approach Bio-inspired optimization techniques for feature engineering and its applications are systematically reviewed with chief objective of assessing statistical influence and significance of “Bio-inspired optimization”-based computational models by referring to vast research literature published between year 2015 and 2022. Findings The Scopus and Web of Science databases were explored for review with focus on parameters such as country-wise publications, keyword occurrences and citations per year. Springer and IEEE emerge as the most creative publishers, with indicative prominent and superior journals, namely, PLoS ONE, Neural Computing and Applications, Lecture Notes in Computer Science and IEEE Transactions. The “National Natural Science Foundation” of China and the “Ministry of Electronics and Information Technology” of India lead in funding projects in this area. China, India and Germany stand out as leaders in publications related to bio-inspired algorithms for feature engineering research. Originality/value The review findings integrate various bio-inspired algorithm selection techniques over a diverse spectrum of optimization techniques. Anti colony optimization contributes to decentralized and cooperative search strategies, bee colony optimization (BCO) improves collaborative decision-making, particle swarm optimization leads to exploration-exploitation balance and bio-inspired algorithms offer a range of nature-inspired heuristics.

Enhancing explainable SARS-CoV-2 vaccine development leveraging bee colony optimised Bi-LSTM, Bi-GRU models and bioinformatic analysis

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a single-stranded RNA virus that caused the outbreak of the coronavirus disease 2019 (COVID-19). The COVID-19 outbreak has led to millions of deaths and economic losses globally. Vaccination is the most practical solution, but finding epitopes (antigenic peptide regions) in the SARS-CoV-2 proteome is challenging, costly, and time-consuming. Here, we proposed a deep learning method based on standalone Recurrent Neural networks to predict epitopes from SARS-CoV-2 proteins easily. We optimised the standalone Bidirectional Long Short-Term Memory (Bi-LSTM) and Bidirectional Gated Recurrent Unit (Bi-GRU) with a bioinspired optimisation algorithm, namely, Bee Colony Optimization (BCO). The study shows that LSTM-based models, particularly BCO-Bi-LSTM, outperform all other models and achieve an accuracy of 0.92 and AUC of 0.944. To overcome the challenge of understanding the model predictions, explainable AI using the Shapely Additive Explanations (SHAP) method was employed to explain how Blackbox models make decisions. Finally, the predicted epitopes led to the development of a multi-epitope vaccine. The multi-epitope vaccine effectiveness evaluation is based on vaccine toxicity, allergic response risk, and antigenic and biochemical characteristics using bioinformatic tools. The developed multi-epitope vaccine is non-toxic and highly antigenic. Codon adaptation, cloning, gel electrophoresis assess genomic sequence, protein composition, expression and purification while docking and IMMSIM servers simulate interactions and immunological response, respectively. These investigations provide a conceptual framework for developing a SARS-CoV-2 vaccine.

Power quality improvement using a 31-level multi-level inverter with bio-inspired optimization approach

In recent years, the power quality (PQ) improvements have been explored through various approaches. The employment of electronic devices with renewable energy sources has expanded the harmonics level of voltage and current. Due to harmonics, the PQ of a specific electrical system gets affected. At critical load conditions, the traditional PQ mitigation approaches fail to develop the performance of the system. Therefore, in this work, the Spider Monkey Optimization convolutional neural network (SM-CNN)-based 31-level multilevel inverter (MLI) is used. This method balances the reactive power demands and enhances real power in the grid-tied photovoltaic (PV) system. The maximum power point tracking (MPPT) algorithm depending on radial basis function neural networks (RBFNNs) is used to maximize PV power. For strengthening the voltage level of the PV and to generate higher DC voltage with a minimized switching loss, an integrated boost fly back converter (IBFC) is introduced. The presented technique is implemented in the MATLAB/Simulink platform to figure out the estimation of PQ issues. The suggested MLI lessens the total harmonic distortion (THD) value to 2.45% with an improved power factor.

Optimization of a three-stage cascade refrigeration system operating with natural refrigerants to produce low temperatures by applying a bio-inspired method

With the growing global energy consumption, on the first hand, and the need to produce low temperatures on the second hand, therefore the technological challenges consist in minimizing greenhouse gas emissions by reducing the energy consumption. In fact, in refrigeration, this consists of the use of environmentally friendly refrigerants (GWP < 150) with the optimization of cold production processes. In this context, several optimization methods have been tested, and the objective of this work to seek the most reliable method to optimize a three-stage cascade vapor compression refrigeration system. Regarding refrigerants, hydrocarbons can be a solution, the R50/R1150/R290 and R50/R1150/R1270 groups are selected and used here to produce temperatures ranging from −160 °C to −120 °C. After comparing seven (7) nature-inspired optimization methods, the JSOA bio-inspired optimization method is applied. Energetic, exergetic and environmental analyzes are also applied to determine the optimal operating point that allows both the minimization of energy consumption and TEWI (Total Equivalent Warming Impact). A pragmatic comparison between the two considered groups provides insights into the choice of high-temperature sub-cycle fluid, and it is found that R290 shows better results compared to R1270.

Modeling of discharge capacity of H-weir using experiments, bio-inspired optimization and data preprocess based on SVM

Modeling of discharge capacity of H-weir using experiments, bio-inspired optimization and data preprocess based on SVM

Boosted dipper throated optimization algorithm-based Xception neural network for skin cancer diagnosis: An optimal approach

Skin cancer is a prevalent form of cancer that necessitates prompt and precise detection. However, current diagnostic methods for skin cancer are either invasive, time-consuming, or unreliable. Consequently, there is a demand for an innovative and efficient approach to diagnose skin cancer that utilizes non-invasive and automated techniques. In this study, a unique method has been proposed for diagnosing skin cancer by employing an Xception neural network that has been optimized using Boosted Dipper Throated Optimization (BDTO) algorithm. The Xception neural network is a deep learning model capable of extracting high-level features from skin dermoscopy images, while the BDTO algorithm is a bio-inspired optimization technique that can determine the optimal parameters and weights for the Xception neural network. To enhance the quality and diversity of the images, the ISIC dataset is utilized, a widely accepted benchmark system for skin cancer diagnosis, and various image preprocessing and data augmentation techniques were implemented. By comparing the method with several contemporary approaches, it has been demonstrated that the method outperforms others in detecting skin cancer. The method achieves an average precision of 94.936%, an average accuracy of 94.206%, and an average recall of 97.092% for skin cancer diagnosis, surpassing the performance of alternative methods. Additionally, the 5-fold ROC curve and error curve have been presented for the data validation to showcase the superiority and robustness of the method.

Optimizing Lithium-Ion Battery Modeling: A Comparative Analysis of PSO and GWO Algorithms

In recent years, the modeling and simulation of lithium-ion batteries have garnered attention due to the rising demand for reliable energy storage. Accurate charge cycle predictions are fundamental for optimizing battery performance and lifespan. This study compares particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms in modeling a commercial lithium-ion battery, emphasizing the voltage behavior and the current delivered to the battery. Bio-inspired optimization tunes parameters to reduce the root mean square error (RMSE) between simulated and experimental outputs. The model, implemented in MATLAB/Simulink, integrates electrochemical parameters and estimates battery behavior under varied conditions. The assessment of terminal voltage revealed notable enhancements in the model through both the PSO and GWO algorithms compared to the non-optimized model. The GWO-optimized model demonstrated superior performance, with a reduced RMSE of 0.1700 (25 °C; 3.6 C, 455 s) and 0.1705 (25 °C; 3.6 C, 10,654 s) compared to the PSO-optimized model, achieving a 42% average RMSE reduction. Battery current was identified as a key factor influencing the model analysis, with optimized models, particularly the GWO model, exhibiting enhanced predictive capabilities and slightly lower RMSE values than the PSO model. This offers practical implications for battery integration into energy systems. Analyzing the execution time with different population values for PSO and GWO provides insights into computational complexity. PSO exhibited greater-than-linear dynamics, suggesting a polynomial complexity of O(nk), while GWO implied a potential polynomial complexity within the range of O(nk) or O(2n) based on execution times from populations of 10 to 1000.

Optimizing Cybersecurity Attack Detection in Computer Networks: A Comparative Analysis of Bio-Inspired Optimization Algorithms Using the CSE-CIC-IDS 2018 Dataset

In computer network security, the escalating use of computer networks and the corresponding increase in cyberattacks have propelled Intrusion Detection Systems (IDSs) to the forefront of research in computer science. IDSs are a crucial security technology that diligently monitor network traffic and host activities to identify unauthorized or malicious behavior. This study develops highly accurate models for detecting a diverse range of cyberattacks using the fewest possible features, achieved via a meticulous selection of features. We chose 5, 9, and 10 features, respectively, using the Artificial Bee Colony (ABC), Flower Pollination Algorithm (FPA), and Ant Colony Optimization (ACO) feature-selection techniques. We successfully constructed different models with a remarkable detection accuracy of over 98.8% (approximately 99.0%) with Ant Colony Optimization (ACO), an accuracy of 98.7% with the Flower Pollination Algorithm (FPA), and an accuracy of 98.6% with the Artificial Bee Colony (ABC). Another achievement of this study is the minimum model building time achieved in intrusion detection, which was equal to 1 s using the Flower Pollination Algorithm (FPA), 2 s using the Artificial Bee Colony (ABC), and 3 s using Ant Colony Optimization (ACO). Our research leverages the comprehensive and up-to-date CSE-CIC-IDS2018 dataset and uses the preprocessing Discretize technique to discretize data. Furthermore, our research provides valuable recommendations to network administrators, aiding them in selecting appropriate machine learning algorithms tailored to specific requirements.

GREPHRO: Nature-inspired optimization duo for Internet-of-Things

The optimization techniques usually work with the maximization or minimization of the problem to obtain the local loci or cumulative global loci. Two-dimensional bio-inspired optimization techniques face convexing problems towards a global solution and use an increased number of iterations. Besides, the duality principle considers the dual optimization aspects of the problems leading to a large duality gap of uncertain deviations and optimization errors between any prime solution and its dual solution. Moreover, several problems exist where one objective function requires maximization and another objective function requires minimization using the same set of parameters and some chaining of the feedback process. In such cases, we generally use two different optimization problems as per the best suit to the problem environment and obtain the different sub-solutions of the individual problems. This increases the complexity of the system and often deviates from the original optimal solution. We address these problems of dual optimization in our present work.In this paper, we introduce the first optimization duo model for computing services. To be specific, our proposed model is the first optimization model that works in a dual combined mode with maximization and minimization simultaneously to obtain a global optimum value or loci. We call our model GREen PHotosynthesis and Respiratory-based Optimization (GREPHRO). GREPHRO is primarily motivated by the observation of plants’ photosynthesis and respiratory processes, which work on the same set of environmental variables and have a chaining process. Further, our proposed nature-inspired GREPHRO can value the global optima or infimum point considering a single objective function serving as maximization and minimization combined. GREPHRO uses the Lagrange dual principle and non-linear parameters to obtain a linear solution for the infimum optima. We use a set of experiments on the GREPHRO model in the domain of Wireless Sensor Network (WSN)-based Internet of Thing (IoT) to derive the use case of our proposed work. The experimental results and the comparative analysis with the two of our previous works show that GREPHRO takes fewer iterations with more stability of the optimum solution. Moreover, the computational and memory complexities are also less. Therefore, GREPHRO is efficient and suitable for two-dimensional optimization problems in resource-constrained environments for IoTs.

A new QoS-aware service discovery technique in the Internet of Things using whale optimization and genetic algorithms

Rapid technological advances have made daily life easier and more convenient in recent years. As an emerging technology, the Internet of Things (IoT) facilitates interactions between physical devices. With the advent of sensors and features on everyday items, they have become intelligent entities able to perform multiple functions as services. IoT enables routine activities to become more intelligent, deeper communication, and processes more efficient. In the dynamic landscape of the IoT, effective service discovery is key to optimizing user experiences. A Quality of Service (QoS)-aware service discovery technique is proposed in this paper to address this challenge. Through whale optimization and genetic algorithms, our method aims to streamline decision-making processes in IoT service selection. The bio-inspired optimization techniques employed in our approach facilitate the discovery of services more efficiently than traditional methods. Our results demonstrate superior performance regarding reduced data access time, optimized energy utilization, and cost-effectiveness through comprehensive simulations.

African Bison Optimization Algorithm: A New Bio-Inspired Optimizer with Engineering Applications

African Bison Optimization Algorithm: A New Bio-Inspired Optimizer with Engineering Applications

MSCMGTB: A Novel Approach for Multimodal Social Media Content Moderation Using Hybrid Graph Theory and Bio-Inspired Optimization

MSCMGTB: A Novel Approach for Multimodal Social Media Content Moderation Using Hybrid Graph Theory and Bio-Inspired Optimization

Bio-inspired Optimization Algorithms in Medical Image Segmentation: A Review

Bio-inspired Optimization Algorithms in Medical Image Segmentation: A Review

Frilled Lizard Optimization: A Novel Bio-Inspired Optimizer for Solving Engineering Applications

Frilled Lizard Optimization: A Novel Bio-Inspired Optimizer for Solving Engineering Applications

Bio-inspired optimisation algorithms in medical image segmentation: a review

Bio-inspired optimisation algorithms in medical image segmentation: a review

Enhancing Grid-Connected Microgrid Power Dispatch Efficiency Through Bio-Inspired Optimization Algorithms

Enhancing Grid-Connected Microgrid Power Dispatch Efficiency Through Bio-Inspired Optimization Algorithms