Butterfly Optimization Research Articles

Multi-Step-Ahead Time Series Forecasting using Deep Learning and Fuzzy Time Series-based Error Correction Method

Recently time series forecasting has become one of the prime application areas of climatology, economics and industries. Many research works are conducted to forecast the time series more accurately. But few of them are concentrated on predicting the time series over an extended future horizon, and there is also a scope to improve their forecasting accuracy. This work proposes a multi-step-ahead foresting method to produce a stable and accurate forecasting result for the extended future horizon. Firstly, a deep learning-based forecasting model is proposed to predict the time series. Secondly, a fuzzy time series-based error correction model is implemented to enhance the prediction performance of the deep learning model. Here to optimize all the fuzzy time series (FTS) parameters in an integrated way, an integrated butterfly optimization (FTS-IBO) algorithm is proposed. In this study, two different types of real-world multivariate time series datasets are used to analyze the forecasting performance of the proposed model. The performance of the proposed FTS-IBO algorithm is compared with the traditional butterfly optimization (FTS-BO) algorithm. The experimental results show that the FTS-IBO technique is superior to the FTS-BO technique. The forecasting performance of the proposed model has also compared the other state-of-the-art models, and the simulation results exhibit that the proposed model produces a more accurate prediction performance for multi-step-ahead time series forecasting problems compared to other models.

Research on hybrid strategy Particle Swarm Optimization algorithm and its applications

The increasing complexity and high-dimensional nature of real-world optimization problems necessitate the development of advanced optimization algorithms. Traditional Particle Swarm Optimization (PSO) often faces challenges such as local optima entrapment and slow convergence, limiting its effectiveness in complex tasks. This paper introduces a novel Hybrid Strategy Particle Swarm Optimization (HSPSO) algorithm, which integrates adaptive weight adjustment, reverse learning, Cauchy mutation, and the Hook-Jeeves strategy to enhance both global and local search capabilities. HSPSO is evaluated using CEC-2005 and CEC-2014 benchmark functions, demonstrating superior performance over standard PSO, Dynamic Adaptive Inertia Weight PSO (DAIW-PSO), Hummingbird Flight patterns PSO (HBF-PSO), Butterfly Optimization Algorithm (BOA), Ant Colony Optimization (ACO), and Firefly Algorithm (FA). Experimental results show that HSPSO achieves optimal results in terms of best fitness, average fitness, and stability. Additionally, HSPSO is applied to feature selection for the UCI Arrhythmia dataset, resulting in a high-accuracy classification model that outperforms traditional methods. These findings establish HSPSO as an effective solution for complex optimization and feature selection tasks.

Coverage optimization for IoT-based network using Monarch Butterfly Optimization with greedy strategy and self-adaptive crossover operator

In the dynamic development of technology, the Internet of Things (IoT) stands as a driving force of innovation and opening new frontiers. Data collection and processing in IoT systems provide services to decision making in various fields including home automation, disaster management, healthcare, and so on. In such services, the IoT has certain limitations to perform the required tasks and it can cause extreme consequences in the network. One of the main challenge in IoT is to ensure maximum coverage by optimally placing the sensors. To address the coverage problem, a meta-heuristic algorithm Monarch Butterfly Optimization with greedy strategy and self-adaptive crossover operator (GCMBO) is proposed for Optimal Sensor Placement (OSP) in 3D monitoring region. The performance of proposed algorithm is evaluated by a series of simulation and it provide efficient results when compared to other similar existing optimization algorithms. Additionally, a statistical analysis (ANOVA and LSD post-hoc) is exhibited from the comparative results.

A Butterfly Algorithm That Combines Chaos Mapping and Fused Particle Swarm Optimization for UAV Path Planning

Effective path planning is essential for autonomous drone flight to enhance task efficiency. Many researchers have applied swarm intelligence algorithms to drone path planning. For instance, the traditional Butterfly Optimization Algorithm (BOA) has been used for this purpose. However, traditional BOA faces challenges such as slow convergence and susceptibility to being trapped in local optima. An Improved Butterfly Optimization Algorithm (IBOA) has been developed to identify optimal routes to address these limitations. Initially, ICMIC mapping is utilized to establish the butterfly community, enhancing the initial population’s diversity and preventing premature algorithm convergence. Following this, a population reset strategy is introduced, replacing weaker individuals over a specified number of iterations while maintaining a constant population size. This strategy enhances the algorithm’s ability to avoid local optima and increases its robustness. Additionally, characteristics of the Particle Swarm Optimization (PSO) algorithm are integrated to enhance the butterfly’s location update mechanism, accelerating the algorithm’s convergence rate. To evaluate the performance of the IBOA algorithm, this study designed a CEC2020 function test experiment and compared it with several swarm intelligence algorithms. The results showed that IBOA achieved the best performance in 70% of the function tests, outperforming 75% of the other algorithms. In the path planning experiments within a simulated environment, IBOA quickly converged to the optimal path, and the paths it planned were the shortest and safest compared to those generated by other algorithms.

A Hybrid Metaheuristic based Feature Selection Framework for In-silico Mutagenicity Prediction

Mutagenicity is both a toxic risk to humans and an indicator of carcinogenicity. Hence, estimating mutagenicity in the early stages of drug design is crucial to minimize last-stage failures and withdrawals in drug discovery. Recently, in-silico methods have started to play critical and essential roles in the drug development process because they are low cost and low effort procedures. This study aims to predict mutagenicity of chemicals using in-silico methods. To achieve this goal, a two-phased flexible framework was proposed: 1) searching the effective and representative descriptors subset with Butterfly Optimization Algorithm (BOA) and Particle Swarm Optimization and 2) predicting mutagenicity of chemicals by the selected descriptor using gradient boosted tree-based ensemble methods. The study used two datasets: one including 8167 compounds for descriptor selection and modelling, and another containing 716 external compounds to validate the efficacy of our models. The datasets comprise 162 descriptors calculated using PaDEL. The results of both the cross-validation and the external data showed that descriptors reduced by nearly one-third by BOA (51 descriptors) yielded similar or slightly better predictive results than results obtained with the entire data set. The accuracy range attained by the proposed approach using BOA is approximately 91.9% to 97.91% for the external set and 83.35% to 86.47% for the test set. This research contributes that using optimization techniques for improving early drug design and minimizing risks in drug discovery can be considered as a valuable insights and advances in the field of drug toxicity prediction, based on the findings.

Combined Economic Emission Dispatch Including Variable Energy Resources

Sustainable energy solutions are becoming more and more necessary as the world's energy needs increase as a result of population expansion and industrialization. Using renewable energy sources has become essential to meeting these needs and reducing the negative effects on the environment. The increasing incorporation of renewable energy sources (RES) like wind and solar into microgrid systems poses a notable obstacle to attaining optimum power dispatch because of their intrinsic unpredictability. The combined economic emission dispatch (CEED) issue may become inefficient as a result of this fluctuation, especially in islanded microgrid systems. In particular, in areas with significant RES potential, resolving this problem is essential to improving the sustainability and dependability of the energy supply. In this work, the optimization of the CEED issue in an islanded microgrid system with wind, solar, and thermal energy sources is the main emphasis. By employing a weighted sum approach and a Butterfly Optimization Algorithm (BOA), the research aims to provide an efficient solution to the multi-objective CEED dilemma. The proposed method outperforms traditional optimization techniques, offering a more robust framework for integrating RES into microgrids. This research reveals a number of limitations that affect the effectiveness of energy dispatch systems, such as thermal unit ramp rates and operating restrictions. Subsequent investigations have to concentrate on delving deeper into these limitations and devising tactics to augment the flexibility of optimization algorithms such as the Butterfly Optimization Algorithm (BOA) approach.

Bio-inspired MPPT and advanced control for grid-connected solar photovoltaic systems with flyback converter

ABSTRACT This paper presents an enhanced approach for grid-connected photovoltaic (PV) systems using a flyback converter and Sovereign Butterfly Optimization for advanced Maximum Power Point Tracking (MPPT). The focus is on improving energy extraction efficiency from PV panels by dynamically adjusting system parameters. A flyback converter ensures reliable grid integration, while the Butterfly Optimization Algorithm, inspired by butterfly foraging, provides real-time MPPT to rapidly and accurately track the maximum power point, even under varying environmental conditions. A novel voltage harmonic mitigation technique processes three-phase currents through dq0 frames for closed-loop control, reducing harmonics and optimising voltage precision. The DC/DC flyback converter stabilizes AC conversion, minimizing high-frequency harmonics. Additionally, a Subsystem Synchronization Control Strategy synchronizes active and non-active grid currents, improving system stability. Simulations show a 99.3% tracking accuracy, 2% power oscillation around the maximum power point, and a Total Harmonic Distortion (THD) of 1.78%, meeting IEC 61,727 standards.

Solving power system economic emission dispatch problem under complex constraints via dimension differential learn butterfly optimization algorithm with FDC-based

The economic emission dispatch (EED) aims to minimize the fuel and pollutant emission costs of generator units under various complex constraints. Optimizing the EED problem is of crucial importance for alleviating the current energy and environmental pressures. In this work, nearly all known complex constraints in the EED problem, including the valve-point effect, transmission line power loss, prohibited operating zones, and ramp-rate limits, are taken into account, and an enhanced version of butterfly optimization algorithm (FDCDLBOA) is proposed to solve it. First, a new adaptive fragrance is employed to optimize the instability caused by target differences and improve the convergence performance. Second, the proposed dimension differential learning strategy evolves the position of individuals with the help of superior dimensional information in the population, and this extensive learning exchange can balance global and local search, maintain diversity, and get rid of local optima. Third, the Fitness-Distance-Constraint (FDC) guide selection method is employed for the first time to handle the complex constraints of EED problems, enhancing the ability of individuals to bypass the infeasible search areas. After evaluating the proposed FDCDLBOA on CEC 2022 test suite, it is applied to solve 8 EED cases, encompassing small-, medium- and large-scale systems. Notably, the 280-generator case is the first large-scale test to exceed 200 generators. Compared with 9 representative algorithms, FDCDLBOA performs outstandingly in terms of robustness, improvement index (IF), mean constraint violation (MV), feasibility rate (FR) and Quade multiple comparison, among which IF, MV, FR, and Quade are all employed for evaluating the EED problem for the first time. The presented results confirm that the proposed method effectively enhances the robustness of high-quality solutions and the ability to handle complex constraints, demonstrating strong competitiveness and potential in solving the EED problem.

A hybrid YDSE - THDCNN approach based multi objective optimization of energy management for renewable energy sources with electric vehicles

Thermal power plants have long been essential to the fossil fuel-based electricity supply chain, according to statistics studies. Nevertheless, due to the enormous initial and ongoing expenses of modern power plants as well as their negative environmental effects. This manuscript proposes a hybrid technique for grid-connected load-following hybrid photovoltaic and wind microgrid with a grid-connected electric vehicle charging system. The proposed hybrid technique is the joint execution of both Young's double-slit experiment optimizer (YDSE) and the Tree Hierarchical Deep Convolutional Neural Network (THDCNN). Hence, it is named as YDSE-THDCNN. The major objective of the proposed technique is to reduce the fuel, operation, and maintenance costs, as well as to reduce the environmental costs and maximize the efficiency of the system. The proposed YDSE technique is utilized to generate the control signal of the inverter and the optimal signal will be predicted by the THDCNN. By then, the MATLAB working platform has adopted the proposed way, and the existing method is used to compute its execution. The proposed method outperforms any existing techniques, including the Genetic Algorithm (GA), Butterfly Optimization Algorithm (BOA), and Particle Swarm Optimization (PSO) Algorithm. The existing method shows the cost of 2.6$, 3.8$, 4.6$, and the proposed method shows the cost of 1.3$ which is lower than the other existing method.

An innovative method for improving the performance of unmanned aerial vehicle‐based generalized spatial modulation and non‐orthogonal multiple access in 6G networks

AbstractThe rapid evolution of wireless networks is leading to the emergence of 6G, promising unprecedented performance. We propose a nonorthogonal multiple access (NOMA)‐based multiuser generalized spatial modulation (GSM) system that allows various transmitting antennas. Integrating unmanned aerial vehicles (UAVs) with NOMA and GSM enhances overall network performance. This paper proposes optimizing UAV‐GSM‐NOMA systems in 6G using the butterfly optimization algorithm (BOA) in the two‐wave with diffuse power (TWDP) channel. Results are compared with those obtained from Nakagami and Rayleigh channels. Objectives include enhancing the sumrate, outage probability (OP), and energy efficiency (EE) in perfect and imperfect channel state information (CSI). Simulation results yield significant improvements in the TWDP channel compared with the Nakagami and Rayleigh channels. The OP is reduced, the sumrate is enhanced, and the EE is notably improved. These findings underscore the BOA's potential for 6G UAV‐GSM‐NOMA networks in challenging channel environments, ensuring reliable and efficient communication.

Bio-Inspired Energy-Efficient Cluster-Based Routing Protocol for the IoT in Disaster Scenarios.

The Internet of Things (IoT) is a promising technology for sensing and monitoring the environment to reduce disaster impact. Energy is one of the major concerns for IoT devices, as sensors used in IoT devices are battery-operated. Thus, it is important to reduce energy consumption, especially during data transmission in disaster-prone situations. Clustering-based communication helps reduce a node's energy decay during data transmission and enhances network lifetime. Many hybrid combination algorithms have been proposed for clustering and routing protocols to improve network lifetime in disaster scenarios. However, the performance of these protocols varies widely based on the underlying network configuration and the optimisation parameters considered. In this research, we used the clustering parameters most relevant to disaster scenarios, such as the node's residual energy, distance to sink, and network coverage. We then proposed the bio-inspired hybrid BOA-PSO algorithm, where the Butterfly Optimisation Algorithm (BOA) is used for clustering and Particle Swarm Optimisation (PSO) is used for the routing protocol. The performance of the proposed algorithm was compared with that of various benchmark protocols: LEACH, DEEC, PSO, PSO-GA, and PSO-HAS. Residual energy, network throughput, and network lifetime were considered performance metrics. The simulation results demonstrate that the proposed algorithm effectively conserves residual energy, achieving more than a 17% improvement for short-range scenarios and a 10% improvement for long-range scenarios. In terms of throughput, the proposed method delivers a 60% performance enhancement compared to LEACH, a 53% enhancement compared to DEEC, and a 37% enhancement compared to PSO. Additionally, the proposed method results in a 60% reduction in packet drops compared to LEACH and DEEC, and a 30% reduction compared to PSO. It increases network lifetime by 10-20% compared to the benchmark algorithms.

Optimized automated detection of diabetic retinopathy severity: integrating improved multithresholding tunicate swarm algorithm and improved hybrid butterfly optimization.

Diabetic retinopathy, a complication of diabetes, damages the retina due to prolonged high blood sugar levels, leading to vision impairment and blindness. Early detection through regular eye exams and proper diabetes management are crucial in preventing vision loss. DR is categorized into five classes based on severity, ranging from no retinopathy to proliferative diabetic retinopathy. This study proposes an automated detection method using fundus images. Image segmentation divides fundus images into homogeneous regions, facilitating feature extraction. Feature selection aims to reduce computational costs and improve classification accuracy by selecting relevant features. The proposed algorithm integrates an Improved Tunicate Swarm Algorithm (ITSA) with Renyi's entropy for enhanced adaptability in the initial and final stages. An Improved Hybrid Butterfly Optimization (IHBO) Algorithm is also introduced for feature selection. The effectiveness of the proposed method is demonstrated using retinal fundus image datasets, achieving promising results in DR severity classification. For the IDRiD dataset, the proposed model achieves a segmentation Dice coefficient of 98.06% and classification accuracy of 98.21%. In contrast, the E-Optha dataset attains a segmentation Dice coefficient of 97.95% and classification accuracy of 99.96%. Experimental results indicate the algorithm's ability to accurately classify DR severity levels, highlighting its potential for early detection and prevention of diabetes-related blindness.

A novel deep learning approach for optimizing preparation and usage of nonsteroidal anti-inflammatory drugs (NSAIDs) in spine care

Nonsteroidal anti-inflammatory drugs (NSAIDs) are essential for the management of pain and inflammation brought by illnesses of the spine. NSAIDs are essential in the treatment of inflammation and pain in a variety of spinal disorders. In order to relieve pain for patients and enhance their quality of life, NSAIDs should be used effectively. The effectiveness and safety of NSAIDs for spinal pain were assessed using a systematic review and meta-analysis. We gathered the patient data. The insufficient data is cleaned using tokenization and the data extraction employs Term Frequency-Inverse Document Frequency (TF-IDF). A Monarch Butterfly Optimization Dynamic Generative Adversarial Networks (MBO+DGAN) for the preparation and use of NSAIDs in spine care. Our studies show the possibility of the MBO+DGAN strategy to enhance the administration of NSAIDs in spine care, leading to improved clinical results, patient experiences and possibly lower healthcare costs. The MBO+DGAN assesses a method's effectiveness by evaluating its performance in terms of Accuracy (95%), Sensitivity (96%), Precision (97%) and Specificity (94%). The opportunity to use MBO+DGAN to improve how NSAIDs are created and utilized in the complex world of spine care. This technique offers an effective approach to enhance patient care and outcomes in the difficult field of spine healthcare by enabling customized treatment plans.

Power allocation in NOMA using sum rate-based dwarf mongoose optimization

The increasing number of consumers with diverse data rate needs is leading to increased heterogeneity in traditional cellular networks. Nonorthogonal multiple access (NOMA) has emerged as a promising method to serve a large number of users, but research shows that weak users (WU) and strong users (SU) have different throughputs. Intra-group interference reduces WUs throughput due to the superposition of signals. Improper power distribution impacts NOMA performance and lowers the total system rate. The multi-objective sum rate dwarf mongoose optimization algorithm (M-SRDMOA) is implemented as a solution to the NOMA network power allocation problems. The DMOA approach distributes adequate power to all NOMA users to increase the large sum rate. The effectiveness of the M-SRDMOA approach is supported by existing studies on fair NOMA scheduler (FANS) and multi-objective sum rate-based butterfly optimization algorithm (M-SRBOA). The M-SRDMOA’s potential sum rate with an SNR of 9dB and a noise variation=2 is 14.06 bps/Hz, which is high compared to M-SRBOA and FANS.

Chaos-BBO: Chaos balanced butterfly optimizer with dynamic continuum chaotic strategies and its applications

Chaos-BBO: Chaos balanced butterfly optimizer with dynamic continuum chaotic strategies and its applications

Improved Multi-objective Butterfly Optimization Algorithm and its Application in Cascade Reservoirs Optimal Operation Considering Ecological Flow

Improved Multi-objective Butterfly Optimization Algorithm and its Application in Cascade Reservoirs Optimal Operation Considering Ecological Flow

HCSMBO: A hybrid cat swarm and monarch butterfly optimization algorithm for energy consumption optimization in industrial internet of things

Energy consumption optimization is crucial for improving the quality of application services in the industrial Internet of Things (IIoT) environment. Traditional optimization methods often adopt blind search strategies, which leads to a significant waste of computing resources and low efficiency. This paper proposes a Hybrid Cat Swarm Optimization and Monarch Butterfly Optimization (HCSMBO), which combines the advantages of Cat Swarm Optimization (CSO) and Monarch Butterfly Optimization (MBO) to solve this problem. By utilizing the characteristics of swarm intelligence, including strong spatial search ability, high stability, and fast convergence, HCSMBO focuses on detailed optimization of the coverage, efficiency, and energy balance coefficient of Support Vector Machine (SVM) networks. By adjusting the weight factors of the fitness function, this algorithm can effectively find the global optimal solution. The experimental results confirm that HCSMBO significantly improves the intelligence, accuracy, and stability of task scheduling in resource management configuration, while achieving lower energy consumption. The research results of this paper are of great significance for improving the service quality of IIoT applications, meeting personalized business needs, and improving resource utilization and execution efficiency. In addition, they also provide new ideas and methods for energy consumption optimization research in the future IIoT.

Suspended sediment load prediction using sparrow search algorithm-based support vector machine model

Prediction of suspended sediment load (SSL) in streams is significant in hydrological modeling and water resources engineering. Development of a consistent and accurate sediment prediction model is highly necessary due to its difficulty and complexity in practice because sediment transportation is vastly non-linear and is governed by several variables like rainfall, strength of flow, and sediment supply. Artificial intelligence (AI) approaches have become prevalent in water resource engineering to solve multifaceted problems like sediment load modelling. The present work proposes a robust model incorporating support vector machine with a novel sparrow search algorithm (SVM-SSA) to compute SSL in Tilga, Jenapur, Jaraikela and Gomlai stations in Brahmani river basin, Odisha State, India. Five different scenarios are considered for model development. Performance assessment of developed model is analyzed on basis of mean absolute error (MAE), root mean squared error (RMSE), determination coefficient (R2), and Nash–Sutcliffe efficiency (ENS). The outcomes of SVM-SSA model are compared with three hybrid models, namely SVM-BOA (Butterfly optimization algorithm), SVM-GOA (Grasshopper optimization algorithm), SVM-BA (Bat algorithm), and benchmark SVM model. The findings revealed that SVM-SSA model successfully estimates SSL with high accuracy for scenario V with sediment (3-month lag) and discharge (current time-step and 3-month lag) as input than other alternatives with RMSE = 15.5287, MAE = 15.3926, and ENS = 0.96481. The conventional SVM model performed the worst in SSL prediction. Findings of this investigation tend to claim suitability of employed approach to model SSL in rivers precisely and reliably. The prediction model guarantees the precision of the forecasted outcomes while significantly decreasing the computing time expenditure, and the precision satisfies the demands of realistic engineering applications.

Chronic Kidney Disease Classification Using Entropy Based Butterfly Optimization and Improved Artificial Neural Network Algorithm: Preprocessing, Feature Selection, Clustering, and Disease Prediction

Chronic Kidney Disease Classification Using Entropy Based Butterfly Optimization and Improved Artificial Neural Network Algorithm: Preprocessing, Feature Selection, Clustering, and Disease Prediction

Modified effective butterfly optimizer for solving optimal power flow problem

The optimal power flow (OPF) problem remains a popular and challenging work in optimizing power systems. Although researchers have suggested many optimization algorithms to solve this problem in the literature, their comparison studies lack fairness and transparency. As these studies increase in number, they deviate from a standard test system, considering a common security and technical constraints., there is a growing trend away from a standard test system. Different studies used different search ranges for the same decision and constraint parameters, different than the standard ranges suggested by IEEE systems. This caused many unfair comparisons in literature. Furthermore, these studies are generally not transparent enough so that their results cannot be verified. This has resulted in numerous infeasible solutions in the literature, violating the limits of constraint parameters. The recent incorporating of renewable energy sources in OPF studies has made this situation more complicated. Sorting through the literature and identifying those OPF applications having exactly the same test conditions is a challenging process. The main contribution is this paper adapts the modified effective butterfly algorithm (MEBO) to solve OPF problem under the common parameter constraints and sufficient transparency. The focus is on a transparent comparison with works in the literature with the same constraint values. This paper compares the performance of the proposed algorithm with other state-of-the-art algorithms in the literature, focusing on the wind energy and without wind energy IEEE 30-bus and IEEE 57-bus systems and the most commonly used constraints. The results demonstrate the efficiency and superiority of the proposed algorithm. For instance, in the 30-bus test system, compared to the initial case, fuel cost has been reduced by 11.42 %, emission by 14.33 %, L-index by 45.10 %, active power losses by 51.60 %, and voltage deviation by 92.70 %.