Monarch Butterfly Optimization Algorithm Research Articles

Hybrid Spam Filtering using Monarch Butterfly Optimization Algorithm with Self-Adaptive Population

Spam causes bottlenecks and congestion, reducing the speed, processing power, available memory, and bandwidth. Existing spam email classification methods need to be more accurate because of the large dimensionality of hybrid spam datasets. This makes the need for a feature dimensionality reduction technique that uses only associated features of the problem instead of all features in the dataset. This paper presents a feature selection based on the monarch butterfly optimization (MBO) algorithm that emphasizes less complexity and few features. This method is efficient and produces a more accurate classification. To improve further standard MBO algorithm performance, we introduce the population size in both subpopulations 1 and 2 will experience dynamic variations as the algorithm proceeds along its linear way. As the idea of a self-adaptive and greedy strategy is modified, the self-adaptive population monarch butterfly optimization (SPMBO) method is introduced, and only newly generated SPMBO individuals are eligible for the next generations if they are better individuals earlier before. Later, this paper proposes an email classification system based on k-nearest neighbors (k-NN) based on two distance metrics, explicitly Euclidean, and Manhattan, that also uses the SPMBO technique. This method seeks to determine whether a hybrid email is a spam. The efficiency of the proposed SPMBO algorithm was compared with standard MBO based on three datasets Dredze, Image spam hunter, and Spambase. Thus, the use of SPMBO results has shown superior as related to other authors' works in relevant fields.

Reliable and Automatic Recognition of Leaf Disease Detection using Optimal Monarch Ant Lion Recurrent Learning

Around 7.5 billion people worldwide depend on agriculture production for their livelihood, making it an essential component in keeping life alive on the planet. Negative impacts are being caused on the agroecosystem due to the rapid increase in the use of chemicals to combat plant diseases. These chemicals include fungicides, bactericides, and insecticides. Both the quantity and quality of the output are impacted when there is a high-scale prevalence of diseases in crops. Plant diseases provide a significant obstacle for the agricultural industry, which has a negative impact on the growth of plants and the output of crops. The problem of early detection and diagnosis of diseases can be solved for the benefit of the farming community by employing a method that is both quick and reliable regularly. This article proposes a model for the detection and diagnosis of leaf infection called the Automatic Optimal Monarch AntLion Recurrent Learning (MALRL) model, which attains a greater authenticity. The design of a hybrid version of the Monarch Butter Fly optimization algorithm and the AntLion Optimization Algorithm is incorporated into the MALRL technique that has been proposed. In the leaf image, it is used to determine acceptable aspects of impacted regions. After that, the optimal characteristics are used to aid the Long Short Term Neural Network (LSTM) classifier to speed up the process of lung disease categorization. The experiment's findings are analyzed and compared to those of ANN, CNN, and DNN. The proposed method was successful in achieving a high level of accuracy when detecting leaf disease for images of healthy leaves in comparison to other conventional methods.

Solving optimal power flow problem for IEEE-30 bus system using a developed particle swarm optimization method: towards fuel cost minimization

ABSTRACT The optimal power flow (OPF) is an important technique for optimizing the control parameters of modern power systems by taking into account the desired objective functions considering system constraints. This paper provides a developed particle swarm optimization (PSO) method for solving the OPF problem with a rigorous objective function of minimizing generation fuel costs for the utility and industrial companies while satisfying a set of system limitations. By reviewing previous OPF investigations, the developed PSO is used in the IEEE 30-bus test system to reduce overall fuel cost, active/reactive power losses, and exhaust. The obtained results are compared to those obtained using a typical PSO technique and other algorithms. In the IEEE 30-bus test system, one of the paper’s key findings is that the cost of fuel is computed as 800.41 $/h, 830.7779 $/h, 825.6922 $/h, 826.54 $/h, 826.3176 $/h, 823.3999 $/h, 786.03 $/h with the conventional PSO, backtracking search algorithm (BSA), hybrid SFLA-SA, differential evolution (DE), enhanced GA (EGA), monarch butterfly optimization (MBO), proposed algorithm, respectively. Moreover, there is a great reduction in the fuel cost by 4.358% compared with the robust MBO algorithm. MATLAB software is used to demonstrate the effectiveness and accuracy of the proposed technique.

Application of Improved Monarch Butterfly Optimization for Parameters’ Optimization

The reasonable selection of cutting parameters in the machining process is of great significance to improve productivity, reduce production costs, and improve the quality of parts. However, due to the complexity of cutting parameter model optimization, most factories currently use experience or refer to relevant manuals to select the value of cutting parameters in production. In order to avoid and minimize abnormalities, they usually select more experienced and conservative values, and often do not select reasonable cutting parameters, which is not conducive to improving productivity, reducing production costs, and improving the quality of parts. Therefore, the research on cutting parameter optimization has important theoretical value and application value. In this paper, in order to find the optimal cutting parameters, the cutting model is solved by the improved monarch butterfly optimization (IMBO) algorithm, and the optimized cutting parameters are obtained. By establishing the mathematical model of cutting, the constraint conditions of actual machining are introduced into the model. In order to solve the model, some ideas of particle swarm optimization (PSO) and differential evolution (DE) are added to the traditional monarch butterfly optimization (MBO) algorithm. The MBO algorithm is improved to deal with multiobjective optimization problems. The IMBO algorithm is used to optimize the cutting model. The experiment shows that the optimized cutting parameters can significantly reduce production cost and maintain high production efficiency. Compared with NSGA-II algorithm and other swarm intelligence optimization algorithms, it shows that the IMBO algorithm has certain advantages in multiobjective optimization.

Evolutionary Algorithm Based Feature Subset Selection for Students Academic Performance Analysis

Educational Data Mining (EDM) is an emergent discipline that concentrates on the design of self-learning and adaptive approaches. Higher education institutions have started to utilize analytical tools to improve students’ grades and retention. Prediction of students’ performance is a difficult process owing to the massive quantity of educational data. Therefore, Artificial Intelligence (AI) techniques can be used for educational data mining in a big data environment. At the same time, in EDM, the feature selection process becomes necessary in creation of feature subsets. Since the feature selection performance affects the predictive performance of any model, it is important to elaborately investigate the outcome of students’ performance model related to the feature selection techniques. With this motivation, this paper presents a new Metaheuristic Optimization-based Feature Subset Selection with an Optimal Deep Learning model (MOFSS-ODL) for predicting students’ performance. In addition, the proposed model uses an isolation forest-based outlier detection approach to eliminate the existence of outliers. Besides, the Chaotic Monarch Butterfly Optimization Algorithm (CBOA) is used for the selection of highly related features with low complexity and high performance. Then, a sailfish optimizer with stacked sparse autoencoder (SFO-SSAE) approach is utilized for the classification of educational data. The MOFSS-ODL model is tested against a benchmark student’s performance data set from the UCI repository. A wide-ranging simulation analysis portrayed the improved predictive performance of the MOFSS-ODL technique over recent approaches in terms of different measures. Compared to other methods, experimental results prove that the proposed (MOFSS-ODL) classification model does a great job of predicting students’ academic progress, with an accuracy of 96.49%.

Improved clustering algorithm for personal privacy and security protection of elderly consumers

With the advancement of technology, there is an increasing emphasis on the personal privacy and security of elderly consumers. This article focuses on the personal privacy and security protection of elderly consumers. Based on the K-means (KM) clustering algorithm, the optimal value was obtained using the monarch butterfly optimization (MBO) algorithm. The migration operator and adjustment operator of the MBO algorithm were enhanced using differential variation algorithm and adaptive methods to obtain a modified monarch butterfly optimization (MMBO) algorithm. Then, to ensure secure protection during clustering, differential privacy (DP) was employed to add noise perturbation to data to obtained a method called DPMMBO-KM algorithm. In experiments on the UCI dataset, it was found that the MMBO-KM algorithm had better clustering performance. Taking the Iris dataset as an example, the MMBO-KM algorithm achieved the highest accuracy of 93.21%. In the application to recommendation systems, the DPMMBO-KM algorithm achieved higher F1 values under different privacy budgets; the average was 0.06. The results demonstrate that the improved clustering algorithm designed in this article can improve clustering results while ensuring personal privacy and data security, and also perform well in recommendation systems.

Optimal Modeling and Optimizing of a New Biogas Production Configuration based on a Neural Network Optimized by Developed Version of Monarch Butterfly Optimization Algorithm

Optimal Modeling and Optimizing of a New Biogas Production Configuration based on a Neural Network Optimized by Developed Version of Monarch Butterfly Optimization Algorithm

Power Allocation Intelligent Optimization for Mobile NOMA Communication System

Non-orthogonal multiple access (NOMA) technology can greatly improve user access and spectral efficiency. This paper considers the power allocation optimization problem of a two-user mobile NOMA communication system. Firstly, a mobile NOMA communication system model is established. Then, we analyze the outage probability (OP) of mobile NOMA communication system and the relationship between OP performance and user power allocation coefficient. Finally, the optimization objective function is established, and a power allocation optimization algorithm employing monarch butterfly optimization (MBO) is proposed. Compared with firefly algorithm and artificial fish swarm algorithm, the efficiency of MBO algorithm is increased by 20.7%, which can better improve the OP performance.

Arrhythmia classification based on improved monarch butterfly optimization algorithm

Heart attack is a major cause of mortality all over the world also heart-related diseases have increased the expenditure of health care. Electrocardiogram (ECG) is one of the simple ways to diagnose heart diseases. Arrhythmia is characterised by abnormalities in the rhythmic pace of heart, which may occur on a random basis in a everyday life of people. It was necessary to use long-term ECG recording devices to capture these rare occurrences. Morphological feature and wavelet coefficients-based features obtained from the recorded ECG signals. The feature vector is optimized by an Improved Monarch Butterfly optimization (IMBO) algorithm to reduce the dimensionality. These optimized features are applied to convolution neural networks to classify signals. The experimental results of the proposed method give a 99.49% accuracy, 99.58% sensitivity, and 98.83% specificity which is comparative to previous methods, and found better classification accuracy which may help the physician for diagnosing an arrhythmia.

Monarch butterfly optimization algorithm for computed tomography image segmentation

In the medical field, image segmentation provides important information for surgical planning and registration, and thus demands accurate segmentation. In order to improve the effectiveness and the threshold accuracy of segmentation, researchers have tended to use a metaheuristic algorithm as the operational algorithm to achieve better exploitation and exploration of the search space and to solve many different medical image problems in an effective manner. This research applies the monarch butterfly optimization (MBO) algorithm for image segmentation at multiple threshold values. To evaluate the performance of the implemented MBO algorithm, a comparison is made with the brute-force (i.e. Otsu) algorithm and two metaheuristic algorithms (i.e. Darwinian particle swarm optimization (DPSO) and fractional-order DPSO). In addition, the quality structural similarity index matrix and the peak signal-to-noise ratio are used to evaluate the accuracy of the resultant segmented images. The experimental results show the advantage of using the MBO algorithm for medical image segmentation in terms of accuracy and speed. Regards the accuracy, MBO algorithm produced an exact match at thresholds 1 and 2 and a very close match at thresholds 3 and 4. Regards the speed, the average of the execution time for threshold 1 was 0.24187 s, for threshold 2 was 0.33831 s, for threshold 3 was 0.95967 s and for threshold 4 it was 1.15308 s.

A multi-objective Monarch Butterfly Algorithm for virtual machine placement in cloud computing

The growing demand for cloud computing adoption presents more challenges for researchers to make cloud computing more efficient and affordable for infrastructure providers and end users. The management of cloud computing involves investing in IT infrastructure in the first phase and investing in energy, maintenance and space costs in the second phase. However, energy costs account for a large portion of cloud management costs, and saving energy consumption can significantly reduce overall cloud management costs. Server consolidation is a strategy to improve data center energy efficiency and resource utilization. Virtual machine (VM) placement is considered one of the main problems with VM consolidation. The VM placement problem aims to reduce the number of active physical machines in data center to reduce data center power consumption and maintenance costs. However, the waste of data center resources has a significant impact on the energy efficiency of the data center, so it should be considered in the VM placement strategy. This paper proposes a new method based on the Monarch Butterfly Optimization algorithm (MBO) called MBO-VM for new virtual machine placement, designed to maximize packaging efficiency and reduce the number of active physical servers. CloudSim toolkit is used to test the efficiency of the proposed MBO-VM approach under real cloud workloads as well as synthetic workloads. Simulation results show that MBO-VM produces significantly better results compared with known VM placement techniques. The proposed MBO-VM can reduce the number of active servers more effectively and maximize the packaging efficiency.

Solving feature selection problems by combining mutation and crossover operations with the monarch butterfly optimization algorithm

Feature selection (FS) is used to solve hard optimization problems in artificial intelligence and data mining. In the FS process, some, rather than all of the features of a dataset are selected in order to both maximize classification accuracy and minimize the time required for computation. In this paper a FS wrapper method that uses K-nearest Neighbor (KSN) classification is subjected to two modifications using a current improvement algorithm, the Monarch Butterfly Optimization (MBO) algorithm. The first modification, named MBOICO, involves the utilization of an enhanced crossover operator to improve FS. The second, named MBOLF, integrates the Levy flight distribution into the MBO to improve convergence speed. Experiments are carried out on 25 benchmark data sets using the original MBO, MBOICO and MBOLF. The results show that MBOICO is superior, so its performance is also compared against that of four metaheuristic algorithms (PSO, ALO, WOASAT, and GA). The results indicate that it has a high classification accuracy rate of 93% on average for all datasets and significantly reduces the selection size. Hence, the findings demonstrate that the MBOICO outperforms the other algorithms in terms of classification accuracy and number of features chosen (selection size).

An enhanced monarch butterfly optimization with self-adaptive crossover operator for unconstrained and constrained optimization problems

Inspired by the phenomenon of migration of monarch butterflies, Wang et al. developed a novel promising swarm intelligence algorithm, called monarch butterfly optimization (MBO), for addressing unconstrained low-dimensional optimization problems. In this paper, we firstly extend the application area of the basic MBO to solve the constrained optimization problems. At the same time, the crossover operator originally used in evolutionary algorithms (EAs) is incorporated into the butterfly adjusting operator in order to strengthen the exploitation of the basic MBO algorithm. Furthermore, the crossover rate is self-adaptively adjusted according to the fitness of the corresponding individual instead of the fixed crossover rate used in EAs. For migration operator, only individuals having better fitness are accepted and passed to the next generation instead of accepting all the individuals in the basic MBO algorithm. After incorporated all the modifications into the basic MBO algorithm, an improved MBO algorithm with self-adaptive crossover namely SACMBO, is proposed for unstrained and constrained optimization problems. Finally, the proposed SACMBO algorithm is further used to solve 22 unstrained optimization problems (with dimension of 100, 300, 500, 1000, and 1500) and 28 constrained real-parameter optimization functions from CEC 2017 competition (with dimension of 50 and 100), respectively. The experimental results indicate that the proposed SACMBO algorithm outperforms the basic MBO and other five state-of-the-art metaheuristic algorithms.

Improving monarch butterfly optimization through simulated annealing strategy

AbstractCurrently, a novel of meta-heuristic algorithm called monarch butterfly optimization (MBO) is presented for solving machine learning and continuous optimization problems. It has been proved experimentally that MBO is superior to artificial bee colony algorithm (ABC), ant colony optimization algorithm (ACO), Biogeography-based optimization (BBO), differential evolution algorithm (DE) and simple genetic algorithm (SGA) algorithms on most test functions. This paper presents a new version of MBO with simulated annealing (SA) strategy called SAMBO. The SA strategy is put in the migration operator and butterfly adjusting operator. So the newly proposed algorithm has two features: One is that the algorithm accepts all the butterfly individuals whose fitness are better than their parents. The other is that the algorithm randomly selects some individuals which are worse than their parents to disturbance the convergence of algorithm. In this way, the SAMBO algorithm can escape from local optima. Finally, the experiments are carried on 14 continuous nonlinear functions, and results show that SAMBO method exceeds the MBO algorithm on most test functions.

Biometric Iris Recognition of Person from an Image at Long Distance using Chronological Monarch Butterfly Optimization based Deep Belief Network

Now days, for the identification of personal information of a person, biometrics is mostly used. Also for the personal identification, the recognition of eye based biometric feature extraction is the most powerful tool. The biometric is an important identity to identify the individual. But in real time it is quite difficult to capture the better quality of iris images. The images obtained are more degraded due to the lack of texture, blur. In this paper, more convenient method is presented for extracting the features of biometric images. The method Iris Recognition at-a Distance (IAAD) is used to extract the iris features of biometric image and to enhance the quality of an image in a biometric system. The Chronological Monarch Butterfly Optimization -based Deep Belief Network (Chronological MBO-based DBN) is proposed for iris recognition to get better accuracy. The Monarch Butterfly Optimization algorithm is used to arrange the Chronological assumption of an iris image. Also, the Hough Transform algorithm is used for detection of iris circle and edge. The scaT T loop descriptor and the Local Gradient Pattern (LGP) are used for feature extraction, which is fed to the Chronological MBO-based DBN for iris recognition that enhances the accuracy. The Daugman’s rubber sheet model, median filter and trained neural network are used for normalization and segmentation. The UBIRIS.v1 database is used to take an iris recognition images and MATLAB is used for programming of for reading the iris images and for performing the Hough transform operations. The iris recognition at a distance 4 to 8 meter is done with the help of simulation result. The performance is analyzed based on the metrics, like False Acceptance Rate (FAR), accuracy, and False Rejection Rate (FRR) with the value of 0.4847%, 96.078%, and 0.4745%

Cloudlet Scheduling by Hybridized Monarch Butterfly Optimization Algorithm

Cloud computing technology enables efficient utilization of available physical resources through the virtualization where different clients share the same underlying physical hardware infrastructure. By utilizing the cloud computing concept, distributed, scalable and elastic computing resources are provided to the end-users over high speed computer networks (the Internet). Cloudlet scheduling that has a significant impact on the overall cloud system performance represents one of the most important challenges in this domain. In this paper, we introduce implementations of the original and hybridized monarch butterfly optimization algorithm that belongs to the category of swarm intelligence metaheuristics, adapted for tackling the cloudlet scheduling problem. The hybridized monarch butterfly optimization approach, as well as adaptations of any monarch butterfly optimization version for the cloudlet scheduling problem, could not be found in the literature survey. Both algorithms were implemented within the environment of the CloudSim platform. The proposed hybridized version of the monarch butterfly optimization algorithm was first tested on standard benchmark functions and, after that, the simulations for the cloudlet scheduling problem were performed using artificial and real data sets. Based on the obtained simulation results and the comparative analysis with six other state-of-the-art metaheuristics and heuristics, under the same experimental conditions and tested on the same problem instances, a hybridized version of the monarch butterfly optimization algorithm proved its potential for tackling the cloudlet scheduling problem. It has been established that the proposed hybridized implementation is superior to the original one, and also that the task scheduling problem in cloud environments can be more efficiently solved by using such an algorithm with positive implications to the cloud management.

Improved Monarch Butterfly Optimization Algorithm Based on Opposition-Based Learning and Random Local Perturbation

Many optimization problems have become increasingly complex, which promotes researches on the improvement of different optimization algorithms. The monarch butterfly optimization (MBO) algorithm has proven to be an effective tool to solve various kinds of optimization problems. However, in the basic MBO algorithm, the search strategy easily falls into local optima, causing premature convergence and poor performance on many complex optimization problems. To solve the issues, this paper develops a novel MBO algorithm based on opposition-based learning (OBL) and random local perturbation (RLP). Firstly, the OBL method is introduced to generate the opposition-based population coming from the original population. By comparing the opposition-based population with the original population, the better individuals are selected and pass to the next generation, and then this process can efficiently prevent the MBO from falling into a local optimum. Secondly, a new RLP is defined and introduced to improve the migration operator. This operation shares the information of excellent individuals and is helpful for guiding some poor individuals toward the optimal solution. A greedy strategy is employed to replace the elitist strategy to eliminate setting the elitist parameter in the basic MBO, and it can reduce a sorting operation and enhance the computational efficiency. Finally, an OBL and RLP-based improved MBO (OPMBO) algorithm with its complexity analysis is developed, following on which many experiments on a series of different dimensional benchmark functions are performed and the OPMBO is applied to clustering optimization on several public data sets. Experimental results demonstrate that the proposed algorithm can achieve the great optimization performance compared with a few state-of-the-art algorithms in most of the test cases.

Integrating mutation scheme into monarch butterfly algorithm for global numerical optimization

Monarch butterfly optimization algorithm (MBO) has recently been proposed as a robust metaheuristic optimization algorithm for solving numerical global optimization problems. To enhance the performance of MBO algorithm, harmony search (HS) is introduced as a mutation operator during the adjusting operator of MBO. A novel hybrid metaheuristic optimization method, the so-called HMBO, is introduced to find the best solution for the global optimization problems. HMBO combines HS exploration with MBO exploitation, and therefore, it produces potential candidate solutions. The implementation process for enhancing MBO method is also presented. To evaluate the effectiveness of this improvement, fourteen standard benchmark functions are used. The mean and the best performance of these benchmark functions in 20, 50, and 100 dimensions demonstrated that HMBO often performs better than the original MBO and other population-based optimization algorithms such as ACO, BBO, DE, ES, GAPBIL, PSO and SGA. Moreover, the t-test result proved that the performance differences between the enhanced HMBO and the original MBO as well as the other optimization methods are statistically significant.

Improving Monarch Butterfly Optimization Algorithm with Self-Adaptive Population

Inspired by the migration behavior of monarch butterflies in nature, Wang et al. proposed a novel, promising, intelligent swarm-based algorithm, monarch butterfly optimization (MBO), for tackling global optimization problems. In the basic MBO algorithm, the butterflies in land 1 (subpopulation 1) and land 2 (subpopulation 2) are calculated according to the parameter p, which is unchanged during the entire optimization process. In our present work, a self-adaptive strategy is introduced to dynamically adjust the butterflies in land 1 and 2. Accordingly, the population size in subpopulation 1 and 2 are dynamically changed as the algorithm evolves in a linear way. After introducing the concept of a self-adaptive strategy, an improved MBO algorithm, called monarch butterfly optimization with self-adaptive population (SPMBO), is put forward. In SPMBO, only generated individuals who are better than before can be accepted as new individuals for the next generations in the migration operation. Finally, the proposed SPMBO algorithm is benchmarked by thirteen standard test functions with dimensions of 30 and 60. The experimental results indicate that the search ability of the proposed SPMBO approach significantly outperforms the basic MBO algorithm on most test functions. This also implies the self-adaptive strategy is an effective way to improve the performance of the basic MBO algorithm.

Differential Evolution and Local Search based Monarch Butterfly Optimization Algorithm with Applications

Global optimization for nonlinear function is a challenging issue. In this paper, an improved monarch butterfly algorithm based on local search and differential evolution is proposed. Local search strategy is first embedded into original monarch butterfly optimization to enhance the searching capability. Then, differential evolution is incorporated with the aim of balancing the exploration and exploitation. To evaluate the performance of proposed algorithm, some widely-used benchmark functions are tested, and the experiment results show its significant superiority compared with other state-of-the-art methods. In addition, the proposed algorithm is applied to PID tuning and FIR filter design, the superiority of solving practical problems is verified.