Multi-criteria decision making monarch butterfly optimization for optimal distributed energy resources mix in distribution networks

The optimal integration of distributed energy resources (DERs) is a multiobjective and complex combinatorial optimization problem that conventional optimization methods cannot solve efficiently. This paper reviews the existing DER integration models, optimization and multi-criteria decision-making approaches. Further to that, a recently developed monarch butterfly optimization method is introduced to solve the problem of DER mix in distribution systems. A new multiobjective DER integration problem is formulated to find the optimal sites, sizes and mix (dispatchable and non-dispatchable) for DERs considering multiple key performance objectives. Besides, a hybrid method that combines the monarch butterfly optimization and the technique for order of preference by similarity to ideal solution (TOPSIS) is proposed to solve the formulated large-scale multi-criteria decision-making problem. Whilst the meta-heuristic optimization method generates non-dominated solutions (creating Pareto-front), the TOPSIS approach selects that with the most promising outcome from a large number of alternatives. The effectiveness of this approach is verified by solving single and multiobjective dispatchable DER integration problems over the benchmark 33-bus distribution system and the performance is compared with the existing optimization methods. The proposed model of DER mix and the optimization technique significantly improve the system performance in terms of average annual energy loss reduction by 78.36%, mean node voltage deviation improvement by 9.59% and average branches loadability limits enhancement by 50%, and minimized the power fluctuation induced by 48.39% renewable penetration. The proposed optimization techniques outperform the existing methods with promising exploration and exploitation abilities to solve engineering optimization problems

Metaheuristic-Based Neural Network Training And Feature Selector For Intrusion Detection

Intrusion Detection (ID) in the context of computer networks is an essential technique in modern defense-in-depth security strategies. As such, Intrusion Detection Systems (IDSs) have received tremendous attention from security researchers and professionals. An important concept in ID is anomaly detection, which amounts to the isolation of normal behavior of network traffic from abnormal (anomaly) events. This isolation is essentially a classification task, which led researchers to attempt the application of well-known classifiers from the area of machine learning to intrusion detection. Neural Networks (NNs) are one of the most popular techniques to perform non-linear classification, and have been extensively used in the literature to perform intrusion detection. However, the training datasets usually compose feature sets of irrelevant or redundant information, which impacts the performance of classification, and traditional learning algorithms such as backpropagation suffer from known issues, including slow convergence and the trap of local minimum. Those problems lend themselves to the realm of optimization. Considering the wide success of swarm intelligence methods in optimization problems, the main objective of this thesis is to contribute to the improvement of intrusion detection technology through the application of swarm-based optimization techniques to the basic problems of selecting optimal packet features, and optimal training of neural networks on classifying those features into normal and attack instances. To realize these objectives, the research in this thesis follows three basic stages, succeeded by extensive evaluations

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

Energy-Efficient Flexible Flow Shop Scheduling With Due Date and Total Flow Time

One of the most significant optimization issues facing a manufacturing company is the flexible flow shop scheduling problem (FFSS). However, FFSS with uncertainty and energy-related elements has received little investigation. Additionally, in order to reduce overall waiting times and earliness/tardiness issues, the topic of flexible flow shop scheduling with shared due dates is researched. Using transmission line loadings and bus voltage magnitude variations, an unique severity function is formulated in this research. Optimize total energy consumption, total agreement index, and make span all at once. Many different meta-heuristics have been presented in the past to find near-optimal answers in an acceptable amount of computation time. To explore the potential for energy saving in shop floor management, a multi-level optimization technique for flexible flow shop scheduling and integrates power models for individual machines with cutting parameters optimisation into energy-efficient scheduling issues is proposed. However, it can be difficult and time-consuming to fine-tune algorithm-specific parameters for solving FFSP

Speaker Recognition Based on Mutated Monarch Butterfly Optimization Configured Artificial Neural Network

Speaker recognition is the process of extracting speaker-specific details from voice waves to validate the features asserted by system users; in other words, it allows voice-controlled access to a range of services. The research initiates with extraction features from voice signals and employing those features in Artificial Neural Network (ANN) for speaker recognition. Increasing the number of hidden layers and their associated neurons reduces the training error and increases the computational process\u27s complexity. It is essential to have an optimal number of hidden layers and their corresponding, but attaining those optimal configurations through a manual or trial and the process takes time and makes the process more complex. This urges incorporating optimization approaches for finding optimal hidden layers and their corresponding neurons. The technique involve in configuring the ANN is Mutated Monarch Butterfly Optimization (MMBO). The proposed MMBO employed for configuring the ANN achieves the sensitivity of 97.5% in a real- time database that is superior to contest techniques

Virtual machine placement in cloud using artificial bee colony and imperialist competitive algorithm

Increasing resource efficiency and reducing energy consumption are significant challenges in cloud environments. Placing virtual machines is essential in improving cloud systems’ performance. This paper presents a hybrid method using the artificial bee colony and imperialist competitive algorithm to reduce provider costs and decrease client expenditure. Implementation of the proposed plan in the CloudSim simulation environment indicates the proposed method performs better than the Monarch butterfly optimization and salp swarm algorithms regarding energy consumption and resource usage. Moreover, average central processing unit (CPU) and random-access memory (RAM) usage and the number of host shutdowns show better results for the proposed model

An Efficient Data Analytics and Optimized Algorithm for Enhancing the Performance of Image Segmentation Using Deep Learning Model

Image segmentation is the key topic in computer vision and image processing with applications like robotic perception, scene understanding, video surveillance, image compression, medical image analysis, and augmented reality among many others. There are numerous algorithms are developed in the literature for image segmentation. This paper provides a broad spectrum of pioneering works for instance and semantic level segmentation with mask Region based Convolution Neural Network with Monarch butterfly Optimization (RCNN-MBO) architecture. The system is initially constructed in a Python environment with images of people and animals being input. Remove the unnecessary data from the gathered datasets during the pre-processing stage. Next, use a stochastic threshold function to segment the image. Then update the segmented images into a designed model for detecting and classifying a group of images. The main goal of the designed approach is to attain accurate prediction results also improve the performance of the designed model by attaining better results. To enhance the performance, two activation functions were used and MBO fitness is updated in the classification layer. It improves the prediction results and takes less time to detect and classify images. Finally, the experimental outcomes show the reliability of the designed approach by other conventional techniques in terms of accuracy, precision, sensitivity, specificity, F-measure, error rate, and computation time

Victoria Amazonica Optimization (VAO): An Algorithm Inspired by the Giant Water Lily Plant

The Victoria Amazonica plant, often known as the Giant Water Lily, has the largest floating spherical leaf in the world, with a maximum leaf diameter of 3 meters. It spreads its leaves by the force of its spines and creates a large shadow underneath, killing any plants that require sunlight. These water tyrants use their formidable spines to compel each other to the surface and increase their strength to grab more space from the surface. As they spread throughout the pond or basin, with the earliest-growing leaves having more room to grow, each leaf gains a unique size. Its flowers are transsexual and when they bloom, Cyclocephala beetles are responsible for the pollination process, being attracted to the scent of the female flower. After entering the flower, the beetle becomes covered with pollen and transfers it to another flower for fertilization. After the beetle leaves, the flower turns into a male and changes color from white to pink. The male flower dies and sinks into the water, releasing its seed to help create a new generation. In this paper, the mathematical life cycle of this magnificent plant is introduced, and each leaf and blossom are treated as a single entity. The proposed bio-inspired algorithm is tested with 24 benchmark optimization test functions, such as Ackley, and compared to ten other famous algorithms, including the Genetic Algorithm. The proposed algorithm is tested on 10 optimization problems: Minimum Spanning Tree, Hub Location Allocation, Quadratic Assignment, Clustering, Feature Selection, Regression, Economic Dispatching, Parallel Machine Scheduling, Color Quantization, and Image Segmentation and compared to traditional and bio-inspired algorithms. Overall, the performance of the algorithm in all tasks is satisfactory.Comment: 45 page

Economic Lot Sizing and Scheduling in Distributed Permutation Flow Shops

This paper addresses a new mixed integer nonlinear and linear mathematical programming economic lot sizing and scheduling problem in distributed permutation flow shop problem with number of identical factories and machines. Different products must be distributed between the factories and then assignment of products to factories and sequencing of the products assigned to each factory has to be derived. The objective is to minimize the sum of setup costs, work-in-process inventory costs and finished products inventory costs per unit of time. Since the proposed model is NP-hard, an efficient Water Cycle Algorithm is proposed to solve the model. To justify proposed WCA, Monarch Butterfly Optimization (MBO), Genetic Algorithm (GA) and combination of GA and simplex are utilized. In order to determine the best value of algorithms parameters that result in a better solution, a fine-tuning procedure according to Response Surface Methodology is executed