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

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

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

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

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

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

Random Subset Feature Selection for Ecological Niche Modeling of Wildfire Activity and the Monarch Butterfly

Correlative ecological niche models (ENMs) are essential for investigating distributions of species and natural phenomena via environmental correlates across broad fields, including entomology and pyrogeography featured in this study. Feature (variable) selection is critical for producing more robust ENMs with greater transferability across space and time, but few studies evaluate formal feature selection algorithms (FSAs) for producing higher performance ENMs. Variability of ENMs arising from feature subsets is also seldom represented. A novel FSA is developed and evaluated, the random subset feature selection algorithm (RSFSA). The RSFSA generates an ensemble of higher accuracy ENMs from different feature subsets, producing a feature subset ensemble (FSE). The RSFSA-selected FSEs are novelly used to represent ENM variability. Wildfire activity presence/absence databases for the western US prove ideal for evaluating RSFSA-selected MaxEnt ENMs. The RSFSA was effective in identifying FSEs of 15 of 90 variables with higher accuracy and information content than random FSEs. Selected FSEs were used to identify severe contemporary wildfire deficits and significant future increases in wildfire activity for many ecoregions. Migratory roosting localities of declining eastern North American monarch butterflies (Danaus plexippus) were used to spatially model migratory pathways, comparing RSFSAselected MaxEnt ENMs and kernel density estimate models (KDEMs). The higher information content ENMs best correlated migratory pathways with nectar resources in grasslands. Higher accuracy KDEMs best revealed migratory pathways through less suitable desert environments. Monarch butterfly roadkill data was surveyed for Texas within the main Oklahoma to Mexico Central Funnel migratory pathway. A random FSE of MaxEnt roadkill ENMs was used to estimate a 2-3% loss of migrants to roadkill. Hotspots of roadkill in west Texas and Mexico were recommended for assessing roadkill mitigation to assist in monarch population recovery. The RSFSA effectively produces higher performance ENM FSEs for estimating optimal feature subset sizes, and comparing ENM algorithms and parameters, and environmental scenarios. The RSFSA also performed comparably to expert variable selection, confirming its value in the absence of expert information. The RSFSA should be compared with other FSAs for developing ENMs and in data mining applications across other disciplines, such as image classification and molecular bioinformatics

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

Beetle Colony Optimization Algorithm and its Application

Massive data sets and complex scheduling processes have high-dimensional and non-convex features bringing challenges on various applications. With deep insight into the bio-heuristic opinion, we propose a novel Beetle Colony Optimization (BCO) being able to adapt NP-hard issues to meet growing application demands. Two important mechanisms are introduced into the proposed BCO algorithm. The first one is Beetle Antennae Search (BAS), which is a mechanism of random search along the gradient direction but not use gradient information at all. The second one is swarm intelligence, which is a collective mechanism of decentralized and self-organized agents. Both of them have reached a performance balance to elevate the proposed algorithm to maintain a wide search horizon and high search efficiency. Finally, our algorithm is applied to traveling salesman problem, and quadratic assignment problem and possesses excellent performance, which also shows that the algorithm has good applicability from the side. The effectiveness of the algorithm is also substantiated by comparing the results with the original ant colony optimization (ACO) algorithm in 3D simulation model experimental path planning