Electrical power prediction through a combination of multilayer perceptron with water cycle ant lion and satin bowerbird searching optimizers

Predicting the electrical power (PE) output is a significant step toward the sustainable development of combined cycle power plants. Due to the effect of several parameters on the simulation of PE, utilizing a robust method is of high importance. Hence, in this study, a potent metaheuristic strategy, namely, the water cycle algorithm (WCA), is employed to solve this issue. First, a nonlinear neural network framework is formed to link the PE with influential parameters. Then, the network is optimized by the WCA algorithm. A publicly available dataset is used to feed the hybrid model. Since the WCA is a population-based technique, its sensitivity to the population size is assessed by a trial-and-error effort to attain the most suitable configuration. The results in the training phase showed that the proposed WCA can find an optimal solution for capturing the relationship between the PE and influential factors with less than 1% error. Likewise, examining the test results revealed that this model can forecast the PE with high accuracy. Moreover, a comparison with two powerful benchmark techniques, namely, ant lion optimization and a satin bowerbird optimizer, pointed to the WCA as a more accurate technique for the sustainable design of the intended system. Lastly, two potential predictive formulas, based on the most efficient WCAs, are extracted and presented

Analysis and Prediction of Student Performance by Using A Hybrid Optimized BFO-ALO Based Approach: Student Performance Prediction using Hybrid Approach

Data mining offers effective solutions for a variety of industries, including education. Research in the subject of education is expanding rapidly because of thebigquantityof student data that can be utilized to uncover valuable learning behavior patterns. This research presents a method for forecasting the academic presentation of students in Portuguese as well as math subjects, and it is describing with the help of  33 attributes. Forecasting the educationalattainment of students is the most popular field of study in the modern period. Previous research has employed a variety of categorization algorithms to forecast student performance. Educational data mining is a topic that needs a lot of research to improve the precision of the classification technique and predict how well students will do in school. In this study, we made a method to predict how well a student will do that uses a mix of optimization techniques. BFO and ALO-based popular optimization techniques were applied to the data set. Python was used to process all the files and conduct a performance comparison analysis. In this study, we compared our model's performance with various existing baseline models and examined the accuracy with which the hybrid algorithm predicted the student data set. To verify the expected classification accuracy, a calculation was performed. The experiment's findings indicate that the BFO-ALO Based hybrid model, which, out of all the methods, with a 94.5 percent success rate, is the preferred choice

Application of artificial intelligence techniques for predicting the flyrock, Sungun mine, Iran

Flyrock is known as one of the main problems in open pit mining operations. This phenomenon can threaten the safety of mine personnel, equipment and buildings around the mine area. One way to reduce the risk of accidents due to flyrock is to accurately predict that the safe area can be identified and also with proper design of the explosion pattern, the amount of flyrock can be greatly reduced. For this purpose, 14 effective parameters on flyrock have been selected in this paper i.e. burden, blasthole diameter, sub-drilling, number of blastholes, spacing, total length, amount of explosives and a number of other effective parameters, predicting the amount of flyrock in a case study, Songun mine, using linear multivariate regression (LMR) and artificial intelligence algorithms such as Gray Wolf Optimization algorithm (GWO), Moth-Flame Optimization algorithm (MFO), Whale Optimization Algorithm (WOA), Ant Lion Optimizer (ALO) and Multi-Verse Optimizer (MVO). Results showed that intelligent algorithms have better capabilities than linear regression method and finally method MVO showed the best performance for predicting flyrock. Moreover, the results of the sensitivity analysis show that the burden, ANFO, total rock blasted, total length and blast hole diameter are the most significant factors to determine flyrock, respectively, while dynamite has the lowest impact on flyrock generation.Peer ReviewedObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraPostprint (published version

Antlion re-sampling based deep neural network model for classification of imbalanced multimodal stroke dataset

Stroke is enlisted as one of the leading causes of death and serious disability affecting millions of human lives across the world with high possibilities of becoming an epidemic in the next few decades. Timely detection and prompt decision making pertinent to this disease, plays a major role which can reduce chances of brain death, paralysis and other resultant outcomes. Machine learning algorithms have been a popular choice for the diagnosis, analysis and predication of this disease but there exists issues related to data quality as they are collected cross-institutional resources. The present study focuses on improving the quality of stroke data implementing a rigorous pre-processing technique. The present study uses a multimodal stroke dataset available in the publicly available Kaggle repository. The missing values in this dataset are replaced with attribute means and LabelEncoder technique is applied to achieve homogeneity. However, the dataset considered was observed to be imbalanced which reflect that the results may not represent the actual accuracy and would be biased. In order to overcome this imbalance, resampling technique was used. In case of oversampling, some data points in the minority class are replicated to increase the cardinality value and rebalance the dataset. transformed and oversampled data is further normalized using Standardscalar technique. Antlion optimization (ALO) algorithm is implemented on the deep neural network (DNN) model to select optimal hyperparameters in minimal time consumption. The proposed model consumed only 38.13% of the training time which was also a positive aspect. The experimental results proved the superiority of proposed model

Secure Energy Aware Optimal Routing using Reinforcement Learning-based Decision-Making with a Hybrid Optimization Algorithm in MANET

Mobile ad hoc networks (MANETs) are wireless networks that are perfect for applications such as special outdoor events, communications in areas without wireless infrastructure, crises and natural disasters, and military activities because they do not require any preexisting network infrastructure and can be deployed quickly. Mobile ad hoc networks can be made to last longer through the use of clustering, which is one of the most effective uses of energy. Security is a key issue in the development of ad hoc networks. Many studies have been conducted on how to reduce the energy expenditure of the nodes in this network. The majority of these approaches might conserve energy and extend the life of the nodes. The major goal of this research is to develop an energy-aware, secure mechanism for MANETs. Secure Energy Aware Reinforcement Learning based Decision Making with Hybrid Optimization Algorithm (RL-DMHOA) is proposed for detecting the malicious node in the network. With the assistance of the optimization algorithm, data can be transferred more efficiently by choosing aggregation points that allow individual nodes to conserve power The optimum path is chosen by combining the Particle Swarm Optimization (PSO) and the Bat Algorithm (BA) to create a fitness function that maximizes across-cluster distance, delay, and node energy. Three state-of-the-art methods are compared to the suggested method on a variety of metrics. Throughput of 94.8 percent, average latency of 28.1 percent, malicious detection rate of 91.4 percent, packet delivery ratio of 92.4 percent, and network lifetime of 85.2 percent are all attained with the suggested RL-DMHOA approach

Synthesizing multi-layer perceptron network with ant lion biogeography-based dragonfly algorithm evolutionary strategy invasive weed and league champion optimization hybrid algorithms in predicting heating load in residential buildings

The significance of accurate heating load (HL) approximation is the primary motivation of this research to distinguish the most efficient predictive model among several neural-metaheuristic models. The proposed models are formulated through synthesizing a multi-layer perceptron network (MLP) with ant lion optimization (ALO), biogeography-based optimization (BBO), the dragonfly algorithm (DA), evolutionary strategy (ES), invasive weed optimization (IWO), and league champion optimization (LCA) hybrid algorithms. Each ensemble is optimized in terms of the operating population. Accordingly, the ALO-MLP, BBO-MLP, DA-MLP, ES-MLP, IWO-MLP, and LCA-MLP presented their best performance for population sizes of 350, 400, 200, 500, 50, and 300, respectively. The comparison was carried out by implementing a ranking system. Based on the obtained overall scores (OSs), the BBO (OS = 36) featured as the most capable optimization technique, followed by ALO (OS = 27) and ES (OS = 20). Due to the efficient performance of these algorithms, the corresponding MLPs can be promising substitutes for traditional methods used for HL analysis

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC