This paper proposes a delivery task allocation model based on m-UAVs, aimed at maximizing the number of delivery tasks and minimizing the average and longest delivery times underling a fixed number of UAVs. A new disc...
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The intrusion detection system is responsible for revealing different intrusion activities, including the denial of service, man-in-middle, Mirai, Scan, and other types of intrusion activities. It is used in many appl...
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The intrusion detection system is responsible for revealing different intrusion activities, including the denial of service, man-in-middle, Mirai, Scan, and other types of intrusion activities. It is used in many applications, including the smart home Internet of Things networks, where security risks threaten the privacy of individuals. In this context, many works were proposed for detecting and classifying the different types of attacks. However, many challenges are identified for this type of problem, such as the large amount of data available, the imbalanced nature of the data, and the quality of detection and classification outcomes. This paper aims to address these challenges by proposing an approach that considers a metaheuristic-based random weight neural network to detect intrusion activities and classify the different types and subtypes of activities. The following points summarize the contribution of this paper. First, the automatic tuning of the neural network parameters where the weights, biases, regularization value, the number of neurons, and the type of activation function are optimized by different metaheuristic algorithms to produce high-quality results. Second, the proposed approach adopts a clustering with reduction technique to tackle the challenge of processing large volumes of data. Third, oversampling the dataset is also embedded in the proposed approach to avoid a biased classification of the majority class. The experiments are conducted based on a large dataset with more than half a million instances. The results show that the proposed approach outperforms the other classification approaches in geometric mean (G-Mean) and has promising results.
Power coupling between different size waveguides has been successfully and efficiently designed and optimized by using evolutionary algorithms based on the artificial immune system and differential evolution in conjun...
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Here we introduce a new evolutionary algorithm called the Lotus Effect Algorithm, which combines efficient operators from the dragonfly algorithm, such as the movement of dragonflies in flower pollination for explorat...
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Here we introduce a new evolutionary algorithm called the Lotus Effect Algorithm, which combines efficient operators from the dragonfly algorithm, such as the movement of dragonflies in flower pollination for exploration, with the self-cleaning feature of water on flower leaves known as the lotus effect, for extraction and local search operations. The authors compared this method to other improved versions of the dragonfly algorithm using standard benchmark functions, and it outperformed all other methods according to Fredman's test on 29 benchmark functions. The article also highlights the practical application of LEA in reducing energy consumption in IoT nodes through clustering, resulting in increased packet delivery ratio and network lifetime. Additionally, the performance of the proposed method was tested on real-world problems with multiple constraints, such as the welded beam design optimization problem and the speed-reducer problem applied in a gearbox, and the results showed that LEA performs better than other methods in terms of accuracy.
In current years, the death rate from skin cancers (SCs) tends to develop pretty. Various research verified that SC rank third as a deadliest disease, after breast and lung cancer. It will become vital to diagnose thi...
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In current years, the death rate from skin cancers (SCs) tends to develop pretty. Various research verified that SC rank third as a deadliest disease, after breast and lung cancer. It will become vital to diagnose this malignancy at an early stage. The objective of this research is to mix machine learning and soft computing techniques to gain higher accuracy within the prediction of SC. To play out the exploration work, we utilized two data sets, one from "Save Life Hospital," India, and the other is the UCI repository skin cancer data set. In this article, three meta-heuristic algorithms, the FS_GA, the FS_PSO, and the FS_ACO, were used to select the best features from the data set provided to it. The AFRG_algorithm generates a set of fuzzy rules automatically and the RR_algorithm reduces certain fuzzy rules from the fuzzy system. For the SCC_dataset, the end accuracy obtained was 97.67%, 98.45%, and 99.22%. For the UCI_dataset, the end accuracy obtained was 98.81%, 99.72%, and 99.67%. Experimental results on the used datasets show that the proposed method strikingly improves the forecast exactitude of skin malignancy.
evolutionary neural networks (ENNs) are an adaptive approach that combines the adaptive mechanism of evolutionary algorithms (EAs) with the learning mechanism of Artificial Neural Network (ANNs). In view of the diffic...
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evolutionary neural networks (ENNs) are an adaptive approach that combines the adaptive mechanism of evolutionary algorithms (EAs) with the learning mechanism of Artificial Neural Network (ANNs). In view of the difficulties in design and development of DNNs, ENNs can optimize and supplement deep learning algorithm, and the more powerful neural network systems are hopefully built. Many valuable conclusions and results have been obtained in this field, especially in the construction of automated deep learning systems. This study conducted a systematic review of the literature on ENNs by using the PRISMA protocol. In literature analysis, the basic principles and development background of ENNs are firstly introduced. Secondly, the main research techniques are introduced in terms of connection weights, architecture design and learning rules, and the existing research results are summarized and the advantages and disadvantages of different research methods are analyzed. Then, the key technologies and related research progress of ENNs are summarized. Finally, the applications of ENNs are summarized and the direction of future work is proposed.
Thanks to the enhanced computational capacity of modern computers, even sophisticated analog/radio frequency (RF) circuit sizing problems can be solved via electronic design automation (EDA) tools. Recently, several a...
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Thanks to the enhanced computational capacity of modern computers, even sophisticated analog/radio frequency (RF) circuit sizing problems can be solved via electronic design automation (EDA) tools. Recently, several analog/RF circuit optimization algorithms have been successfully applied to automatize the analog/RF circuit design process. Conventionally, metaheuristic algorithms are widely used in optimization process. Among various nature-inspired algorithms, evolutionary algorithms (EAs) have been more preferred due to their superiorities (robustness, efficiency, accuracy etc.) over the other algorithms. Furthermore, EAs have been diversified and several distinguished analog/RF circuit optimization approaches for single-, multi-, and many-objective problems have been reported in the literature. However, there are conflicting claims on the performance of these algorithms and no objective performance comparison has been revealed yet. In the previous work, only a few case study circuits have been under test to demonstrate the superiority of the utilized algorithm, so a limited comparison has been made for only these specific circuits. The underlying reason is that the literature lacks a generic benchmark for analog/RF circuit sizing problem. To address these issues, we propose a comprehensive comparison of the most popular two evolutionary computation algorithms, namely Non-Sorting Genetic Algorithm-II and Multi-Objective evolutionary Algorithm based Decomposition, in this article. For that purpose, we introduce two ad hoc testbenches for analog and RF circuits including the common building blocks. The comparison has been made at both multi- and many-objective domains and the performances of algorithms have been quantitatively revealed through the well-known Pareto-optimal front quality metrics.
This work describes a new surrogate-assisted constraint-handling technique (CHT) for parametric multi-objective evolutionary algorithms, called Bayesian CHT. Parametric optimization finds optimal solutions as a functi...
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This work describes a new surrogate-assisted constraint-handling technique (CHT) for parametric multi-objective evolutionary algorithms, called Bayesian CHT. Parametric optimization finds optimal solutions as a function of one or more exogenous variables. The solution is a family of Pareto frontiers called the parameterized Pareto frontier (PPF). CHTs from non-parametric multi-objective evolutionary algorithms do not produce a good sampling of solutions on the PPF. The proposed CHT addresses this using Bayesian methods. A Gaussian process classifier serves as an uncertainty-quantified surrogate for problem constraints, enabling active learning and a novel repair mechanism that promotes sampling along the PPF. The new technique is evaluated on a suite of 36 test problems and two engineering cases of structural design. Quantitative results show that the proposed Bayesian CHT outperforms several state-of-the-art algorithms in most cases. From a qualitative perspective, the nondominated solutions are visualized to support the superiority of the new approach, which achieves a better spread of solutions on the PPF. The results of engineering studies also indicate that the new approach is computationally more efficient than the others.
White blood cells (WBCs) are essential for immune and inflammatory responses, and their precise classification is crucial for diagnosing and managing diseases. Although convolutional neural networks (CNNs) are effecti...
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White blood cells (WBCs) are essential for immune and inflammatory responses, and their precise classification is crucial for diagnosing and managing diseases. Although convolutional neural networks (CNNs) are effective for image classification, their high computational demands necessitate feature selection to enhance efficiency and interpretability. This study utilizes transfer learning with EfficientNet-B0 and DenseNet201 to extract features, along with a Bayesian-based feature selection method with a novel optimization mechanism to improve convergence. The reduced feature set is classified using soft voting across multiple classifiers. Tests on benchmark datasets achieved over 99% accuracy with fewer features, surpassing or matching existing methods.
Sparse large-scale evolutionary multiobjective optimization has garnered substantial interest over the past years due to its significant practical implications. These optimization problems are characterized by a predo...
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Sparse large-scale evolutionary multiobjective optimization has garnered substantial interest over the past years due to its significant practical implications. These optimization problems are characterized by a predominance of zero-valued decision variables in the Pareto optimal solutions. Most existing algorithms focus on exploiting the sparsity of solutions by starting with initializing all decision variables with a nonzero value. Opposite to the existing approaches, we propose to initialize all decision variables to zero, then progressively identify and optimize the nonzero ones. The proposed framework consists of two stages. In the first stage of evolutionary optimization, a clustering method is applied at a predefined period of generations to identify nonzero decision variables according to the statistics of each variable's current and historical values. Once a new nonzero decision variable is identified, it is randomly initialized within one of the two intervals, one defined by its lower quartile and lower bound, and the other by its upper quartile and upper bound. In the second stage, the clustering method is also periodically employed to distinguish between zero and nonzero decision variables. Different to the first stage, the zero decision variables will be set to zero straight, and the nonzero decision variables will be mutated at a higher probability. The performance of the proposed framework is empirically examined against state-of-the-art evolutionary algorithms on both sparse and nonsparse benchmarks and real-world problems, demonstrating its superior performance on different classes of problems.
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