Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (36)
  • Open Access

    ARTICLE

    An Effective Runge-Kutta Optimizer Based on Adaptive Population Size and Search Step Size

    Ala Kana, Imtiaz Ahmad*

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3443-3464, 2023, DOI:10.32604/cmc.2023.040775

    Abstract A newly proposed competent population-based optimization algorithm called RUN, which uses the principle of slope variations calculated by applying the Runge Kutta method as the key search mechanism, has gained wider interest in solving optimization problems. However, in high-dimensional problems, the search capabilities, convergence speed, and runtime of RUN deteriorate. This work aims at filling this gap by proposing an improved variant of the RUN algorithm called the Adaptive-RUN. Population size plays a vital role in both runtime efficiency and optimization effectiveness of metaheuristic algorithms. Unlike the original RUN where population size is fixed throughout the search process, Adaptive-RUN automatically… More >

  • Open Access

    ARTICLE

    Ensemble of Population-Based Metaheuristic Algorithms

    Hao Li, Jun Tang*, Qingtao Pan, Jianjun Zhan, Songyang Lao

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2835-2859, 2023, DOI:10.32604/cmc.2023.038670

    Abstract No optimization algorithm can obtain satisfactory results in all optimization tasks. Thus, it is an effective way to deal with the problem by an ensemble of multiple algorithms. This paper proposes an ensemble of population-based metaheuristics (EPM) to solve single-objective optimization problems. The design of the EPM framework includes three stages: the initial stage, the update stage, and the final stage. The framework applies the transformation of the real and virtual population to balance the problem of exploration and exploitation at the population level and uses an elite strategy to communicate among virtual populations. The experiment tested two benchmark function… More >

  • Open Access

    ARTICLE

    New Denial of Service Attacks Detection Approach Using Hybridized Deep Neural Networks and Balanced Datasets

    Ouail Mjahed1,*, Salah El Hadaj1, El Mahdi El Guarmah1,2, Soukaina Mjahed1

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 757-775, 2023, DOI:10.32604/csse.2023.039111

    Abstract Denial of Service (DoS/DDoS) intrusions are damaging cyber-attacks, and their identification is of great interest to the Intrusion Detection System (IDS). Existing IDS are mainly based on Machine Learning (ML) methods including Deep Neural Networks (DNN), but which are rarely hybridized with other techniques. The intrusion data used are generally imbalanced and contain multiple features. Thus, the proposed approach aims to use a DNN-based method to detect DoS/DDoS attacks using CICIDS2017, CSE-CICIDS2018 and CICDDoS 2019 datasets, according to the following key points. a) Three imbalanced CICIDS2017-2018-2019 datasets, including Benign and DoS/DDoS attack classes, are used. b) A new technique based… More >

  • Open Access

    ARTICLE

    Selection of Metaheuristic Algorithm to Design Wireless Sensor Network

    Rakhshan Zulfiqar1,2, Tariq Javed1, Zain Anwar Ali2,*, Eman H. Alkhammash3, Myriam Hadjouni4

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 985-1000, 2023, DOI:10.32604/iasc.2023.037248

    Abstract The deployment of sensor nodes is an important aspect in mobile wireless sensor networks for increasing network performance. The longevity of the networks is mostly determined by the proportion of energy consumed and the sensor nodes’ access network. The optimal or ideal positioning of sensors improves the portable sensor networks effectiveness. Coverage and energy usage are mostly determined by successful sensor placement strategies. Nature-inspired algorithms are the most effective solution for short sensor lifetime. The primary objective of work is to conduct a comparative analysis of nature-inspired optimization for wireless sensor networks (WSNs’) maximum network coverage. Moreover, it identifies quantity… More >

  • Open Access

    ARTICLE

    An Efficient Approach Based on Remora Optimization Algorithm and Levy Flight for Intrusion Detection

    Abdullah Mujawib Alashjaee*

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 235-254, 2023, DOI:10.32604/iasc.2023.036247

    Abstract With the recent increase in network attacks by threats, malware, and other sources, machine learning techniques have gained special attention for intrusion detection due to their ability to classify hundreds of features into normal system behavior or an attack attempt. However, feature selection is a vital preprocessing stage in machine learning approaches. This paper presents a novel feature selection-based approach, Remora Optimization Algorithm-Levy Flight (ROA-LF), to improve intrusion detection by boosting the ROA performance with LF. The developed ROA-LF is assessed using several evaluation measures on five publicly available datasets for intrusion detection: Knowledge discovery and data mining tools competition,… More >

  • Open Access

    ARTICLE

    Improved Supervised and Unsupervised Metaheuristic-Based Approaches to Detect Intrusion in Various Datasets

    Ouail Mjahed1,*, Salah El Hadaj1, El Mahdi El Guarmah1,2, Soukaina Mjahed1

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 265-298, 2023, DOI:10.32604/cmes.2023.027581

    Abstract Due to the increasing number of cyber-attacks, the necessity to develop efficient intrusion detection systems (IDS) is more imperative than ever. In IDS research, the most effectively used methodology is based on supervised Neural Networks (NN) and unsupervised clustering, but there are few works dedicated to their hybridization with metaheuristic algorithms. As intrusion detection data usually contains several features, it is essential to select the best ones appropriately. Linear Discriminant Analysis (LDA) and t-statistic are considered as efficient conventional techniques to select the best features, but they have been little exploited in IDS design. Thus, the research proposed in this… More >

  • Open Access

    ARTICLE

    An Improved Elite Slime Mould Algorithm for Engineering Design

    Li Yuan1, Jianping Ji1, Xuegong Liu1, Tong Liu2, Huiling Chen3, Deng Chen4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 415-454, 2023, DOI:10.32604/cmes.2023.026098

    Abstract The Swarm intelligence algorithm is a very prevalent field in which some scholars have made outstanding achievements. As a representative, Slime mould algorithm (SMA) is widely used because of its superior initial performance. Therefore, this paper focuses on the improvement of the SMA and the mitigation of its stagnation problems. For this aim, the structure of SMA is adjusted to develop the efficiency of the original method. As a stochastic optimizer, SMA mainly stimulates the behavior of slime mold in nature. For the harmony of the exploration and exploitation of SMA, the paper proposed an enhanced algorithm of SMA called… More > Graphic Abstract

    An Improved Elite Slime Mould Algorithm for Engineering Design

  • Open Access

    ARTICLE

    Smart Fraud Detection in E-Transactions Using Synthetic Minority Oversampling and Binary Harris Hawks Optimization

    Chandana Gouri Tekkali, Karthika Natarajan*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3171-3187, 2023, DOI:10.32604/cmc.2023.036865

    Abstract Fraud Transactions are haunting the economy of many individuals with several factors across the globe. This research focuses on developing a mechanism by integrating various optimized machine-learning algorithms to ensure the security and integrity of digital transactions. This research proposes a novel methodology through three stages. Firstly, Synthetic Minority Oversampling Technique (SMOTE) is applied to get balanced data. Secondly, SMOTE is fed to the nature-inspired Meta Heuristic (MH) algorithm, namely Binary Harris Hawks Optimization (BinHHO), Binary Aquila Optimization (BAO), and Binary Grey Wolf Optimization (BGWO), for feature selection. BinHHO has performed well when compared with the other two. Thirdly, features… More >

  • Open Access

    ARTICLE

    Dark Forest Algorithm: A Novel Metaheuristic Algorithm for Global Optimization Problems

    Dongyang Li1, Shiyu Du2,*, Yiming Zhang2, Meiting Zhao3

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2775-2803, 2023, DOI:10.32604/cmc.2023.035911

    Abstract Metaheuristic algorithms, as effective methods for solving optimization problems, have recently attracted considerable attention in science and engineering fields. They are popular and have broad applications owing to their high efficiency and low complexity. These algorithms are generally based on the behaviors observed in nature, physical sciences, or humans. This study proposes a novel metaheuristic algorithm called dark forest algorithm (DFA), which can yield improved optimization results for global optimization problems. In DFA, the population is divided into four groups: highest civilization, advanced civilization, normal civilization, and low civilization. Each civilization has a unique way of iteration. To verify DFA’s… More >

  • Open Access

    ARTICLE

    An Improved Reptile Search Algorithm Based on Cauchy Mutation for Intrusion Detection

    Salahahaldeen Duraibi*

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2509-2525, 2023, DOI:10.32604/csse.2023.036119

    Abstract With the growth of the discipline of digital communication, the topic has acquired more attention in the cybersecurity medium. The Intrusion Detection (ID) system monitors network traffic to detect malicious activities. The paper introduces a novel Feature Selection (FS) approach for ID. Reptile Search Algorithm (RSA)—is a new optimization algorithm; in this method, each agent searches a new region according to the position of the host, which makes the algorithm suffers from getting stuck in local optima and a slow convergence rate. To overcome these problems, this study introduces an improved RSA approach by integrating Cauchy Mutation (CM) into the… More >

Displaying 1-10 on page 1 of 36. Per Page