Application of an improved discrete crow search algorithm with local search and elitism on a humanitarian relief case

Eligüzel, İbrahim Miraç; Özceylan, Eren

doi:10.1007/s10462-021-10006-2

Cited by 12 publications

(6 citation statements)

References 41 publications

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“…In this section, the results of various hybrid deep architectures for intrusion detection in IoT systems are evaluated. The performance of the proposed NSBPSO algorithm is also evaluated in comparison with some widely-used and competitive metaheuristic algorithms, including the particle swarm optimization (PSO) algorithm, the artificial bee colony (ABC) algorithm, the iterated greedy algorithm (IG) [66], the improved crow search algorithm (I-CSA) [67], and the black widow optimization (BWO) algorithm [68]. All algorithms have been coded in MATLAB, and the calibration parameters of the algorithms have been shown in Table 2.…”

Section: Simulation Results On the Nid Datasetsmentioning

confidence: 99%

A Novel Deep Supervised Learning-Based Approach for Intrusion Detection in IoT Systems

Baniasadi

Rostami

Martín

et al. 2022

Sensors

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The Internet of Things (IoT) has become one of the most important concepts in various aspects of our modern life in recent years. However, the most critical challenge for the world-wide use of the IoT is to address its security issues. One of the most important tasks to address the security challenges in the IoT is to detect intrusion in the network. Although the machine/deep learning-based solutions have been repeatedly used to detect network intrusion through recent years, there is still considerable potential to improve the accuracy and performance of the classifier (intrusion detector). In this paper, we develop a novel training algorithm to better tune the parameters of the used deep architecture. To specifically do so, we first introduce a novel neighborhood search-based particle swarm optimization (NSBPSO) algorithm to improve the exploitation/exploration of the PSO algorithm. Next, we use the advantage of NSBPSO to optimally train the deep architecture as our network intrusion detector in order to obtain better accuracy and performance. For evaluating the performance of the proposed classifier, we use two network intrusion detection datasets named UNSW-NB15 and Bot-IoT to rate the accuracy and performance of the proposed classifier.

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Section: Simulation Results On the Nid Datasetsmentioning

confidence: 99%

A Novel Deep Supervised Learning-Based Approach for Intrusion Detection in IoT Systems

Baniasadi

Rostami

Martín

et al. 2022

Sensors

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“…In this approach, several whales The shrinking encircling mechanism facilitates exploitation of the solution space that is governed by a variable A using Eq. ( 11) and (12).…”

Section: Proposed Parallel Enhanced Whale Optimization Algorithmmentioning

confidence: 99%

“…Heuristic based solutions are tailored for specific problems and yield optimal results; however, for NP-hard problems, their viability is limited to below a certain threshold. On the contrary, meta-heuristic algorithms are problem independent techniques that prove promising in areas where integer programming cannot cope with the sheer number of feasible solutions in a near optimal time frame [12]. These algorithms provide an acceptable solution within a reasonable amount of time with the help of random search capability [13].…”

Section: Introductionmentioning

confidence: 99%

Parallel Enhanced Whale Optimization Algorithm for Independent Tasks Scheduling on Cloud Computing

Khan,

Aziz,

Osman

et al. 2024

IEEE Access

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Cloud computing has been imperative for computing systems worldwide since its inception. The researchers strive to leverage the efficient utilization of cloud resources to execute workload quickly in addition to providing better quality of service. Among several challenges on the cloud, task scheduling is one of the fundamental NP-hard problems. Meta-heuristic algorithms are extensively employed to solve task scheduling as a discrete optimization problem and therefore several meta-heuristic algorithms have been developed. However, they have their own strengths and weaknesses. Local optima, poor convergence, high execution time, and scalability are the predominant issues among meta-heuristic algorithms. In this paper, a parallel enhanced whale optimization algorithm is proposed to schedule independent tasks in the cloud with heterogeneous resources. The proposed algorithm improves solution diversity and avoids local optima using a modified encircling maneuver and an adaptive bubble net attacking mechanism. The parallelization technique keeps the execution time low despite its internal complexity. The proposed algorithm minimizes the makespan while improving resource utilization and throughput. It demonstrates the effectiveness of the proposed PEWOA against the best performing enhanced whale optimization algorithm (WOAmM) and Multi-core Random Matrix Particle Swarm Optimization (MRMPSO). The algorithm consistently produces better results with varying number of tasks on GoCJ dataset, indicating better scalability. The experiments are conducted in CloudSim utilizing a variety of GoCJ and HCSP instances. Various statistical tests are also conducted to evaluate the significance of the results.

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“…In the literature, optimization algorithms are proved to be successful in various applications (Chiang et al, 2014;Eligüzel & Özceylan, 2021;Precup et al, 2021;Preitl et al, 2006). In addition, optimization and meta-heuristic algorithms have been utilized successfully in text categorization.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel approach for text categorization by applying hybrid genetic bat algorithm through feature extraction and feature selection methods

Eligüzel

Çetinkaya

Dereli

2022

Expert Systems with Applications

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Application of an improved discrete crow search algorithm with local search and elitism on a humanitarian relief case

Cited by 12 publications

References 41 publications

A Novel Deep Supervised Learning-Based Approach for Intrusion Detection in IoT Systems

A Novel Deep Supervised Learning-Based Approach for Intrusion Detection in IoT Systems

Parallel Enhanced Whale Optimization Algorithm for Independent Tasks Scheduling on Cloud Computing

A novel approach for text categorization by applying hybrid genetic bat algorithm through feature extraction and feature selection methods

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