Shark Smell Optimization (SSO) Algorithm

Mohammad-Azari, Sahar; Bozorg‐Haddad, Omid; Chu, Xuefeng

doi:10.1007/978-981-10-5221-7_10

Cited by 35 publications

(21 citation statements)

References 9 publications

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“…The proposed model for secured CHS in IoT using SADO-BM was done in MATLAB 2020a. Here, the performance of the SADO-BM scheme was proven over Cat Swarm Optimization (CSO) [ 19 ], FF Firefly (FF) [ 2 ], Shark Smell Optimization (SSO) [ 20 ], Poor Rich Optimization (PRO) [ 21 ], Hunger Games Optimizer (HGS) [ 22 ], Bald Eagle Search (BES) [ 23 ], Black Widow Optimization (BWO) [ 24 ], Fuzzy + Harris Hawks Optimization (HHO) [ 25 ], Adaptive Neuro-Fuzzy Inference System (ANFIS) + Self-Adaptive Jellyfish Search Optimizer (SA-JSO) [ 26 ], and Dingo Optimizer (DOX) [ 18 ] on wide-ranging metrics like delay, throughput, and so on. In addition, Table 3 presents the simulation parameters; this work considered 100, 250, 750, and 1000 nodes with 500, 1000, 1500, and 2000 rounds.…”

Section: Results and Discussionmentioning

confidence: 99%

Dingo Optimization Based Cluster Based Routing in Internet of Things

Aravind

Maddikunta

2022

Sensors

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The Wireless Sensor Network (WSN) is a collection of distinct, geographically distributed, Internet-connected sensors, which is capable of processing, analyzing, storing, and exchanging collected information. However, the Internet of Things (IoT) devices in the network are equipped with limited resources and minimal computing capability, resulting in energy conservation problems. Although clustering is an efficient method for energy saving in network nodes, the existing clustering algorithms are not effective due to the short lifespan of a network, an unbalanced load among the network nodes, and increased end-to-end delays. Hence, this paper proposes a novel cluster-based approach for IoT using a Self-Adaptive Dingo Optimizer with Brownian Motion (SDO-BM) technique to choose the optimal cluster head (CH) considering the various constraints such as energy, distance, delay, overhead, trust, Quality of Service (QoS), and security (high risk, low risk, and medium risk). If the chosen optimal CH is defective, then fault tolerance and energy hole mitigation techniques are used to stabilize the network. Eventually, analysis is done to ensure the progression of the SADO-BM model. The proposed model provides optimal results compared to existing models.

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Section: Results and Discussionmentioning

confidence: 99%

Dingo Optimization Based Cluster Based Routing in Internet of Things

Aravind

Maddikunta

2022

Sensors

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“…The presented IDS in the cloud with the RBF‐NN + HMPRO scheme was implemented in PYTHON and the outcomes were confirmed. Furthermore, the performance of the presented RBF‐NN + HMPRO scheme was computed over the conventional schemes such as SVM, 56 RF, 57 LSTM, 58 convolutional neural network (CNN), 59 butterfly migration‐monarch search algorithm (BM‐MSA), 60 TEHO‐DBN, 61 RBF‐NN, 49 RBF‐NN + WOA, 62 RBF‐NN + SSO, 63 RBF‐NN + BOA, 64 and RBF‐NN + PRO, 50 correspondingly. In addition, the datasets were collected from References 65,66.…”

Section: Resultsmentioning

confidence: 99%

An optimized radial bias function neural network for intrusion detection of distributed denial of service attack in the cloud

Varghese

Jose

2022

Concurrency and Computation

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Cloud system is a set of resources meant to provide cloud customers with on-demand services. Accessibility to cloud environment is provided through internet services, making data stored on cloud more accessible to attackers internally and externally. Several intrusion detection systems and authentication methods have been developed in previous studies to identify the intruders, however they are generally unsuccessful with certain drawbacks. Numerous existing researchers have focused on machine learning techniques for identifying intrusions. This article intends to introduce a new intrusion detection model in the cloud and it involves three processes like preprocessing, feature extraction and detection. Initially, preprocessing takes place and the preprocessed data are subjected to feature extraction process. The flow-based features, statistical, and higher-order statistical features with improved holoentropy features, and the technical indicators are extracted. After extracting the features, they are subjected to a detection process, where the optimized radial bias function neural network (RBF-NN) is exploited. For precise detection, the weight of RBF-NN is tuned optimally by (harmonic mean based poor and rich optimization) HMPRO Algorithm. The main aim of the proposed model is to detect attacks in the cloud.Finally, the performance of the presented scheme is computed over the existing approaches using the CICDDoS2019 and UNSW-NB_15 dataset in terms of different metrics.

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“…Although extant EFO 37 includes varied improvements, it endures specified limits regarding convergence rate and speed. To prevail over the disadvantages of traditional EFO, the theory of SSO 38 is combined with it to set up a new algorithm termed as SSI‐EHO. Hybrid optimization approaches are much more proficient for specified searching issues 27,39‐41…”

Section: Proposed Ssi‐eho Based Optimization For Optimal Mpa Designmentioning

confidence: 99%

Hybrid optimization model for design and optimization of microstrip patch antenna

Sarngadharan¹,

Narasimhamurthy²,

Sankaramoorthy

et al. 2022

Trans Emerging Tel Tech

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The antenna design for a specified resonant frequency necessitates the computation of optimal values of different sizes. This is a harder task for microstrip patch antenna (MPA) since there is no precise numerical formula that leads to accurate solutions for designing these antennas. Presently, bio‐inspired approaches are widely deployed in numerous antenna designs and it has revealed an immense assurance in handling the rising necessities of antenna engineering for overall cost, reduced size, and enhanced performances. This work aims to introduce an optimal MPA design, where the antenna elements like patch length, patch height, substrate width, and substrate length are optimally tuned by a new hybrid optimization model. For this, a new hybridized model known as shark smell integrated EHO is proposed. Eventually, the primacy of the suggested model is scrutinized via varied assessments.

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Shark Smell Optimization (SSO) Algorithm

Cited by 35 publications

References 9 publications

Dingo Optimization Based Cluster Based Routing in Internet of Things

Dingo Optimization Based Cluster Based Routing in Internet of Things

An optimized radial bias function neural network for intrusion detection of distributed denial of service attack in the cloud

Hybrid optimization model for design and optimization of microstrip patch antenna

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