A Hybrid Algorithm Based on Squirrel Search Algorithm and Invasive Weed Optimization for Optimization

Hu, Hongping; Zhang, Linmei; Bai, Yanping; Wang, Peng; Tan, Xiaoyang

doi:10.1109/access.2019.2932198

Cited by 29 publications

(18 citation statements)

References 40 publications

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“…As the existing techniques faced an inappropriate cloud server selection and improper utilization of resources which caused a high error rate. In order to select the cloud server, the work uses the improved adaptive neuro-fuzzy inference system (I-ANFIS) [32]. The primary goal of I-ANFIS is to allocate an appropriate resource to the application or software with maximum resource utilization and minimum delay.…”

Section: Cloud Server Selectionmentioning

confidence: 99%

An Efficient Applications Cloud Interoperability Framework Using I-Anfis

Ramalingam¹,

Mohan

2021

Symmetry

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Cloud interoperability provides cloud services such as Software as a Service (SaaS) or customer system to communicate between the cloud providers. However, one of the most important barriers for existing researches was to adopt the application’s or data’s in cloud computing environments so as to obtain efficient cloud interoperability. This paper focuses on reliable cloud interoperability with a heterogeneous cloud computing resource environment with the objective of providing unilateral provision computing capabilities of a cloud server without the help of human interaction and allowing proper utilization of applications and services across various domains by using an effective cloud environment available at runtime. Moreover, the framework uses hybrid squirrel search genetic algorithm (HSSGA) to select the relevant features from a set of extracted features in order to eliminate irrelevant data which provides advantages of low computational time and less memory usage. Thereafter, for a proper selection of cloud server with respect to the selected features, the system has developed the improved adaptive neuro-fuzzy inference system (I-ANFIS) which provides accurate server selection and helps against uncertainties caused by servers or applications. Hence, the experimental result of the proposed framework gives an accuracy of 94.24% and remains more efficient compared to existing frameworks.

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Section: Cloud Server Selectionmentioning

confidence: 99%

An Efficient Applications Cloud Interoperability Framework Using I-Anfis

Ramalingam¹,

Mohan

2021

Symmetry

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“…Few recent algorithms that use SSA include Basu (2019), Wang et al (2019b, c) and Hu et al (2019).…”

Section: Squirrel Search Algorithmmentioning

confidence: 99%

A hybrid feature selection model based on improved squirrel search algorithm and rank aggregation using fuzzy techniques for biomedical data classification

Nagarajan

Babu

2021

Netw Model Anal Health Inform Bioinforma

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Feature selection has gained its importance due to the voluminous nature of the data. Owing to the computational complexity of wrapper approaches, the poor performance of filtering techniques, and the classifier dependency of embedded approaches, hybrid approaches are more commonly used in feature selection. Hybrid approaches use filtering metrics to reduce the computational complexity of wrapper algorithms and are proved to yield better feature subset. Though filtering metrics select the features based on their significance, most of them are unstable and biased towards the metric used. Moreover, the choice of filtering metrics depends largely on the distribution of data and data types. Biomedical datasets contain features with different distribution and types adding to the complexity in the choice of filtering metric. We address this problem by proposing a stable filtering method based on rank aggregation in hybrid feature selection model with Improved Squirrel search algorithm for biomedical datasets. Our proposed model is compared with other well-known and state-of-the-art methods and the results prove that our model exhibited superior performance in terms of classification accuracy and computational time. The robustness of our proposed model is proved by conducting experiments on nine biomedical datasets and with three different classifiers.

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“…In recent years, meta-heuristic algorithms have been popular in dealing with various matters, such as real engineering design problems [1], [2], signal denoising [3], [4], DOA estimation [5], air quality indices and mathematical optimization problems [5], [6], and fault diagnosis [7]. These meta-heuristic algorithms have advantages in their simplicity and easy implementation process, whose core operations do not rely on gradient information of the objective landscape or on the algorithms' mathematical traits [2], [8].…”

Section: Introductionmentioning

confidence: 99%

An Improved Harris’s Hawks Optimization for SAR Target Recognition and Stock Market Index Prediction

Bai

et al. 2020

IEEE Access

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Harris's hawks optimization (HHO) algorithm proposed in 2019 is a novel population-based, nature-inspired optimization paradigm that imitates the cooperative behavior and chasing style of Harris's hawks in nature called surprise pounce. Inspired by particle swarm optimization algorithm, velocity is added into the HHO algorithm in the exploration phase. The soft besiege with progressive rapid dives and the hard besiege with progressive rapid dives in the attacking stages of the HHO algorithm are improved by use of the crossover operator of the artificial tree algorithm. Thus the improved HHO algorithm is obtained, written as IHHO. The effectiveness of the IHHO algorithm is tested on 23 benchmark problems by comparison with the other 11 state-of-art meta-heuristic algorithms. The IHHO algorithm is used to optimize the parameters of support vector machine for synthetic aperture radar (SAR) target recognition and of the extreme learning machine for stock market index prediction by considering Google Trends. The comparable results show that the IHHO algorithm is very promising and has some competitive potential.

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A Hybrid Algorithm Based on Squirrel Search Algorithm and Invasive Weed Optimization for Optimization

Cited by 29 publications

References 40 publications

An Efficient Applications Cloud Interoperability Framework Using I-Anfis

An Efficient Applications Cloud Interoperability Framework Using I-Anfis

A hybrid feature selection model based on improved squirrel search algorithm and rank aggregation using fuzzy techniques for biomedical data classification

An Improved Harris’s Hawks Optimization for SAR Target Recognition and Stock Market Index Prediction

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