An Improved Harris Hawks Optimization Algorithm With Simulated Annealing for Feature Selection in the Medical Field

Elgamal, Zenab Mohamed; Yasin, Norizan Binti Mohd; Tubishat, Mohammad; Alswaitti, Mohammed; Mirjalili, Seyedali

doi:10.1109/access.2020.3029728

Cited by 106 publications

(51 citation statements)

References 46 publications

(68 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on literature investigation, many optimization algorithms were improved by combining them with local search algorithms (LSA). For example, in a study by [11], Elgamal et al improved Harris Hawks Optimization (HHO) Algorithm by SA and applied it for feature selection problem. In [2], the authors also improved water cycle optimization with SA and applied it for spam email detection.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Binary Dragonfly Algorithm with Simulated Annealing for Feature Selection

et al. 2021

Self Cite

View full text Add to dashboard Cite

There are various fields are affected by the growth of data dimensionality. The major problems which are resulted from high dimensionality of data including high memory requirements, high computational cost, and low machine learning classifier performance. Therefore, proper selection of relevant features from the set of available features and the removal of irrelevant features will solve these problems. Therefore, to solve the feature selection problem, an improved version of Dragonfly Algorithm (DA) is proposed by combining it with Simulated Annealing (SA), where the improved algorithm named BDA-SA. To solve the local optima problem of DA and enhance its ability in selecting the best subset of features for classification problems, Simulated Annealing (SA) was applied to the best solution found by Binary Dragonfly algorithm in attempt to improve its accuracy. A set of frequently used data sets from UCI repository was utilized to evaluate the performance of the proposed FS approach. Results show that the proposed hybrid approach, named BDA-SA, has superior performance when compared to wrapper-based FS methods including a feature selection method based on the basic version of Binary Dragonfly Algorithm.

show abstract

Section: Related Workmentioning

confidence: 99%

Hybrid Binary Dragonfly Algorithm with Simulated Annealing for Feature Selection

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Genetic algorithms tend to fall into sub-optimal solutions because of premature convergence. A Simulated annealing algorithm (SA) [29] is a heuristic neighborhood search algorithm designed to simulate the metal annealing process [30]. Because it can accept inferior solutions in the search, it is not easy to fall into the local optimal solution.…”

Section: Equipment-task Schedulermentioning

confidence: 99%

Research on Equipment Configuration Optimization of AGV Unmanned Warehouse

et al. 2021

View full text Add to dashboard Cite

To improve the throughput performance of the autonomous guide vehicle (AGV) unmanned storage system, a two-stage mathematical model was established. The model also considers the equipment configuration of the AGV unmanned storage system and the AGV-picking stations dual resource coordination scheduling problem. In the equipment-task scheduling phase, the model aims at the shortest order completion time, while the equipment configuration and layout model aims at the minimum equipment configuration and operating cost. To solve the two-stage model, a two-layer genetic algorithm was designed. The inner layer algorithm was used to optimize the task scheduling order of AGVs and picking stations. The results of the inner layer algorithm are fed back to the outer model to optimize configuration of the equipment and the picking station's layout. The inner and outer loops are combined to obtain the optimal equipment configuration scheme. Through the simulation study of an enterprise AGV unmanned storage case, the optimal equipment configuration combination and picking stations layout scheme are obtained. Compared with the equipment configuration scheme based on the principle the task scheduling in operation is another key link that affects the picking efficiency of an unmanned warehouse of random task scheduling and the principle of shortest job time first; The model can improve the efficiency of warehouse retrieval and minimize the number of equipment configurations. Finally, the improved genetic algorithm is used to solve the model, and the performance is compared with that of LINGO to verify the effectiveness of the improved algorithm.

show abstract

“…In this study, Harris Hawks optimisation (HHO) [17] with Simulated Annealing (SA) [18] and Chaotic initialisation or in short Chaotic Harris Hawks optimisation Algorithm (CHHO) [19] has been utilised for the purpose of FS. This leads to a better performance in terms of accuracy as compared to the CNN model used for classification.…”

Section: Introductionmentioning

confidence: 99%

Harris Hawks optimisation with Simulated Annealing as a deep feature selection method for screening of COVID-19 CT-scans

Bandyopadhyay

Basu

Cuevas

et al. 2021

Applied Soft Computing

View full text Add to dashboard Cite

Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It may cause severe ailments in infected individuals. The more severe cases may lead to death. Automated methods which can detect COVID-19 in radiological images can help in the screening of patients. In this work, a two-stage pipeline composed of feature extraction followed by feature selection (FS) for the detection of COVID-19 from CT scan images is proposed. For feature extraction, a state-of-the-art Convolutional Neural Network (CNN) model based on the DenseNet architecture is utilised. To eliminate the non-informative and redundant features, the meta-heuristic called Harris Hawks optimisation (HHO) algorithm combined with Simulated Annealing (SA) and Chaotic initialisation is employed. The proposed approach is evaluated on the SARS-COV-2 CT-Scan dataset which consists of 2482 CT scans. Without the Chaotic initialisation and the SA, the method gives an accuracy of around 98.42% which further increases to 98.85 % on the inclusion of the two and thus delivers better performance than many state-of-the-art methods and various meta-heuristic based FS algorithms. Also comparison has been drawn with many hybrid variants of meta-heuristic algorithms. Although HHO falls behind a few of the hybrid variants, when Chaotic initialisation and SA are incorporated into it, the proposed algorithm performs better than any other algorithm with which comparison has been drawn. The proposed algorithm decreases the number of features selected by around 75% , which is better than most of the other algorithms.

show abstract

An Improved Harris Hawks Optimization Algorithm With Simulated Annealing for Feature Selection in the Medical Field

Cited by 106 publications

References 46 publications

Hybrid Binary Dragonfly Algorithm with Simulated Annealing for Feature Selection

Hybrid Binary Dragonfly Algorithm with Simulated Annealing for Feature Selection

Research on Equipment Configuration Optimization of AGV Unmanned Warehouse

Harris Hawks optimisation with Simulated Annealing as a deep feature selection method for screening of COVID-19 CT-scans

Contact Info

Product

Resources

About