RETRACTED ARTICLE: Coronavirus herd immunity optimizer to solve classification problems

Alweshah, Mohammed

doi:10.1007/s00500-022-06917-z

Cited by 6 publications

(3 citation statements)

References 74 publications

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“…Additionally, two wrapper-based approaches were developed by combining the greedy search operator with and without the CHIO and then tested on 23 benchmark datasets and the real-world COVID-19 dataset [45]. The success of any feature selection technique relies on selecting a suitable classifier.…”

Section: Literature Reviewmentioning

confidence: 99%

A Binary Waterwheel Plant Optimization Algorithm for Feature Selection

Alhussan,

Abdelhamid,

El-Kenawy

et al. 2023

IEEE Access

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The vast majority of today's data is collected and stored in enormous databases with a wide range of characteristics that have little to do with the overarching goal concept. Feature selection is the process of choosing the best features for a classification problem, which improves the classification's accuracy. Feature selection is considered a multi-objective optimization problem with two objectives: boosting classification accuracy while decreasing the feature count. To efficiently handle the feature selection process, we propose in this paper a novel algorithm inspired by the behavior of waterwheel plants when hunting their prey and how they update their locations throughout exploration and exploitation processes. The proposed algorithm is referred to as the binary waterwheel plant algorithm (bWWPA). In this particular approach, the binary search space as well as the technique's mapping from the continuous to the discrete spaces are both represented in a new model. Specifically, the fitness and cost functions that are factored into the algorithm's evaluation are modeled mathematically. To assess the performance of the proposed algorithm, a set of extensive experiments were conducted and evaluated in terms of 30 benchmark datasets that include low, medium, and high dimensional features. In comparison to other recent binary optimization algorithms, the experimental findings demonstrate that the bWWPA performs better than the other competing algorithms. In addition, a statistical analysis is performed in terms of the one-way analysis-of-variance (ANOVA) and Wilcoxon signed-rank tests to examine the statistical differences between the proposed feature selection algorithm and compared algorithms. These experiments' results confirmed the proposed algorithm's superiority and effectiveness in handling the feature selection process.

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Section: Literature Reviewmentioning

confidence: 99%

A Binary Waterwheel Plant Optimization Algorithm for Feature Selection

Alhussan,

Abdelhamid,

El-Kenawy

et al. 2023

IEEE Access

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“…Study and analysis of the behavior of COVID-19 have inspired a lot of optimization algorithms such as coronavirus optimization algorithm [ 35 ], COVID-19-based optimization algorithm (C-19BOA) [ 36 ], Coronavirus optimization (CVO) [ 37 ], and coronavirus herd immunity optimizer (CHIO) [ 38 ]. While these algorithms proved an encouraging execution in treating optimization issues, CHIO may be considered as the most efficient because of its flexible and adaptable control parameters which allow effective investigating and exploring of search areas [ 39 ]. So, in this paper, the CHIO is used to solve the OPP problem considering zero injection buses.…”

Section: Introductionmentioning

confidence: 99%

Optimal location of PMUs for full observability of power system using coronavirus herd immunity optimizer

Alghassab,

Hatata,

Sokrana

et al. 2024

Heliyon

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“…The last category of swarm intelligence algorithms mimics the microorganism behaviors such as diffusion, foraging, reproduction, variation, invasion, infection, and phagocytosis. Some powerful microorganism-based swarm intelligence algorithms proposed in literature are Mushroom Reproduction Optimization (MRO) (Bidar et al 2018), Slime Mould Algorithm (SMA) (Li et al 2020), Coronavirus Herd Immunity Optimizer (CHIO) (Alweshah 2022), and so no.…”

Section: Introductionmentioning

confidence: 99%

Enhanced crow search algorithm with multi-stage search integration for global optimization problems

Peng

Zhang

et al. 2023

Soft Comput

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Crow search algorithm (CSA), as a new swarm intelligence algorithm that simulates the crows’ behaviors of hiding and tracking food in nature, performs well in solving many optimization problems. However, while handling complex and high-dimensional global optimization problems, CSA is apt to fall into evolutionary stagnation and has slow convergence speed, low accuracy, and weak robustness. This is mainly because it only utilizes a single search stage, where position updating relies on random following among individuals or arbitrary flight of individuals. To address these deficiencies, a CSA with multi-stage search integration (MSCSA) is presented. Chaos and multiple opposition-based learning techniques are first introduced to improve original population quality and ergodicity. The free foraging stage based on normal random distribution and Lévy flight is designed to conduct local search for enhancing the solution accuracy. And the following stage using mixed guiding individuals is presented to perform global search for expanding the search space through tracing each other among individuals. Finally, the large-scale migration stage based on the best individual and mixed guiding individuals concentrates on increasing the population diversity and helping the population jump out of local optima by moving the population to a promising area. All of these strategies form multi-level and multi-granularity balances between global exploration and local exploitation throughout the evolution. The proposed MSCSA is compared with a range of other algorithms, including original CSA, three outstanding variants of CSA, two classical meta-heuristics, and six state-of-the-art meta-heuristics covering different categories. The experiments are conducted based on the complex and high-dimensional benchmark functions CEC 2017 and CEC 2010, respectively. The experimental and statistical results demonstrate that MSCSA is competitive for tackling large-scale complicated problems, and is significantly superior to the competitors.

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RETRACTED ARTICLE: Coronavirus herd immunity optimizer to solve classification problems

Cited by 6 publications

References 74 publications

A Binary Waterwheel Plant Optimization Algorithm for Feature Selection

A Binary Waterwheel Plant Optimization Algorithm for Feature Selection

Optimal location of PMUs for full observability of power system using coronavirus herd immunity optimizer

Enhanced crow search algorithm with multi-stage search integration for global optimization problems

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