A modified binary version of aphid–ant mutualism for feature selection: a COVID-19 case study

Eslami, N; Yazdani, Saeed; Mirzaei, Mohammad; Hadavandi, Esmaeil

doi:10.1093/jcde/qwad009

Cited by 3 publications

(1 citation statement)

References 113 publications

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“…Metaheuristic algorithms have gained significant traction across various domains due to their ability to provide robust and near-optimal solutions for complex optimization problems [48][49][50][51][52]. Their advantages include flexibility, simplicity, and the capacity to handle large-scale and nonlinear problems effectively [53][54][55][56][57][58][59]. These algorithms have demonstrated success in various fields such as engineering, logistics, and artificial intelligence [60][61][62][63][64][65][66].…”

Section: Solution Approachmentioning

confidence: 99%

Strategies for Humanitarian Logistics and Supply Chain in Organizational Contexts: Pre- and Post-Disaster Management Perspectives

Aghsami,

Sharififar,

Markazi Moghaddam

et al. 2024

Systems

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Every organization typically comprises various internal components, including regional branches, operations centers/field offices, major transportation hubs, and operational units, among others, housing a population susceptible to disaster impacts. Moreover, organizations often possess resources such as staff, various vehicles, and medical facilities, which can mitigate human casualties and address needs across affected areas. However, despite the importance of managing disasters within organizational networks, there remains a research gap in the development of mathematical models for such scenarios, specifically incorporating operations centers/field offices and external stakeholders as relief centers. Addressing this gap, this study examines an optimization model for both before and after disaster planning in a humanitarian supply chain and logistical framework within an organization. The affected areas are defined as regional branches, operational units, major transportation hubs, operations centers/field offices, external stakeholders, and medical facilities. A mixed-integer nonlinear model is formulated to minimize overall costs, considering factors such as penalty costs for untreated injuries and demand, delays in rescue and relief item distribution operations, and waiting costs for the injured in emergency medical vehicles and air ambulances. The model is implemented using GAMS software 47.1.0 for various test problems across different scales, with the Grasshopper Optimization Algorithm proposed for larger-scale scenarios. Numerical examples are provided to show the effectiveness and feasibility of the proposed model and to validate the metaheuristic approach. Sensitivity analysis is conducted to assess the model’s performance under different conditions, and key managerial insights and implications are discussed.

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Section: Solution Approachmentioning

confidence: 99%

Strategies for Humanitarian Logistics and Supply Chain in Organizational Contexts: Pre- and Post-Disaster Management Perspectives

Aghsami,

Sharififar,

Markazi Moghaddam

et al. 2024

Systems

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Utilizing bee foraging behavior in mutational salp swarm for feature selection: a study on return-intentions of overseas Chinese after COVID-19

Xing,

Zhao,

Chen

et al. 2023

Journal of Computational Design and Engineering

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We present a Bee Foraging Behavior Driven Mutational Salp Swarm Algorithm (BMSSA) based on an improved bee foraging strategy and an unscented mutation strategy. The improved bee foraging strategy is leveraged in the follower location update phase to break the fixed range search of SSA, while the unscented mutation strategy on the optimal solution is employed to enhance the quality of the optimal solution. Extensive experimental results on public CEC 2014 benchmark functions validate that the proposed BMSSA performs better than nine well-known metaheuristic methods and seven state-of-the-art algorithms. The Binary BMSSA algorithm is further proposed for feature selection by using BMSSA as the selection strategy and support vector machine as the classifier. Experimental comparisons on twelve UCI datasets demonstrate the superiority of binary BMSSA. Finally, we collected a dataset on the return-intentions of overseas Chinese after COVID-19 through an anonymous online questionnaire and performed a case study by setting up a binary BMSSA-based feature selection optimization model. . The case study shows that the development prospects, the family and job in the place of residence, seeking opportunities in China, and the possible time to return to China are critical factors influencing the willingness to return to China after COVID-19.

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Advancing feature ranking with hybrid feature ranking weighted majority model: a weighted majority voting strategy enhanced by the Harris hawks optimizer

Aljohani,

AbdulAzeem,

Balaha

et al. 2024

Journal of Computational Design and Engineering

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Feature selection (FS) is vital in improving the performance of machine learning (ML) algorithms. Despite its importance, identifying the most important features remains challenging, highlighting the need for advanced optimization techniques. In this study, we propose a novel hybrid feature ranking technique called the Hybrid Feature Ranking Weighted Majority Model (HFRWM2). HFRWM2 combines ML models with the Harris Hawks Optimizer (HHO) metaheuristic. HHO is known for its versatility in addressing various optimization challenges, thanks to its ability to handle continuous, discrete, and combinatorial optimization problems. It achieves a balance between exploration and exploitation by mimicking the cooperative hunting behavior of Harris’s hawks, thus thoroughly exploring the search space and converging towards optimal solutions. Our approach operates in two phases. First, an odd number of ML models, in conjunction with HHO, generate feature encodings along with performance metrics. These encodings are then weighted based on their metrics and vertically aggregated. This process produces feature rankings, facilitating the extraction of the top-K features. The motivation behind our research is twofold: to enhance the precision of ML algorithms through optimized FS and to improve the overall efficiency of predictive models. To evaluate the effectiveness of HFRWM2, we conducted rigorous tests on two datasets: “Australian” and “Fertility.” Our findings demonstrate the effectiveness of HFRWM2 in navigating the search space and identifying optimal solutions. We compared HFRWM2 against twelve other feature ranking techniques and found it to outperform them. This superiority was particularly evident in the graphical comparison of the “Australian” dataset, where HFRWM2 showed significant advancements in feature ranking.

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A modified binary version of aphid–ant mutualism for feature selection: a COVID-19 case study

Cited by 3 publications

References 113 publications

Strategies for Humanitarian Logistics and Supply Chain in Organizational Contexts: Pre- and Post-Disaster Management Perspectives

Strategies for Humanitarian Logistics and Supply Chain in Organizational Contexts: Pre- and Post-Disaster Management Perspectives

Utilizing bee foraging behavior in mutational salp swarm for feature selection: a study on return-intentions of overseas Chinese after COVID-19

Advancing feature ranking with hybrid feature ranking weighted majority model: a weighted majority voting strategy enhanced by the Harris hawks optimizer

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