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An Improved Binary Quantum-based Avian Navigation Optimizer Algorithm to Select Effective Feature Subset from Medical Data:A COVID-19 Case Study

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Journal of Bionic Engineering 2024年第1期21卷 426-446页

作者： Ali Fatahi Mohammad H.Nadimi-Shahraki hoda zamani Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityNajafabad ***Iran Big Data Research Center Najafabad BranchIslamic Azad UniversityNajafabad ***Iran

Feature Subset Selection(FSS)is an NP-hard problem to remove redundant and irrelevant features particularly from medical data,and it can be effectively addressed by metaheuristic ***,existing binary versions of metaheuristic algorithms have issues with convergence and lack an effective binarization method,resulting in suboptimal solutions that hinder diagnosis and prediction *** paper aims to propose an Improved Binary Quantum-based Avian Navigation Optimizer Algorithm(IBQANA)for FSS in medical data preprocessing to address the suboptimal solutions arising from binary versions of metaheuristic *** proposed IBQANA’s contributions include the Hybrid Binary Operator(HBO)and the Distance-based Binary Search Strategy(DBSS).HBO is designed to convert continuous values into binary solutions,even for values outside the[0,1]range,ensuring accurate binary *** the other hand,DBSS is a two-phase search strategy that enhances the performance of inferior search agents and accelerates *** combining exploration and exploitation phases based on an adaptive probability function,DBSS effectively avoids local *** effectiveness of applying HBO is compared with five transfer function families and thresholding on 12 medical datasets,with feature numbers ranging from 8 to 10,***'s effectiveness is evaluated regarding the accuracy,fitness,and selected features and compared with seven binary metaheuristic ***,IBQANA is utilized to detect *** results reveal that the proposed IBQANA outperforms all comparative algorithms on COVID-19 and 11 other medical *** proposed method presents a promising solution to the FSS problem in medical data preprocessing.

关键词： Feature subset selection Optimization Binary metaheuristic algorithms Bioinspired Machine learning Medical datasets

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CQFFA:A Chaotic Quasi-oppositional Farmland Fertility Algorithm for Solving Engineering Optimization Problems

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Journal of Bionic Engineering 2023年第1期20卷 158-183页

作者： Farhad Soleimanian Gharehchopogh Mohammad H.Nadimi-Shahraki Saeid Barshandeh Benyamin Abdollahzadeh hoda zamani Department of Computer Engineering Urmia BranchIslamic Azad UniversityUrmia969Iran Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityNajafabad517Iran Big Data Research Center Najafabad BranchIslamic Azad UniversityNajafabad517Iran Afagh Higher Education Institute Urmia969Iran

Farmland Fertility Algorithm(FFA)is a recent nature-inspired metaheuristic algorithm for solving optimization ***,FFA has some drawbacks:slow convergence and imbalance of diversification(exploration)and intensification(exploitation).An adaptive mechanism in every algorithm can achieve a proper balance between exploration and *** literature shows that chaotic maps are incorporated into metaheuristic algorithms to eliminate these ***,in this paper,twelve chaotic maps have been embedded into FFA to find the best numbers of prospectors to increase the exploitation of the best promising ***,the Quasi-Oppositional-Based Learning(QOBL)mechanism enhances the exploration speed and convergence rate;we name a CQFFA *** improvements have been made in line with the weaknesses of the FFA algorithm because the FFA algorithm has fallen into the optimal local trap in solving some complex problems or does not have sufficient ability in the intensification *** results obtained show that the proposed CQFFA model has been significantly *** is applied to twenty-three widely-used test functions and compared with similar state-of-the-art algorithms statistically and ***,the CQFFA algorithm has evaluated six real-world engineering *** experimental results showed that the CQFFA algorithm outperforms other competitor algorithms.

关键词： Nature-inspired algorithm Farmland fertility algorithm Chaotic maps Quasi Engineering optimization problems

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