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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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Multi-trial Vector-based Whale Optimization Algorithm

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Journal of Bionic Engineering 2024年第3期21卷 1465-1495页

作者： mohammad H.nadimi-Shahraki Hajar Farhanginasab Shokooh Taghian Ali Safaa Sadiq Seyedali Mirjalili Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityNajafabad***Iran Big Data Research Center Najafabad BranchIslamic Azad UniversityNajafabad***Iran Department of Computer Science Nottingham Trent UniversityNottinghamNG118NSUK Centre for Artificial Intelligence Research and Optimisation Torrens UniversityBrisbane4006Australia

The Whale Optimization Algorithm(WOA)is a swarm intelligence metaheuristic inspired by the bubble-net hunting tactic of humpback *** spite of its popularity due to simplicity,ease of implementation,and a limited number of parameters,WOA’s search strategy can adversely affect the convergence and equilibrium between exploration and exploitation in complex *** address this limitation,we propose a new algorithm called Multi-trial Vector-based Whale Optimization Algorithm(MTV-WOA)that incorporates a Balancing Strategy-based Trial-vector Producer(BS_TVP),a Local Strategy-based Trial-vector Producer(LS_TVP),and a Global Strategy-based Trial-vector Producer(GS_TVP)to address real-world optimization problems of varied degrees of ***-WOA has the potential to enhance exploitation and exploration,reduce the probability of being stranded in local optima,and preserve the equilibrium between exploration and *** the purpose of evaluating the proposed algorithm's performance,it is compared to eight metaheuristic algorithms utilizing CEC 2018 test ***,MTV-WOA is compared with well-stablished,recent,and WOA variant *** experimental results demonstrate that MTV-WOA surpasses comparative algorithms in terms of the accuracy of the solutions and convergence ***,we conducted the Friedman test to assess the gained results statistically and observed that MTV-WOA significantly outperforms comparative ***,we solved five engineering design problems to demonstrate the practicality of *** results indicate that the proposed MTV-WOA can efficiently address the complexities of engineering challenges and provide superior solutions that are superior to those of other algorithms.

关键词： Swarm intelligence algorithms Metaheuristic algorithms Optimization Engineering design problems Whale optimization algorithm

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An Effective Hybridization of Quantum-based Avian Navigation and Bonobo Optimizers to Solve Numerical and Mechanical Engineering Problems

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Journal of Bionic Engineering 2023年第3期20卷 1361-1385页

作者： mohammad H.nadimi-Shahraki Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityNajafabad***Iran Big Data Research Center Najafabad BranchIslamic Azad UniversityNajafabad***Iran

Hybrid metaheuristic algorithms play a prominent role in improving algorithms' searchability by combining each algorithm's advantages and minimizing any substantial shortcomings. The Quantum-based Avian Navigation Optimizer Algorithm (QANA) is a recent metaheuristic algorithm inspired by the navigation behavior of migratory birds. Different experimental results show that QANA is a competitive and applicable algorithm in different optimization fields. However, it suffers from shortcomings such as low solution quality and premature convergence when tackling some complex problems. Therefore, instead of proposing a new algorithm to solve these weaknesses, we use the advantages of the bonobo optimizer to improve global search capability and mitigate premature convergence of the original QANA. The effectiveness of the proposed Hybrid Quantum-based Avian Navigation Optimizer Algorithm (HQANA) is assessed on 29 test functions of the CEC 2018 benchmark test suite with different dimensions, 30, 50, and 100. The results are then statistically investigated by the Friedman test and compared with the results of eight well-known optimization algorithms, including PSO, KH, GWO, WOA, CSA, HOA, BO, and QANA. Ultimately, five constrained engineering optimization problems from the latest test suite, CEC 2020 are used to assess the applicability of HQANA to solve complex real-world engineering optimization problems. The experimental and statistical findings prove that the proposed HQANA algorithm is superior to the comparative algorithms.

关键词： Optimization Metaheuristic algorithms Evolutionary algorithm Quantum-based avian navigation optimizer algorithm Engineering optimization problems,Bionic algorithm

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High power and beam quality continuous-wave Nd:Gd VO_4 laser in-band diode-pumped at 912 nm

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Photonics Research 2017年第4期5卷 346-349页

作者： mohammad nadimi TANANT WARITANANT ARKADY MAJOR Department of Electrical and Computer Engineering University of Manitoba

We report on the performance of a continuous-wave Nd:Gd VO_4 laser in-band diode-pumped at 912 nm with high output power and excellent beam quality. The laser produced an output power of 19.8 W at 1063 nm with an optical efficiency of 59.3% and slope efficiency of 62.7%. The laser threshold was ~2.04 W of the absorbed pump power, and the laser output beam quality was ≤ 1.2 in the horizontal and vertical directions. The strength of thermal lensing at full output power(33.4 W of absorbed power) was measured to be an average of 8.6 *** is shown that thermal lensing is reduced by a factor of 2 with respect to the Nd:YVO_4 lasers, thus opening a way for further output-power scaling.

关键词： Nd Gd VO High power and beam quality continuous-wave Nd:Gd VO4 laser in-band diode-pumped at 912 nm wave

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Discrete Improved Grey Wolf Optimizer for Community Detection

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Journal of Bionic Engineering 2023年第5期20卷 2331-2358页

作者： mohammad H.nadimi-Shahraki Ebrahim Moeini Shokooh Taghian Seyedali Mirjalili Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityNajafabad***Iran Big Data Research Center Najafabad BranchIslamic Azad UniversityNajafabad***Iran Centre for Artificial Intelligence Research and Optimisation Torrens UniversityBrisbane4006Australia Yonsei Frontier Lab Yonsei UniversitySeoulSouth Korea

Detecting communities in real and complex networks is a highly contested topic in network *** many metaheuristic-based algorithms for community detection have been proposed,they still cannot effectively fulfill large-scale and real-world ***,this paper presents a new discrete version of the Improved Grey Wolf Optimizer(I-GWO)algorithm named DI-GWOCD for effectively detecting communities of different *** the proposed DI-GWOCD algorithm,I-GWO is first armed using a local search strategy to discover and improve nodes placed in improper communities and increase its ability to search for a better *** a novel Binary Distance Vector(BDV)is introduced to calculate the wolves’distances and adapt I-GWO for solving the discrete community detection *** performance of the proposed DI-GWOCD was evaluated in terms of modularity,NMI,and the number of detected communities conducted by some well-known real-world network *** experimental results were compared with the state-of-the-art algorithms and statistically analyzed using the Friedman and Wilcoxon *** comparison and the statistical analysis show that the proposed DI-GWOCD can detect the communities with higher quality than other comparative algorithms.

关键词： Community detection Complex network Optimization Metaheuristic algorithms Swarm intelligence algorithms Grey wolf optimizer algorithm

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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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Failure criterion effect on solid production prediction and selection of completion solution

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Journal of Rock Mechanics and Geotechnical Engineering 2017年第6期9卷 1123-1130页

作者： Dariush Javani Bernt Aadnoy Mahdi Rastegarnia Siavash nadimi mohammad Ali Aghighi Bijan Maleki Department of Mining Engineering Imam Khomeini International UniversityQazvinIran Department of Petroleum Engineering University of StavangerStavangerNorway Department of Petrophysics Homai Well Services CompanyTehranIran Department of Chemical Engineering Energy and Geoscience InstituteThe University of UtahSalt Lake CityUT 84112USA Department of Mining Engineering University of TehranTehranIran

Production of fines together with reservoir fluid is called solid production. It varies from a few grams or less per ton of reservoir fluid posing only minor problems, to catastrophic amount possibly leading to erosion and complete filling of the borehole. This paper assesses solid production potential in a carbonate gas reservoir located in the south of Iran. Petrophysical logs obtained from the vertical well were employed to construct mechanical earth model. Then, two failure criteria, i.e. Mohre Coulomb and Mogi-Coulomb,were used to investigate the potential of solid production of the well in the initial and depleted conditions of the reservoir. Using these two criteria, we estimated critical collapse pressure and compared them to the reservoir pressure. Solid production occurs if collapse pressure is greater than pore pressure. Results indicate that the two failure criteria show different estimations of solid production potential of the studied reservoir. Mohre Coulomb failure criterion estimated solid production in both initial and depleted conditions, where Mogi-Coulomb criterion predicted no solid production in the initial condition of reservoir. Based on Mogi-Coulomb criterion, the well may not require completion solutions like perforated liner, until at least 60% of reservoir pressure was depleted which leads to decrease in operation cost and time.

关键词： Solid production Mechanical earth model Failure criterion Reservoir depletion Completion solution

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