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Scheme-scale ambiguity in analysis of QCD observable

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Chinese Physics C 2010年第9期34卷 1523-1526页

作者： a.mirjalili B.A.Kniehl Physics Department Yazd University School of Particles and Accelerators Institute for Research in Fundamental Sciences (IPM) Institute for Theoretical Physics-II Hamburg University

The scheme-scale ambiguity that has plagued perturbative analysis in QCD remains on obstacle to making precise tests of the *** attempts have been done to resolve the scale *** this regard the BLM,EC,PMS and CORGI approaches are more *** try to employ these methods to fix the scale ambiguity at NLO,NNLO and even in more higher order *** optimizing the renormalization scale,there will be a possibility to predicate higher order *** present general results for predicted terms at any order,using different optimization *** observable as specific examples will be used to indicate the validity of scale fixing to predicate the higher order terms.

关键词： perturbative QCD scheme and scale dependence optimization approach predicted terms

QCD Predictions for Spin Dependence of Parton Distributions

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Communications in Theoretical Physics 2005年第6期43卷 1087-1098页

作者： S.AtashbarTehrani AliN.Khorramian a.mirjalili PhysicsDepartment PersianGulfUniversityBoushehrIran／／InstituteforStudiesinTheoreticalPhysicsandMathematics（IPM）P.O.Box19395-5531TehranIran PhysicsDepartment SemnanUniversitySemnanIran／／InstituteforStudiesinTheoreticalPhysicsandMathematics（IPM）P.O.Box19395-5531TehranIran physicsDepartment YazdUniversityYazdIran／／InstituteforStudiesinTheoreticalPhysicsandMathematics（IPM）P.O.Box19395-5531TehranIran

We introduce the polarized valon distributions to describe the spin dependence of parton *** polarized valon distributions in the proton and polarized parton distributions inside the valon can help to obtain polarized parton distribution in a proton. In order to be able to obtain the spin contribution of sea quarks, we need to improve the valon model We employ the Bernstein polynomial averages to obtain the unknown parameters which exist in our calculations. Our results for the polarized proton structure function, xg1p, are in good agreement with the experimental data for some values of Q2.

关键词： QCD预测部分子分布函数力矩结构波恩斯坦恩聚合体自旋影响粒子物理学

Nucleon/nuclei polarized structure function,using Jacobi polynomials expansion

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Chinese Physics C 2010年第9期34卷 1534-1537页

作者： a.mirjalili M.M.Yazdanpanah F.Taghavi-Shahri S.Atashbar Tehrani Physics Department Yazd University School of Particles and Accelerators Institute for Research in Fundamental Sciences (IPM) Physics Department Kerman Shahid Bahonar University

We use the constituent quark model to extract polarized parton distributions and finally polarized nucleon structure *** to limited experimental data which do not cover whole （x,Q 2 ） plane and to increase the reliability of the fitting,we employ the Jacobi orthogonal polynomials *** will be possible to extract the polarized structure functions for Helium,using the convolution of the nucleon polarized structure functions with the light cone moment *** results are in good agreement with available experimental data and some theoretical models.

关键词： perturbative QCD nucleon and nuclei structure function jacobi polynomials

Geyser Inspired Algorithm:A New Geological-inspired Meta-heuristic for Real-parameter and Constrained Engineering Optimization

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Journal of Bionic Engineering 2024年第1期21卷 374-408页

作者： Mojtaba Ghasemi Mohsen Zare Amir Zahedi Mohammad-Amin Akbari Seyedali mirjalili Laith Abualigah Department of Electronics and Electrical Engineering Shiraz University of TechnologyShirazIran Department of Electrical Engineering Faculty of EngineeringJahrom UniversityJahromFrasIran Department of Electrical and Computer Engineering Tarbiat Modares UniversityTehranIran Department of Electrical and Computer Engineering University ofCyprusNicosiaCyprus Centre for Artificial Intelligence Research and Optimisation Torrens University AustraliaBrisbaneQLD 4006Australia University Research and Innovation Center Obuda University1034 BudapestHungary Department of Electrical and Computer Engineering Lebanese American UniversityByblos 13-5053Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied Science Research Center Applied Science Private UniversityAmman 11931Jordan

Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world *** study presents a new optimization method based on an unusual geological phenomenon in nature,named Geyser inspired Algorithm(GEA).The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization *** efficiency and accuracy of GEA are verified using statistical examination and convergence rate comparison on numerous CEC 2005,CEC 2014,CEC 2017,and real-parameter benchmark ***,GEA has been applied to several real-parameter engineering optimization problems to evaluate its *** addition,to demonstrate the applicability and robustness of GEA,a comprehensive investigation is performed for a fair comparison with other standard optimization *** results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms,including ABC,BBO,PSO,and *** that the source code of the GEA is publicly available at https://***/projects/gea.

关键词： Nature-inspired algorithms Real-world and engineering optimization Mathematical modeling Geyser algorithm(GEA)

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

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

Non-dominated Sorting Advanced Butterfly Optimization Algorithm for Multi-objective Problems

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Journal of Bionic Engineering 2023年第2期20卷 819-843页

作者： Sushmita Sharma Nima Khodadadi Apu Kumar Saha Farhad Soleimanian Gharehchopogh Seyedali mirjalili Department of Mathematics National Institute of Technology AgartalaTripura799046India Department of Civil and Environment Engineering Florida International University MiamiMiamiFL33199USA Department of Computer Engineering Urmia BranchIslamic Azad UniversityUrmia***Iran Centre for Artificial Intelligence Research and Optimization Torrens UniversityFortitude ValleyQLD4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722South Korea

This paper uses the Butterfly Optimization Algorithm(BOA)with dominated sorting and crowding distance mechanisms to solve multi-objective optimization *** is also an improvement to the original version of BOA to alleviate its drawbacks before extending it into a multi-objective *** to better coverage and a well-distributed Pareto front,non-dominant rankings are applied to the modified BOA using the crowding distance *** benchmark functions and eight real-world problems have been used to test the performance of multi-objective non-dominated advanced BOA(MONSBOA),including unconstrained,constrained,and real-world design multiple-objective,highly nonlinear constraint *** performance metrics,such as Generational Distance(GD),Inverted Generational Distance(IGD),Maximum Spread(MS),and Spacing(S),have been used for performance *** is demonstrated that the new MONSBOA algorithm is better than the compared algorithms in more than 80%occasions in solving problems with a variety of linear,nonlinear,continuous,and discrete characteristics based on the Pareto front when compared *** all the analysis,it may be concluded that the suggested MONSBOA is capable of producing high-quality Pareto fronts with very competitive results with rapid convergence.

关键词： Multi-objective problems Butterfly optimization algorithm Non-dominated sorting Crowding distance

A Global Best-guided Firefly Algorithm for Engineering Problems

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Journal of Bionic Engineering 2023年第5期20卷 2359-2388页

作者： Mohsen Zare Mojtaba Ghasemi Amir Zahedi Keyvan Golalipour Soleiman Kadkhoda Mohammadi Seyedali mirjalili Laith Abualigah Department of Electrical Engineering Faculty of EngineeringJahrom UniversityJahrom***FrasIran Department of Electronics and Electrical Engineering Shiraz University of TechnologyShiraz***Iran Department of Electrical and Computer Engineering Tarbiat Modares UniversityTehran***Iran Department of Computer Engineering Sari BranchIslamic Azad UniversitySari***Iran Department of Electrical Engineering Urmia BranchIslamic Azad UniversityUrmia571696896Iran Centre for Artificial Intelligence Research and Optimisation Torrens University AustraliaBrisbaneQLD4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722South Korea Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan Faculty of Information Technology Middle East UniversityAmman11831Jordan School of Computer Sciences Universiti Sains Malaysia11800George TownPulau PinangMalaysia University Research and Innovation Center Obuda University1034BudapestHungary School of Engineering and Technology Sunway University MalaysiaPetaling Jaya27500Malaysia Applied science research center Applied science private universityAmman11931Jordan

The Firefly Algorithm(FA)is a highly efficient population-based optimization technique developed by mimicking the flashing behavior of fireflies when *** article proposes a method based on Differential Evolution(DE)/current-to-best/1 for enhancing the FA's movement *** proposed modification increases the global search ability and the convergence rates while maintaining a balance between exploration and exploitation by deploying the global best ***,employing the best solution can lead to premature algorithm convergence,but this study handles this issue using a loop adjacent to the algorithm's main ***,the suggested algorithm’s sensitivity to the alpha parameter is reduced compared to the original *** GbFA surpasses both the original and five-version of enhanced FAs in finding the optimal solution to 30 CEC2014 real parameter benchmark problems with all selected alpha ***,the CEC 2017 benchmark functions and the eight engineering optimization challenges are also utilized to evaluate GbFA’s efficacy and robustness on real-world problems against several enhanced *** all cases,GbFA provides the optimal result compared to other *** that the source code of the GbFA algorithm is publicly available at https://***/projects/gbfa.

关键词： Firefly algorithm New movement vector Global best-guided firefly algorithm Global optimization Engineering design

Enhanced degradation of reactive dyes using a novel carbon ceramic electrode based on copper nanoparticles and multiwall carbon nanotubes

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Chinese Journal of Chemical Engineering 2020年第1期28卷 318-327页

作者： Fatemeh Jafari Navid Nasirizadeh Mohammad mirjalili Department of Textile and Polymer Engineering Yazd BranchIslamic Azad UniversityYazdIran

A novel carbon ceramic electrode consisting of Cu NPs and MWCNT was developed to treat reactive orange 84(RO84) wastewater using ultrasound-assisted electrochemical degradation. The proposed electrode generated more hydroxyl radicals than non-nanoparticle electrodes did. In addition, a new electrochemical sensor was applied to determine residue RO84 in an aqueous medium during discoloration. This sensor is based on a glassy carbon electrode modified with gold nanourchins and graphene oxide and can detect RO84 concentration in the range of 1.0–1200 μmol·L^-1 with the detection limit of 0.03 μmol·L^-1. The degradation effects of the modified electrode on RO84 were evaluated systematically with different initial pH values, time durations, and amounts of Cu NPs and MWCNT. The results suggested that the removal efficiency of RO84 was approximately 83% after120 min of electrolysis in a phosphate buffer with pH 8.0 using a carbon ceramic electrode made with 4.0 wt%Cu NPs and 4.0 wt% MWCNT. The possible mechanism of RO84 degradation was monitored by gas chromatography–mass spectrometry, and degradation pathways were proposed.

关键词： Electrochemical degradation Carbon ceramic nanocomposite Reactive dye Copper nanoparticles