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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 problems.This 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 process.The 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 functions.Moreover,GEA has been applied to several real-parameter engineering optimization problems to evaluate its effectiveness.In addition,to demonstrate the applicability and robustness of GEA,a comprehensive investigation is performed for a fair comparison with other standard optimization methods.The 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 RCGA.Note 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)

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Investigation of the preparation methodologies of Pd-Cu single atom alloy catalysts for selective hydrogenation of acetylene

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Frontiers of Chemical Science and Engineering 2015年第4期9卷 442-449页

作者： Xinxiang Cao arash mirjalili James Wheeler Wentao Xie Ben W.-L. Jang Department of Chemistry Texas A&M University-Commerce Commerce TX 75429-3011 USA

Galvanic replacement, co-impregnation and sequential impregnation have been employed to prepare Pd-Cu bimetallic catalysts with less than 1 wt-% Cu and ca. 0.03 wt-% Pd for selective hydrogenation of acetylene in excess ethylene. High angle annular dark field-scanning transmission electron microscopy （HAADF-STEM） and H2 chemisorption results confirmed that Pd-Cu singleatom alloy structures were constructed in all three bimetallic catalysts. Catalytic tests indicated that when the conversion of acetylene was above 99%, the selectivity of ethylene of these three single atom alloy catalysts was still more than 73%. Furthermore, the single atom alloy catalyst prepared by sequential incipient wetness impregnation was found to have the best stability among the three procedures used.

关键词： palladium-copper bimetallic catalyst singleatom alloy acetylene hydrogenation HAADF-STEM H2pulse chemisorption

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Preparation and investigation of Pd doped Cu catalysts for selective hydrogenation of acetylene

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Frontiers of Chemical Science and Engineering 2020年第4期14卷 522-533页

作者： Xinxiang Cao Tengteng Lyu Wentao Xie arash mirjalili Adelaide Bradicich Ricky Huitema Ben W.-L.Jang Jong K.Keum Karren More Changjun Liu Xiaoliang Yan Department of Chemistry Texas A&M University-CommerceCommerceTX 75429-3011.USA Laboratory for Development&Application of Cold Plasma Technology College of Chemistry and Chemical EngineeringLuoyang Normnal UniversityLuoyang 471022China Collaborative Innovation Center of Chemical Science and Engineering School of Chemical Engineering and TechnologyTianjin UniversityTianjin 300072China Center for Nanophase Materials Sciences and Chemical Science Division Oak Ridge National LaboratoryOak RidgeTN 37831USA College of Chemistry and Chemical Engineering Taiyuan University of TechnologyTaiyuan 030024China

A series of PdCu bimetallic catalysts with low Cu and Pd loadings and different Cu:Pd atomic ratios were prepared by conventionally sequential impregnation(CSD)and modified sequential impregnation(MSI)of Cu and Pd for selective hydrogenation of acetylene.Characterization indicates that the supported copper(I)nitrate in the PdCu bimetallic catalysts prepared by MSI can be directly reduced to Cu metal particles due to the hydrogen spillover from Pd to Cu(NO3)2 crystals.In addition,for the catalysts prepared by MSI,Pd atoms can form PdCu alloy on the surface of metal particles,however,for the catalysts prepared by CSI,Pd tends to migrate and exist below the surface layer of Cu.Reaction results indicate that compared with CSI,the MSI method enables samples to possess preferable stability as well as comparable reaction activity.This should be due to the MSI method in favor of the formation of PdCu alloy on the surface of metal particles.Moreover,even Pd loading is super low,<0.045 wt-%in this study,by through adjusting Cu loading to an appropriate value,attractive reactivity and selectivity still can be achieved.

关键词： copper palladium catalysts acetylene selec-tive hydrogenation

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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 analysis.Although many metaheuristic-based algorithms for community detection have been proposed,they still cannot effectively fulfill large-scale and real-world networks.Thus,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 networks.In 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 solution.Then a novel Binary Distance Vector(BDV)is introduced to calculate the wolves’distances and adapt I-GWO for solving the discrete community detection problem.The 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 datasets.The experimental results were compared with the state-of-the-art algorithms and statistically analyzed using the Friedman and Wilcoxon tests.The 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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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 problems.There is also an improvement to the original version of BOA to alleviate its drawbacks before extending it into a multi-objective version.Due to better coverage and a well-distributed Pareto front,non-dominant rankings are applied to the modified BOA using the crowding distance strategy.Seven 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 problems.Various performance metrics,such as Generational Distance(GD),Inverted Generational Distance(IGD),Maximum Spread(MS),and Spacing(S),have been used for performance comparison.It 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 quantitatively.From 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

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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 mating.This article proposes a method based on Differential Evolution(DE)/current-to-best/1 for enhancing the FA's movement process.The proposed modification increases the global search ability and the convergence rates while maintaining a balance between exploration and exploitation by deploying the global best solution.However,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 loop.Additionally,the suggested algorithm’s sensitivity to the alpha parameter is reduced compared to the original FA.The 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 values.Additionally,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 algorithms.In all cases,GbFA provides the optimal result compared to other methods.Note 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

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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

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Optimized Ensemble Algorithm for Predicting Metamaterial Antenna Parameters

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Computers, Materials & Continua 2022年第6期71卷 4989-5003页

作者： El-Sayed M.El-kenawy Abdelhameed Ibrahim Seyedali mirjalili Yu-Dong Zhang Shaima Elnazer Rokaia M.Zaki Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Nile Higher Institute for Engineering and Technology MansouraEgypt Computer and Information Technology College Taif UniversityTaifSaudi Arabia Higher Institute of Engineering and Technology Kafrelsheikh Department of Electrical Engineering Shoubra Faculty of EngineeringBenha UniversityEgypt

Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve performance.Metamaterial antennas can overcome the bandwidth constraint associated with tiny antennas.Machine learning is receiving a lot of interest in optimizing solutions in a variety of areas.Machine learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today’s technology.The accuracy of the forecast is mostly determined by the model used.The purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial Antenna.Support Vector Machines(SVM),Random Forest,K-Neighbors Regressor,and Decision Tree Regressor were utilized as the basic models.The Adaptive Dynamic Polar Rose Guided Whale Optimization method,named AD-PRS-Guided WOA,was used to pick the optimal features from the datasets.The suggested model is compared to models based on five variables and to the average ensemble model.The findings indicate that the presented model using Random Forest results in a Root Mean Squared Error(RMSE)of(0.0102)for bandwidth and RMSE of(0.0891)for gain.This is superior to other models and can accurately predict antenna bandwidth and gain.

关键词： Metamaterial antenna machine learning ensemble model feature selection guided whale optimization support vector machines

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Nano-TiO_2:An eco-friendly alternative for the synthesis of quinoxalines

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Chinese Chemical Letters 2011年第6期22卷 753-756页

作者： Bi Bi Fatemeh mirjalili Ali Akbari Department of Chemistry College of Science Yazd University

Nano-TiO_2 as an eco-friendly and efficient nanocatalyst was applied for quinoxaline preparation with improved yield.In this protocol,diketones and 1,2-diamines were condensed in the presence of catalyst at room tempe... 详细信息

关键词： Nano-TiO_2 Quinoxalines 1 2-Diketones 1 2-Diamines

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Meson polarized distribution function and mass dependence of the nucleon parton densities

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Chinese Physics C 2014年第8期38卷 7-17页

作者： A.mirjalili K.Keshavarzian Physics Department Yazd University

The polarized distribution functions of mesons, including pion, kaon and eta, using the proton structure function, are calculated. We are looking for a relationship between the polarized distribution of mesons and the polarized structure of nucleons. We show that the meson polarized parton distributions leads to zero total spin for the concerned mesons, considering the orbital angular momentum of quarks and gluons inside the meson. Two separate Monte Carlo algorithms are applied to compute the polarized parton distributions of the kaon. Via the mass dependence of quark distributions, the distribution function of the eta meson is obtained. A new method by which the polarized sea quark distributions of protons are evolved separately which cannot be performed easily using the standard solution of DGLAP equations - is introduced. The mass dependence of these distributions is obtained, using the renormalization group equation which makes their evolutions more precise. Comparison between the evolved distributions and the available experimental data validates the suggested solutions for separated evolutions.

关键词： Valon model polarized chiral quark model constituent quark renormalization group equation evolution operator

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