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Erratum to:Recent Advances of Chimp Optimization Algorithm:Variants and Applications

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Journal of Bionic Engineering 2024年第3期21卷 1618-1618页

作者： mohammad Sh.Daoud mohammad shehab Laith Abualigah mohammad Alshinwan Mohamed Abd Elaziz Mohd Khaled Yousef Shambour Diego Oliva mohammad A.Alia Raed Abu Zitar College of Engineering Al Ain University112612 Abu DhabiUnited Arab Emirates College of Computer Sciences and Informatics Amman Arab UniversityAmman 11953Jordan Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq 25113Jordan 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 School of Computer Sciences Universiti Sains Malaysia11800 Pulau PinangMalaysia Faculty of Information Technology Applied Science Private UniversityAmman 11931Jordan Department of Mathematics Faculty of ScienceZagazig UniversityZagazig 7120001Egypt The Custodian of the Two Holy Mosques Institute for Hajj and Umrah Research Umm Al-Qura UniversityMakkah 24382Saudi Arabia Depto.de Innovacio′n Basada en la Informacio′n y el Conocimiento Universidad de GuadalajaraCUCEI45129 GuadalajaraMexico Cyber Security Department Faculty of Science and Information TechnologyAl Zaytoonah University of JordanAmman 11733Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi38044 Abu DhabiUnited Arab Emirates School of Engineering and Technology Sunway University Malaysia27500 Petaling JayaMalaysia

Erratum to:J Bionic Eng https://***/10.1007/*** this article the statement in the Funding information section was incorrectly given as‘22UQU4361183DSR03’and should have read‘23UQU4361183DSR03’.

关键词： statement section correctly

Hybrid Modified Chimp Optimization Algorithm and Reinforcement Learning for Global Numeric Optimization

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Journal of Bionic Engineering 2023年第6期20卷 2896-2915页

作者： mohammad ShDaoud mohammad shehab Laith Abualigah Cuong-Le Thanh College of Engineering Al Ain University112612 Abu DhabiUnited Arab Emirates College of Computer Sciences and Informatics Amman Arab UniversityAmman 11953Jordan Center for Engineering Application Technology Solutions Ho Chi Minh City Open UniversityHo Chi Minh City 700000Vietnam

Chimp Optimization Algorithm(ChOA)is one of the most efficient recent optimization algorithms,which proved its ability to deal with different problems in various ***,ChOA suffers from the weakness of the local search technique which leads to a loss of diversity,getting stuck in a local minimum,and procuring premature *** response to these defects,this paper proposes an improved ChOA algorithm based on using Opposition-based learning(OBL)to enhance the choice of better solutions,written as ***,utilizing Reinforcement Learning(RL)to improve the local research technique of OChOA,called *** way effectively avoids the algorithm falling into local *** performance of the proposed RLOChOA algorithm is evaluated using the Friedman rank test on a set of CEC 2015 and CEC 2017 benchmark functions problems and a set of CEC 2011 real-world *** results and statistical experiments show that RLOChOA provides better solution quality,convergence accuracy and stability compared with other state-of-the-art algorithms.

关键词： Chimp optimization algorithm Reinforcement learning Disruption operator Opposition-based learning CEC 2011 real-world problems CEC 2015 and CEC 2017 benchmark functions problems

Recent Advances of Chimp Optimization Algorithm:Variants and Applications

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Journal of Bionic Engineering 2023年第6期20卷 2840-2862页

作者： mohammad Sh.Daoud mohammad shehab Laith Abualigah mohammad Alshinwan Mohamed Abd Elaziz Mohd Khaled Yousef Shambour Diego Oliva mohammad AAlia Raed Abu Zitar College of Engineering Al Ain University112612Abu DhabiUnited Arab Emirates College of Computer Sciences and Informatics Amman Arab UniversityAmman11953Jordan Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains Malaysia11800Pulau PinangMalaysia Faculty of Information Technology Applied Science Private UniversityAmman 11931Jordan Department of Mathematics Faculty of ScienceZagazig UniversityZagazig 7120001Egypt The Custodian of the Two Holy Mosques Institute for Haj and Umrah Research Umm Al-Qura UniversityMakkah 24382Saudi Arabia Depto.de Innovacio'n Basada en la Informacio'n y el Conocimiento Universidad de GuadalajaraCUCEI45129 GuadalajaraMexico Cyber Security Department Faculty of Science and Information TechnologyAl Zaytoonah University of JordanAmman 11733Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi38044 Abu DhabiUnited Arab Emirates School of Engineering and Technology Sunway University Malaysia27500Petaling JayaMalaysia

Chimp Optimization Algorithm(ChOA)is one of the recent metaheuristics swarm intelligence *** has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods:it has very few parameters,and no derivation information is required in the initial ***,it is simple,easy to use,flexible,scalable,and has a special capability to strike the right balance between exploration and exploitation during the search which leads to favorable ***,the ChOA has recently gained a very big research interest with tremendous audiences from several domains in a very short ***,in this review paper,several research publications using ChOA have been overviewed and ***,introductory information about ChOA is provided which illustrates the natural foundation context and its related optimization conceptual *** main operations of ChOA are procedurally discussed,and the theoretical foundation is ***,the recent versions of ChOA are discussed in detail which are categorized into modified,hybridized,and paralleled *** main applications of ChOA are also thoroughly *** applications belong to the domains of economics,image processing,engineering,neural network,power and energy,networks,*** of ChOA is also *** review paper will be helpful for the researchers and practitioners of ChOA belonging to a wide range of audiences from the domains of optimization,engineering,medical,data mining,and *** well,it is wealthy in research on health,environment,and public ***,it will aid those who are interested by providing them with potential future research.

关键词： Artificial intelligence Nature-inspired optimization algorithms Chimp optimization algorithm Optimization problems

Comparative Study between Microscopic and Endoscopic Trans-Sphenoidal Surgery for Growth Hormone-Secreting Pituitary Adenomas with Super-Sellar Extension

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Open Journal of Modern Neurosurgery 2020年第4期10卷 393-402页

作者： Ahmed Sabry Abdelkader Ibrahim Abdel Fattah Abou Alfotoh shehab mohammad Fathy Eissa Neurosurgery Department Nasr City Hospital of Health Insurance Cairo Egypt Neurosurgery Department Faculty of Medicine Al-Azhar University Cairo Egypt

Background: Advances in the era of modern micro-neurosurgery enabled a reduction of surgical invasiveness and brain retraction which has been defined as minimally invasive or keyhole surgery. Sinonasal endoscopy has brought radical changes in the concepts of pathophysiology and treatment of sinonasal aliments as well as surgical techniques. Aim of the Study: To compare between the use of endoscopic and microscopic trans-sphenoidal approach in resection of growth hormone-secreting pituitary adenomas with Suprasellar Extension. Patients and Methods: This is a prospective study. It had been conducted upon 20 patients having growth hormone secreting pituitary adenoma admitted to Neurosurgery department in Alzar University hospitals and Nasr City Insurance hospital from 2015 to 2018, divided into 2 groups;group A (10 cases) underwent endoscopic endonasal trans-sphenoidal pituitary adenoma resection, while group B (10 cases) operated upon using the standard microscopic trans-sphenoidal pituitary adenoma resection. The inclusion criteria were included: All patients with growth hormone secreting pituitary adenomas showing manifestations of acromegaly, mass effect or hormonal disturbance. Results: This study showed that improvement in outcome was higher in endoscopic group opposed to microscopic group (100% vs. 71% improved headache, 80% vs. 60% visual improvement, 75% vs. 20% fundus improvement and 60% vs. 30% field improvement). Conclusion: We concluded that fully endoscopic procedure result in improved rates of complete tumor removal and a reduced incidence of complications, when compared to the microscopic approach.

关键词： Acromegaly Pituitary Adenomas Endoscopic Trans-Sphenoidal Surgery Microscopic Trans-Sphenoidal Surgery