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A Novel Quantum Entanglement-Inspired meta-heuristic Framework for Solving Multimodal optimization Problems

Chinese Journal of Electronics 2021年第1期30卷 145-152页

作者： ZHAO Shijie MA Shilin GAO Leifu YU Dongmei Institute of optimization and Decision College of Science Liaoning Technical University Institute for optimization and Decision Analytics Liaoning Technical University

To solve Multimodal optimization problems(MOPs), a Novel Quantum entanglement-inspired meta-heuristic framework(NMF-QE) is proposed. Its main inspirations are two concepts of quantum physics:quantum entanglement and quantum superposition. When given Proto-born particles(PBPs) of a population, these two concepts are mathematically developed to generate twin-born and combination-born particles, respectively.And if any elite-born particles would be created by a local re-searching strategy. These three or four groups of particles come together as a whole search population of NMF-QE to realize exploration and exploitation of algorithms. To guarantee dynamical optimization capability of NMF-QE, the individual evolutionary mechanism of some existing meta-heuristics will be adopted to iteratively create PBPs. A selected meta-heuristic is coupled with NMF-QE to present its improved variant. Numerical results show that the proposed NMF-QE can effectively improve optimization performance of meta-heuristics on MOPs.

关键词： meta-heuristic optimization Quantum entanglement Long-distance effect Combination superposition Multimodal optimization

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Novel integration of extreme learning machine and improved Harris hawks optimization with particle swarm optimization-based mutation for predicting soil consolidation parameter

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Journal of Rock Mechanics and Geotechnical Engineering 2022年第5期14卷 1588-1608页

作者： Abidhan Bardhan Navid Kardani Abdel Kareem Alzo'ubi Bishwajit Roy Pijush Samui Amir HGandomi Department of Civil Engineering National Institute of Technology PatnaPatna800005India Discipline of Civil and Infrastructure Engineering School of EngineeringRoyal Melbourne Institute of Technology(RMIT)MelbourneVIC3001Australia Department of Civil Engineering Abu Dhabi UniversityAbu DhabiUnited Arab Emirates School of Computer Science Engineering and Technology Bennett UniversityGreater NoidaIndia Faculty of Engineering and Information Technology University of Technology SydneySydneyNSW2007Australia

The study proposes an improved Harris hawks optimization(IHHO) algorithm by integrating the standard Harris hawks optimization(HHO) algorithm and mutation-based search mechanism for developing a high-performance machine learning solution for predicting soil compression index. HHO is a newly introduced meta-heuristic optimization algorithm(MOA) used to solve continuous search problems.Compared to the original HHO, the proposed IHHO can evade trapping in local optima, which in turn raises the search capabilities and enhances the search mechanism relying on mutation. Subsequently, a novel meta-heuristic-based soft computing technique called ELM-IHHO was established by integrating IHHO and extreme learning machine(ELM) to estimate soil compression index. A sum of 688 consolidation test data was collected for this purpose from an ongoing dedicated freight corridor railway project. To evaluate the generalization capability of the proposed ELM-IHHO model, a detailed comparison between ELM-IHHO and other well-established MOAs, such as particle swarm optimization,genetic algorithm, and biogeography-based optimization integrated with ELM, was performed. Based on the outcomes, the ELM-IHHO model exhibits superior performance over the other MOAs in predicting soil compression index.

关键词： Compression index Artificial intelligence Swarm intelligence meta-heuristic optimization Dedicated freight corridor

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Al-Biruni Earth Radius optimization for COVID-19 Forecasting

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Computer Systems Science & Engineering 2023年第7期46卷 883-896页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Tatiana Makarovskikh Amal H.Alharbi Doaa Sami Khafaga Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of System Programming South Ural State University454080ChelyabinskRussia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Several instances of pneumonia with no clear etiology were recorded in Wuhan,China,on December 31,2019.The world health organization(WHO)called it COVID-19 that stands for“Coronavirus Disease 2019,”which is the second version of the previously known severe acute respiratory syndrome(SARS)Coronavirus and identified in short as(SARSCoV-2).There have been regular restrictions to avoid the infection spread in all countries,including Saudi Arabia.The prediction of new cases of infections is crucial for authorities to get ready for early handling of the virus spread.Methodology:Analysis and forecasting of epidemic patterns in new SARSCoV-2 positive patients are presented in this research using metaheuristic optimization and long short-term memory(LSTM).The optimization method employed for optimizing the parameters of LSTM is Al-Biruni Earth Radius(BER)algorithm.Results:To evaluate the effectiveness of the proposed methodology,a dataset is collected based on the recorded cases in Saudi Arabia between March 7^(th),2020 and July 13^(th),2022.In addition,six regression models were included in the conducted experiments to show the effectiveness and superiority of the proposed approach.The achieved results show that the proposed approach could reduce the mean square error(MSE),mean absolute error(MAE),and R^(2)by 5.92%,3.66%,and 39.44%,respectively,when compared with the six base models.On the other hand,a statistical analysis is performed to measure the significance of the proposed approach.Conclusions:The achieved results confirm the effectiveness,superiority,and significance of the proposed approach in predicting the infection cases of COVID-19.

关键词： COVID-19 prediction meta-heuristic optimization LSTM Al-Biruni earth radius algorithm

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Al-Biruni Based optimization of Rainfall Forecasting in Ethiopia

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Computer Systems Science & Engineering 2023年第6期45卷 2885-2899页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Fadwa Alrowais Mostafa Abotaleb Abdelhameed Ibrahim Doaa Sami Khafaga Department of Communications and Elecronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science F aculty of Computer and Infomation SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Progamming South Ual State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Rainfall plays a significant role in managing the water level in the reser-voir.The unpredictable amount of rainfall due to the climate change can cause either overflow or dry in the reservoir.Many individuals,especially those in the agricultural sector,rely on rain forecasts.Forecasting rainfall is challenging because of the changing nature of the weather.The area of Jimma in southwest Oromia,Ethiopia is the subject of this research,which aims to develop a rainfall forecasting model.To estimate Jimma's daily rainfall,we propose a novel approach based on optimizing the parameters of long short-term memory(LSTM)using Al-Biruni earth radius(BER)optimization algorithm for boosting the fore-casting accuracy.N ash-Sutcliffe model eficiency(NSE),mean square error(MSE),root MSE(RMSE),mean absolute error(MAE),and R2 were all used in the conducted experiments to assess the proposed approach,with final scores of(0.61),(430.81),(19.12),and(11.09),respectively.Moreover,we compared the proposed model to current machine-learning regression models;such as non-optimized LSTM,bidirectional LSTM(BiLSTM),gated recurrent unit(GRU),and convolutional LSTM(ConvLSTM).It was found that the proposed approach achieved the lowest RMSE of(19.12).In addition,the experimental results show that the proposed model has R-with a value outperforming the other models,which confirms the superiority of the proposed approach.On the other hand,a statistical analysis is performed to measure the significance and stability of the proposed approach and the recorded results proved the expected perfomance.

关键词： Rainfall prediction long short-term memory Al-Biruni earth radius algorithm meta-heuristic optimization

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Reliability Analysis of Piled Raft Foundation Using a Novel Hybrid Approach of ANN and Equilibrium Optimizer

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Computer Modeling in Engineering & Sciences 2021年第9期128卷 1033-1067页

作者： Abidhan Bardhan Priyadip Manna Vinay Kumar Avijit Burman Bojan Zlender Pijush Samui Department of Civil Engineering National Institute of TechnologyPatnaBiharIndia Faculty of Civil Engineering Transportation Engineering and ArchitectureUniversity of MariborMariborSlovenia

In many civil engineering projects,Piled Raft Foundations(PRFs)are usually preferred where the incoming load fromthe superstructures is very high.In geotechnical engineering practice,the settlement of soil layers is a critical issue for the serviceability of the structures.Thus,assessment of risk associated with the structures corresponding to the maximum allowable settlement of soils needs to be carried out in the design phase.In this study,reliability analysis of PRF based on settlement criteria is performed using a high-performance hybrid soft computing model.The new approach is an integration of the artificial neural network(ANN)and a recently developed meta-heuristic algorithm called equilibrium optimizer(EO).The concept of reliability index was used to explore the feasibility of a newly constructed hybrid model of ANN and EO(i.e.,ANN-EO)against the conventional approach of calculating the probability of failure of PRF.Experimental results show that the proposed ANN-EO attained the most accurate prediction with R^(2)=0.9914 and RMSE=0.0518 in the testing phase,which are significantly better than those obtained from conventional ANN,multivariate adaptive regression splines,and genetic programming,including the ANNoptimized with particle swarmoptimization developed in this study.Based on the experimental results of different settlement values,the newly constructedANN-EOis very potential to analyze the risk associatedwith civil engineering structures.Also,the present study would significantly contribute to the knowledge pool of reliability studies related to piled raft systems because the works of literature on reliability analysis of piled raft systems are relatively scarce.

关键词： Risk analysis soil meta-heuristic optimization particle swarm optimization

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