The sparrow search algorithm(SSA)is a newly proposed meta-heuristic optimization algorithm based on the sparrowforaging principle.Similar to other meta-heuristic algorithms,SSA has problems such as slowconvergence spe...The sparrow search algorithm(SSA)is a newly proposed meta-heuristic optimization algorithm based on the sparrowforaging principle.Similar to other meta-heuristic algorithms,SSA has problems such as slowconvergence speed and difficulty in jumping out of the local optimum.In order to overcome these shortcomings,a chaotic sparrow search algorithm based on logarithmic spiral strategy and adaptive step strategy(CLSSA)is proposed in this paper.Firstly,in order to balance the exploration and exploitation ability of the algorithm,chaotic mapping is introduced to adjust the main parameters of SSA.Secondly,in order to improve the diversity of the population and enhance the search of the surrounding space,the logarithmic spiral strategy is introduced to improve the sparrow search mechanism.Finally,the adaptive step strategy is introduced to better control the process of algorithm exploitation and exploration.The best chaotic map is determined by different test functions,and the CLSSA with the best chaotic map is applied to solve 23 benchmark functions and 3 classical engineering problems.The simulation results show that the iterative map is the best chaotic map,and CLSSA is efficient and useful for engineering problems,which is better than all comparison algorithms.展开更多
Due to the NP-hardness of the two-sided assembly line balancing （TALB） problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In ...Due to the NP-hardness of the two-sided assembly line balancing （TALB） problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In this paper, an effective hybrid algorithm is proposed to address the TALB problem with multiple constraints （TALB-MC）. Considering the discrete attribute of TALB-MC and the continuous attribute of the standard teaching-learning-based optimization （TLBO） algorithm, the random-keys method is hired in task permutation representation, for the purpose of bridging the gap between them. Subsequently, a special mechanism for handling multiple constraints is developed. In the mechanism, the directions constraint of each task is ensured by the direction check and adjustment. The zoning constraints and the synchronism constraints are satisfied by teasing out the hidden correlations among constraints. The positional constraint is allowed to be violated to some extent in decoding and punished in cost fimction. Finally, with the TLBO seeking for the global optimum, the variable neighborhood search （VNS） is further hybridized to extend the local search space. The experimental results show that the proposed hybrid algorithm outperforms the late acceptance hill-climbing algorithm （LAHC） for TALB-MC in most cases, especially for large-size problems with multiple constraints, and demonstrates well balance between the exploration and the exploitation. This research proposes an effective and efficient algorithm for solving TALB-MC problem by hybridizing the TLBO and VNS.展开更多
A novel coupled model integrating Elman-AdaBoost with adaptive mutation sparrow search algorithm(AM-SSA),called AMSSAElman-AdaBoost,is proposed for predicting the existing metro tunnel deformation induced by adjacent ...A novel coupled model integrating Elman-AdaBoost with adaptive mutation sparrow search algorithm(AM-SSA),called AMSSAElman-AdaBoost,is proposed for predicting the existing metro tunnel deformation induced by adjacent deep excavations in soft ground.The novelty is that the modified SSA proposes adaptive adjustment strategy to create a balance between the capacity of exploitation and exploration.In AM-SSA,firstly,the population is initialized by cat mapping chaotic sequences to improve the ergodicity and randomness of the individual sparrow,enhancing the global search ability.Then the individuals are adjusted by Tent chaotic disturbance and Cauchy mutation to avoid the population being too concentrated or scattered,expanding the local search ability.Finally,the adaptive producer-scrounger number adjustment formula is introduced to balance the ability to seek the global and local optimal.In addition,it leads to the improved algorithm achieving a better accuracy level and convergence speed compared with the original SSA.To demonstrate the effectiveness and reliability of AM-SSA,23 classical benchmark functions and 25 IEEE Congress on Evolutionary Computation benchmark test functions(CEC2005),are employed as the numerical examples and investigated in comparison with some wellknown optimization algorithms.The statistical results indicate the promising performance of AM-SSA in a variety of optimization with constrained and unknown search spaces.By utilizing the AdaBoost algorithm,multiple sets of weak AMSSA-Elman predictor functions are restructured into one strong predictor by successive iterations for the tunnel deformation prediction output.Additionally,the on-site monitoring data acquired from a deep excavation project in Ningbo,China,were selected as the training and testing sample.Meanwhile,the predictive outcomes are compared with those of other different optimization and machine learning techniques.In the end,the obtained results in this real-world geotechnical engineering field reveal the feasibility of the proposed hybrid algorithm model,illustrating its power and superiority in terms of computational efficiency,accuracy,stability,and robustness.More critically,by observing data in real time on daily basis,the structural safety associated with metro tunnels could be supervised,which enables decision-makers to take concrete control and protection measures.展开更多
Social distancing during COVID-19 has become one of the most important measures in reducing the risks of the spread of the virus. Implementing thesemeasures at universities is crucial and directly related to the physi...Social distancing during COVID-19 has become one of the most important measures in reducing the risks of the spread of the virus. Implementing thesemeasures at universities is crucial and directly related to the physical attendance ofthe populations of students, professors, employees, and other members on campus. This research proposes an automated scheduling approach that can help universities and schools comply with the social distancing regulations by providingassistance in avoiding huge assemblages of people. Furthermore, this paper proposes a novel course timetable-scheduling scheme based on four main constraints.First, a distance of two meters must be maintained between each student inside theclassroom. Second, no classrooms should contain more than 20% of their regularcapacity. Third, there would be no back-to-back classes. Lastly, no lectures shouldbe held simultaneously in adjacent classrooms. The proposed approach wasimplemented using a variable neighborhood search (VNS) approach with an adaptive neighborhood structure (AD-NS) to resolve the problem of scheduling coursetimetables at Al-Ahlyyia Amman University. However, the experimental resultsshow that the proposed techniques outperformed the standard VNS tested on university course timetabling benchmark dataset ITC2007-Track3. Meanwhile, theapproach was tested using datasets collected from the faculty of information technology at Al-Ahlyyia Amman University (Jordan). Where the results showed that,the proposed technique could help educational institutes to resume their regularoperations while complying with the social distancing guidelines.展开更多
基金The Science Foundation of Shanxi Province,China(2020JQ-481,2021JM-224)Aero Science Foundation of China(201951096002).
文摘The sparrow search algorithm(SSA)is a newly proposed meta-heuristic optimization algorithm based on the sparrowforaging principle.Similar to other meta-heuristic algorithms,SSA has problems such as slowconvergence speed and difficulty in jumping out of the local optimum.In order to overcome these shortcomings,a chaotic sparrow search algorithm based on logarithmic spiral strategy and adaptive step strategy(CLSSA)is proposed in this paper.Firstly,in order to balance the exploration and exploitation ability of the algorithm,chaotic mapping is introduced to adjust the main parameters of SSA.Secondly,in order to improve the diversity of the population and enhance the search of the surrounding space,the logarithmic spiral strategy is introduced to improve the sparrow search mechanism.Finally,the adaptive step strategy is introduced to better control the process of algorithm exploitation and exploration.The best chaotic map is determined by different test functions,and the CLSSA with the best chaotic map is applied to solve 23 benchmark functions and 3 classical engineering problems.The simulation results show that the iterative map is the best chaotic map,and CLSSA is efficient and useful for engineering problems,which is better than all comparison algorithms.
基金Supported by National Natural Science Foundation of China(Grant Nos.51275366,50875190,51305311)Specialized Research Fund for the Doctoral Program of Higher Education of China(Grant No.20134219110002)
文摘Due to the NP-hardness of the two-sided assembly line balancing （TALB） problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In this paper, an effective hybrid algorithm is proposed to address the TALB problem with multiple constraints （TALB-MC）. Considering the discrete attribute of TALB-MC and the continuous attribute of the standard teaching-learning-based optimization （TLBO） algorithm, the random-keys method is hired in task permutation representation, for the purpose of bridging the gap between them. Subsequently, a special mechanism for handling multiple constraints is developed. In the mechanism, the directions constraint of each task is ensured by the direction check and adjustment. The zoning constraints and the synchronism constraints are satisfied by teasing out the hidden correlations among constraints. The positional constraint is allowed to be violated to some extent in decoding and punished in cost fimction. Finally, with the TLBO seeking for the global optimum, the variable neighborhood search （VNS） is further hybridized to extend the local search space. The experimental results show that the proposed hybrid algorithm outperforms the late acceptance hill-climbing algorithm （LAHC） for TALB-MC in most cases, especially for large-size problems with multiple constraints, and demonstrates well balance between the exploration and the exploitation. This research proposes an effective and efficient algorithm for solving TALB-MC problem by hybridizing the TLBO and VNS.
基金supported by the National Natural Science Foundation of China(Grant No.52125803).
文摘A novel coupled model integrating Elman-AdaBoost with adaptive mutation sparrow search algorithm(AM-SSA),called AMSSAElman-AdaBoost,is proposed for predicting the existing metro tunnel deformation induced by adjacent deep excavations in soft ground.The novelty is that the modified SSA proposes adaptive adjustment strategy to create a balance between the capacity of exploitation and exploration.In AM-SSA,firstly,the population is initialized by cat mapping chaotic sequences to improve the ergodicity and randomness of the individual sparrow,enhancing the global search ability.Then the individuals are adjusted by Tent chaotic disturbance and Cauchy mutation to avoid the population being too concentrated or scattered,expanding the local search ability.Finally,the adaptive producer-scrounger number adjustment formula is introduced to balance the ability to seek the global and local optimal.In addition,it leads to the improved algorithm achieving a better accuracy level and convergence speed compared with the original SSA.To demonstrate the effectiveness and reliability of AM-SSA,23 classical benchmark functions and 25 IEEE Congress on Evolutionary Computation benchmark test functions(CEC2005),are employed as the numerical examples and investigated in comparison with some wellknown optimization algorithms.The statistical results indicate the promising performance of AM-SSA in a variety of optimization with constrained and unknown search spaces.By utilizing the AdaBoost algorithm,multiple sets of weak AMSSA-Elman predictor functions are restructured into one strong predictor by successive iterations for the tunnel deformation prediction output.Additionally,the on-site monitoring data acquired from a deep excavation project in Ningbo,China,were selected as the training and testing sample.Meanwhile,the predictive outcomes are compared with those of other different optimization and machine learning techniques.In the end,the obtained results in this real-world geotechnical engineering field reveal the feasibility of the proposed hybrid algorithm model,illustrating its power and superiority in terms of computational efficiency,accuracy,stability,and robustness.More critically,by observing data in real time on daily basis,the structural safety associated with metro tunnels could be supervised,which enables decision-makers to take concrete control and protection measures.
文摘Social distancing during COVID-19 has become one of the most important measures in reducing the risks of the spread of the virus. Implementing thesemeasures at universities is crucial and directly related to the physical attendance ofthe populations of students, professors, employees, and other members on campus. This research proposes an automated scheduling approach that can help universities and schools comply with the social distancing regulations by providingassistance in avoiding huge assemblages of people. Furthermore, this paper proposes a novel course timetable-scheduling scheme based on four main constraints.First, a distance of two meters must be maintained between each student inside theclassroom. Second, no classrooms should contain more than 20% of their regularcapacity. Third, there would be no back-to-back classes. Lastly, no lectures shouldbe held simultaneously in adjacent classrooms. The proposed approach wasimplemented using a variable neighborhood search (VNS) approach with an adaptive neighborhood structure (AD-NS) to resolve the problem of scheduling coursetimetables at Al-Ahlyyia Amman University. However, the experimental resultsshow that the proposed techniques outperformed the standard VNS tested on university course timetabling benchmark dataset ITC2007-Track3. Meanwhile, theapproach was tested using datasets collected from the faculty of information technology at Al-Ahlyyia Amman University (Jordan). Where the results showed that,the proposed technique could help educational institutes to resume their regularoperations while complying with the social distancing guidelines.
