A novel heuristic search algorithm called seeker op- timization algorithm （SOA） is proposed for the real-parameter optimization. The proposed SOA is based on simulating the act of human searching. In the SOA, search...A novel heuristic search algorithm called seeker op- timization algorithm （SOA） is proposed for the real-parameter optimization. The proposed SOA is based on simulating the act of human searching. In the SOA, search direction is based on empir- ical gradients by evaluating the response to the position changes, while step length is based on uncertainty reasoning by using a simple fuzzy rule. The effectiveness of the SOA is evaluated by using a challenging set of typically complex functions in compari- son to differential evolution （DE） and three modified particle swarm optimization （PSO） algorithms. The simulation results show that the performance of the SOA is superior or comparable to that of the other algorithms.展开更多
Seeker optimization algorithm （SOA） has applications in continuous space of swarm intelligence. In the fields of proton exchange membrane fuel cell （PEMFC） modeling, SOA was proposed to research a set of optimized...Seeker optimization algorithm （SOA） has applications in continuous space of swarm intelligence. In the fields of proton exchange membrane fuel cell （PEMFC） modeling, SOA was proposed to research a set of optimized parameters in PEMFC polarization curve model. Experimental result showed that the mean square error of the optimization modeling strategy was only 6.9 × 10^-23. Hence, the optimization model could fit the experiment data with high precision.展开更多
A new hybrid optimization method based on genetic algorithm(GA)and seeker optimization algorithm(SOA)is presented in this paper.The hybrid algorithm optimizes SOA by using crossover and mutation operations in GA in or...A new hybrid optimization method based on genetic algorithm(GA)and seeker optimization algorithm(SOA)is presented in this paper.The hybrid algorithm optimizes SOA by using crossover and mutation operations in GA in order to improve the global search ability of SOA.Four algorithms,i.e.particle swarm optimization(PSO),SOA,GA and quantum-behaved particle swarm optimization(GA-QPSO)and GA-SOA are used to process the simulation and experimental data of Brillouin scattering spectrum(BSS)at different temperatures.The results show that GA-SOA improves the accuracy of extracting the center frequency shift and the minimum center frequency of Brillouin scattering spectrum compared with other three algorithms.The shift error is 0.203 MHz.Therefore,GA-SOA can be applied to the accurate extraction of BSS characteristics.展开更多
World Wide Web enables its users to connect among themselves through social networks,forums,review sites,and blogs and these interactions produce huge volumes of data in various forms such as emotions,sentiments,views...World Wide Web enables its users to connect among themselves through social networks,forums,review sites,and blogs and these interactions produce huge volumes of data in various forms such as emotions,sentiments,views,etc.Sentiment Analysis(SA)is a text organization approach that is applied to categorize the sentiments under distinct classes such as positive,negative,and neutral.However,Sentiment Analysis is challenging to perform due to inadequate volume of labeled data in the domain of Natural Language Processing(NLP).Social networks produce interconnected and huge data which brings complexity in terms of expanding SA to an extensive array of applications.So,there is a need exists to develop a proper technique for both identification and classification of sentiments in social media.To get rid of these problems,Deep Learning methods and sentiment analysis are consolidated since the former is highly efficient owing to its automatic learning capability.The current study introduces a Seeker Optimization Algorithm with Deep Learning enabled SA and Classification(SOADL-SAC)for social media.The presented SOADL-SAC model involves the proper identification and classification of sentiments in social media.In order to attain this,SOADL-SAC model carries out data preprocessing to clean the input data.In addition,Glove technique is applied to generate the feature vectors.Moreover,Self-Head Multi-Attention based Gated Recurrent Unit(SHMA-GRU)model is exploited to recognize and classify the sentiments.Finally,Seeker Optimization Algorithm(SOA)is applied to fine-tune the hyperparameters involved in SHMA-GRU model which in turn enhances the classifier results.In order to validate the enhanced outcomes of the proposed SOADL-SAC model,various experiments were conducted on benchmark datasets.The experimental results inferred the better performance of SOADLSAC model over recent state-of-the-art approaches.展开更多
基金supported by the National Natural Science Foundation of China(60870004)
文摘A novel heuristic search algorithm called seeker op- timization algorithm （SOA） is proposed for the real-parameter optimization. The proposed SOA is based on simulating the act of human searching. In the SOA, search direction is based on empir- ical gradients by evaluating the response to the position changes, while step length is based on uncertainty reasoning by using a simple fuzzy rule. The effectiveness of the SOA is evaluated by using a challenging set of typically complex functions in compari- son to differential evolution （DE） and three modified particle swarm optimization （PSO） algorithms. The simulation results show that the performance of the SOA is superior or comparable to that of the other algorithms.
文摘Seeker optimization algorithm （SOA） has applications in continuous space of swarm intelligence. In the fields of proton exchange membrane fuel cell （PEMFC） modeling, SOA was proposed to research a set of optimized parameters in PEMFC polarization curve model. Experimental result showed that the mean square error of the optimization modeling strategy was only 6.9 × 10^-23. Hence, the optimization model could fit the experiment data with high precision.
基金Supported by the National Natural Science Foundation of China(No.11673040,61675176,51675461)‘Xinrui Gongcheng’ Talent Project of Yanshan University of Chinathe China Scholarship Council(No.201708130010)
文摘A new hybrid optimization method based on genetic algorithm(GA)and seeker optimization algorithm(SOA)is presented in this paper.The hybrid algorithm optimizes SOA by using crossover and mutation operations in GA in order to improve the global search ability of SOA.Four algorithms,i.e.particle swarm optimization(PSO),SOA,GA and quantum-behaved particle swarm optimization(GA-QPSO)and GA-SOA are used to process the simulation and experimental data of Brillouin scattering spectrum(BSS)at different temperatures.The results show that GA-SOA improves the accuracy of extracting the center frequency shift and the minimum center frequency of Brillouin scattering spectrum compared with other three algorithms.The shift error is 0.203 MHz.Therefore,GA-SOA can be applied to the accurate extraction of BSS characteristics.
基金The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4350139DSR01).
文摘World Wide Web enables its users to connect among themselves through social networks,forums,review sites,and blogs and these interactions produce huge volumes of data in various forms such as emotions,sentiments,views,etc.Sentiment Analysis(SA)is a text organization approach that is applied to categorize the sentiments under distinct classes such as positive,negative,and neutral.However,Sentiment Analysis is challenging to perform due to inadequate volume of labeled data in the domain of Natural Language Processing(NLP).Social networks produce interconnected and huge data which brings complexity in terms of expanding SA to an extensive array of applications.So,there is a need exists to develop a proper technique for both identification and classification of sentiments in social media.To get rid of these problems,Deep Learning methods and sentiment analysis are consolidated since the former is highly efficient owing to its automatic learning capability.The current study introduces a Seeker Optimization Algorithm with Deep Learning enabled SA and Classification(SOADL-SAC)for social media.The presented SOADL-SAC model involves the proper identification and classification of sentiments in social media.In order to attain this,SOADL-SAC model carries out data preprocessing to clean the input data.In addition,Glove technique is applied to generate the feature vectors.Moreover,Self-Head Multi-Attention based Gated Recurrent Unit(SHMA-GRU)model is exploited to recognize and classify the sentiments.Finally,Seeker Optimization Algorithm(SOA)is applied to fine-tune the hyperparameters involved in SHMA-GRU model which in turn enhances the classifier results.In order to validate the enhanced outcomes of the proposed SOADL-SAC model,various experiments were conducted on benchmark datasets.The experimental results inferred the better performance of SOADLSAC model over recent state-of-the-art approaches.
