This study attempts to accelerate the learning ability of an artificial electric field algorithm(AEFA)by attributing it with two mechanisms:elitism and opposition-based learning.Elitism advances the convergence of the...This study attempts to accelerate the learning ability of an artificial electric field algorithm(AEFA)by attributing it with two mechanisms:elitism and opposition-based learning.Elitism advances the convergence of the AEFA towards global optima by retaining the fine-tuned solutions obtained thus far,and opposition-based learning helps enhance its exploration ability.The new version of the AEFA,called elitist opposition leaning-based AEFA(EOAEFA),retains the properties of the basic AEFA while taking advantage of both elitism and opposition-based learning.Hence,the improved version attempts to reach optimum solutions by enabling the diversification of solutions with guaranteed convergence.Higher-order neural networks(HONNs)have single-layer adjustable parameters,fast learning,a robust fault tolerance,and good approximation ability compared with multilayer neural networks.They consider a higher order of input signals,increased the dimensionality of inputs through functional expansion and could thus discriminate between them.However,determining the number of expansion units in HONNs along with their associated parameters(i.e.,weight and threshold)is a bottleneck in the design of such networks.Here,we used EOAEFA to design two HONNs,namely,a pi-sigma neural network and a functional link artificial neural network,called EOAEFA-PSNN and EOAEFA-FLN,respectively,in a fully automated manner.The proposed models were evaluated on financial time-series datasets,focusing on predicting four closing prices,four exchange rates,and three energy prices.Experiments,comparative studies,and statistical tests were conducted to establish the efficacy of the proposed approach.展开更多
基金supported by the Yonsei Fellow Program funded by Lee Youn Jae,Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korean government,Ministry of Science and ICT(MSIT)(No.2020-0-01361,Artificial Intelligence Graduate School Program(Yonsei University)No.2022-0-00113,Developing a Sustainable Collaborative Multi-modal Lifelong Learning Framework)the support of Teachers Associateship for Research Excellence(TARE)Fellowship(No.TAR/2021/00006)of the Science and Engineering Research Board(SERB),Government of India.
文摘This study attempts to accelerate the learning ability of an artificial electric field algorithm(AEFA)by attributing it with two mechanisms:elitism and opposition-based learning.Elitism advances the convergence of the AEFA towards global optima by retaining the fine-tuned solutions obtained thus far,and opposition-based learning helps enhance its exploration ability.The new version of the AEFA,called elitist opposition leaning-based AEFA(EOAEFA),retains the properties of the basic AEFA while taking advantage of both elitism and opposition-based learning.Hence,the improved version attempts to reach optimum solutions by enabling the diversification of solutions with guaranteed convergence.Higher-order neural networks(HONNs)have single-layer adjustable parameters,fast learning,a robust fault tolerance,and good approximation ability compared with multilayer neural networks.They consider a higher order of input signals,increased the dimensionality of inputs through functional expansion and could thus discriminate between them.However,determining the number of expansion units in HONNs along with their associated parameters(i.e.,weight and threshold)is a bottleneck in the design of such networks.Here,we used EOAEFA to design two HONNs,namely,a pi-sigma neural network and a functional link artificial neural network,called EOAEFA-PSNN and EOAEFA-FLN,respectively,in a fully automated manner.The proposed models were evaluated on financial time-series datasets,focusing on predicting four closing prices,four exchange rates,and three energy prices.Experiments,comparative studies,and statistical tests were conducted to establish the efficacy of the proposed approach.
