Green consumption behavior prediction based on fan-shaped search mechanism fruit fly algorithm optimized neural network |
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| Institution: | 1. School of Economics and Management, Beijing Information Science and Technology University, Beijing, 100192, China;2. School of Economics and Management, Beijing University of Technology, Beijing, 100124, China;3. Beijing Key Laboratory of Big Data Decision Making for Green Development, Beijing, 100192, PR China;4. Beijing Union University, Beijing, 100101, China;5. Beijing International Science and Technology Cooperation Base for Intelligent Decision and Big Data Application, Beijing, 100192, PR China;1. Ozyegin University, Nisantepe, Orman Sk. No:13, 34794, Cekmeköy, İstanbul, Turkey;2. Cardiff University, Aberconwy Building, Colum Drive, Cardiff, Wales, CF10 3EU, United Kingdom;3. Sabanci University, Orhanli-Tuzla, Istanbul, Turkey;1. School of Business Administration, Northeastern University, Shenyang, Liaoning, 110819, China;2. Northeastern University at Qinhuangdao, Qinhuangdao, 066004, China;3. College of Mathematics and Informatics, Fujian Normal University, Fuzhou, Fujian, 350117, China;4. Digital Fujian Internet-of-Things Laboratory of Environmental Monitoring, Fujian Normal University, Fuzhou, Fujian, 350117, China;1. Department of Business Administration, Myongji University, Seoul, South Korea;2. College of Business Administration, Inha University, Incheon, South Korea;3. College of Business Administration, Inha University, Incheon, South Korea;1. School of Management, University of Science and Technology of China, Jinzhai Road, 230026, Hefei, China;2. School of Economics and Management, Anhui Polytechnic University, Beijing Road, 241000, Wuhu, China;1. School of Business, Fuyang Normal University, Fuyang, 236037, China;2. Anhui Provincial Key Laboratory of Regional Logistics Planning and Modern Logistics Engineering, Fuyang, 236037, China |
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| Abstract: | Predicting consumption behavior is very important for adjusting supplier production plans and enterprise marketing activities. Conventional statistical methods are unable to accurately predict green consumption behavior because it is characterized by multivariate nonlinear interactions. The paper proposes an optimized fruit fly algorithm (FOA) and extreme learning machine (ELM) model for consumption behavior prediction. First, to address the problem of uneven search direction of FOA leading to insufficient search ability and low efficiency, the paper proposes a sector search mechanism instead of a random search mechanism to improve the global search ability and convergence speed of FOA. Second, to address the issue that the initial weights and hidden layer bias values of the ELM are randomly generated, which affects the learning efficiency and generalization of the ELM, the paper uses an improved FOA to optimize the weights and bias values of ELM for improving the prediction accuracy. Taking the green vegetable consumption behavior of Beijing residents as an example, the results show the optimization of the initial weight and threshold of ELM by the GA, PSO, FOA, and SFOA, the prediction accuracy of the GA-ELM, PSO-ELM, FOA-ELM, and SFOA-ELM models all surpass those of ELM. Compared with BPNN, GRNN, ELM, GA-ELM, PSO-ELM, and FOA-ELM models, the RMSE value of SFOA-ELM was decreased by 9.45%, 8.40%, 11.89%, 5.84%, 2.22%, and 2.69%, respectively. These findings demonstrate the effectiveness of the SFOA-ELM model in green consumption behavior prediction and provide new ideas for the accurate prediction of consumption behaviors of other green products with similar characteristics. |
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| Keywords: | Improved fruit fly algorithm Extreme learning machine Green consumption behavior Prediction |
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