{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T10:00:23Z","timestamp":1760608823087,"version":"3.41.2"},"reference-count":14,"publisher":"Emerald","issue":"7","license":[{"start":{"date-parts":[[2014,7,29]],"date-time":"2014-07-29T00:00:00Z","timestamp":1406592000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,7,29]]},"abstract":"\n Purpose<\/jats:title>\n \u2013 When facing a clouded global economy, many countries would increase their gold reserves. On the other hand, oil supply and demand depends on the political and economic situations of oil producing countries and their production technologies. Both oil and gold reserve play important roles in the economic development of a country. The paper aims to discuss this issue. <\/jats:p>\n <\/jats:sec>\n \n Design\/methodology\/approach<\/jats:title>\n \u2013 This paper uses the historical data of oil and gold prices as research data, and uses the historical price tendency charts of oil and gold, as well as cluster analysis, to discuss the correlation between the historical data of oil and gold prices. By referring to the technical index equation of stocks, the technical indices of oil and gold prices are calculated as the independent variable and the closing price as the dependent variable of the forecasting model. <\/jats:p>\n <\/jats:sec>\n \n Findings<\/jats:title>\n \u2013 The findings indicate that there is no obvious correlation between the price tendencies of oil and gold. According to five evaluating indicators, the MFOAGRNN forecast model has better forecast ability than the other three forecasting models. <\/jats:p>\n <\/jats:sec>\n \n Originality\/value<\/jats:title>\n \u2013 This paper explored the correlation between oil and gold prices, and built oil and gold prices forecasting models. In addition, this paper proposes a modified FOA (MFOA), where an escape parameter \u0394 is added to Si. The findings showed that the forecasting model that combines MFOA and GRNN has the best ability to forecast the closing price of oil and gold.<\/jats:p>\n <\/jats:sec>","DOI":"10.1108\/k-02-2014-0024","type":"journal-article","created":{"date-parts":[[2014,7,29]],"date-time":"2014-07-29T13:37:32Z","timestamp":1406641052000},"page":"1053-1063","source":"Crossref","is-referenced-by-count":21,"title":["Mixed modified fruit fly optimization algorithm with general regression neural network to build oil and gold prices forecasting model"],"prefix":"10.1108","volume":"43","author":[{"given":"Wen-Tsao","family":"Pan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"key":"key2020123023294760100_b1","doi-asserted-by":"crossref","unstructured":"Abdullah, S.N.\n and \n Zeng, X.\n (2010), \u201cMachine learning approach for crude oil price prediction with artificial neural networks-quantitative (ANN-Q) model\u201d, The 2010 International Joint Conference on Neural Networks, pp. 1-8.","DOI":"10.1109\/IJCNN.2010.5596602"},{"key":"key2020123023294760100_b2","unstructured":"Abidin, Z.Z.\n , \n Hamzah, M.S.M.\n , \n Arshad, M.R.\n and \n Ngah, U.K.\n (2012), \u201cA calibration framework for swarming ASVs\u2019 system design\u201d, Indian Journal of Marine Sciences, Vol. 41 No. 6, pp. 581-588."},{"key":"key2020123023294760100_b3","doi-asserted-by":"crossref","unstructured":"Baker, S.A.\n and \n Van-Tassel, R.C.\n (1985), \u201cForecasting the price of gold: a fundamentalist approach\u201d, Atlantic Economic Journal, Vol. 13 No. 4, pp. 43-52.","DOI":"10.1007\/BF02304036"},{"key":"key2020123023294760100_b4","doi-asserted-by":"crossref","unstructured":"Bezdek, J.C.\n (1981), Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, NY.","DOI":"10.1007\/978-1-4757-0450-1"},{"key":"key2020123023294760100_b5","doi-asserted-by":"crossref","unstructured":"Chavarnakul, T.\n and \n Enke, D.\n (2008), \u201cIntelligent technical analysis based equivolume charting for stock trading using neural networks\u201d, Expert Systems with Applications, Vol. 34 No. 2, pp. 1004-1017.","DOI":"10.1016\/j.eswa.2006.10.028"},{"key":"key2020123023294760100_b6","unstructured":"Cheng, H.\n and \n Liu, C.Z.\n (2013), \u201cMixed fruit fly optimization algorithm based on chaotic mapping\u201d, Computer Engineering, Vol. 39 No. 5, pp. 218-221."},{"key":"key2020123023294760100_b7","unstructured":"Khan, A.U.\n , \n Bandopadhyaya, T.K.\n and \n Sharma, S.S.\n (2010), \u201cSOM and technical indicators based hybrid model gives better returns on investments as compared to BSE30 index\u201d, Workshop on Knowledge Discovery and Data Mining, Phuket, pp. 544-547."},{"key":"key2020123023294760100_b8","unstructured":"Kov'acs, A.\n and \n Abonyi, J.\n (2002), \u201cVisualization of fuzzy clustering results by modified sammon mapping\u201d, Proceedings of the 3rd International Symposium of Hungarian Researchers on Computational Intelligence, pp.177-188."},{"key":"key2020123023294760100_b9","doi-asserted-by":"crossref","unstructured":"Li, C.\n , \n Xu, S.\n , \n Li, W.\n and \n Hu, L.\n (2012a), \u201cA novel modified fly optimization algorithm for designing the self-tuning proportional integral derivative controller\u201d, Journal of Convergence Information Technology, Vol. 7 No. 16, pp. 69-77.","DOI":"10.4156\/jcit.vol7.issue16.9"},{"key":"key2020123023294760100_b10","doi-asserted-by":"crossref","unstructured":"Li, H.\n , \n Guo, S.\n , \n Zhao, H.\n , \n Su, C.\n and \n Wang, B.\n (2012b), \u201cAnnual electric load forecasting by a least squares support vector machine with a fruit fly optimization algorithm\u201d, Energies, Vol. 5 No. 11, pp. 4430-4445.","DOI":"10.3390\/en5114430"},{"key":"key2020123023294760100_b11","unstructured":"Li, H.L.\n , \n Shao, J.J.\n and \n Chien, J.H.\n (2002), \u201cOptimization pattern based on animal behaviour: fish group algorithm\u201d, System Engineering Theory and Practice, Vol. 22 No. 11, pp. 32-38."},{"key":"key2020123023294760100_b12","doi-asserted-by":"crossref","unstructured":"Pan, W.T.\n (2012), \u201cA new fruit fly optimization algorithm: taking the financial distress model as an example\u201d, Knowledge-Based Systems, Vol. 26 No. 2, pp. 69-74.","DOI":"10.1016\/j.knosys.2011.07.001"},{"key":"key2020123023294760100_b13","doi-asserted-by":"crossref","unstructured":"Teodorovic, D.\n , \n Lucic, P.\n , \n Markovic, G.\n and \n Dell' Orco, M.\n (2006), \u201cBee colony optimization: principles and applications\u201d, Proceedings of the Eight Seminar on Neural Network Applications in Electrical Engineering, pp.151-156.","DOI":"10.1109\/NEUREL.2006.341200"},{"key":"key2020123023294760100_b14","doi-asserted-by":"crossref","unstructured":"Yang, S.-C.\n , \n Lee, C.-S.\n and \n Lee, H.-S.\n (2012), \u201cEvaluation of logistic flow service satisfaction using the evolutionary computation technique and general regression 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