دانلود مقاله ISI انگلیسی شماره 52592
ترجمه فارسی عنوان مقاله

یک روش بهینه سازی کلونی-الگوریتم ژنتیک مشترک مورچه برای ساخت و ساز پیش بینی تقاضای انرژی سیستم های خبره مبتنی بر دانش

عنوان انگلیسی
A Cooperative Ant Colony Optimization-Genetic Algorithm approach for construction of energy demand forecasting knowledge-based expert systems
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
52592 2013 13 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Knowledge-Based Systems, Volume 39, February 2013, Pages 194–206

ترجمه کلمات کلیدی
سیستم های خبره مبتنی بر دانش - بهینه سازی کلونی مورچه - الگوریتم های ژنتیکی - منطق فازی - پیش بینی تقاضای انرژی
کلمات کلیدی انگلیسی
Knowledge-based expert systems; Ant colony optimization; Genetic algorithms; Fuzzy logic; Energy demand forecasting
پیش نمایش مقاله
پیش نمایش مقاله  یک روش بهینه سازی کلونی-الگوریتم ژنتیک مشترک مورچه برای ساخت و ساز پیش بینی تقاضای انرژی سیستم های خبره مبتنی بر دانش

چکیده انگلیسی

Knowledge-based expert systems are becoming one of the major tools for scientists and engineers nowadays, since they have many attractive features and can be called upon to deal with real/complex engineering application problems which are not easy to solve by orthodox methods. Meanwhile, increasing worldwide demand for different types of energy requires development of advanced intelligent forecasting tools to provide a basis from which decisions and plans can be made. This study presents a new approach called “Cooperative Ant Colony Optimization-Genetic Algorithm” (COR-ACO-GA), to construct expert systems with the ability to model and simulate fluctuations of energy demand under the influence of related factors. The proposed approach has two main stages, at the first stage it uses genetic algorithms to generate data base of the expert system, and at the second stage it adopts ant colony optimization to learn linguistic fuzzy rules such that degree of cooperation between data base and rule base increases and consequently performance of the algorithm improves. We evaluate capability of COR-ACO-GA by applying it on three case studies of annual electricity demand, natural gas demand and oil products demand in Iran. Results indicate that COR-ACO-GA provides more accurate-stable results than adaptive neuro-fuzzy inference systems (ANFISs) and artificial neural networks (ANNs), and can assist decision makers in making appropriate decisions and plans for a coming period.