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首页> 外文期刊>Knowledge-Based Systems >A Cooperative Ant Colony Optimization-Genetic Algorithm approach for construction of energy demand forecasting knowledge-based expert systems
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A Cooperative Ant Colony Optimization-Genetic Algorithm approach for construction of energy demand forecasting knowledge-based expert systems

机译：基于协同蚁群优化遗传算法的能源需求预测知识专家系统的构建

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摘要

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.
机译：如今，基于知识的专家系统具有许多吸引人的功能，并且正被人们用来解决用正统方法不易解决的实际/复杂的工程应用问题，它们正成为当今科学家和工程师的主要工具之一。同时，全球范围内对不同类型能源的需求不断增长，这就要求开发先进的智能预测工具，以提供制定决策和计划的基础。这项研究提出了一种称为“合作蚁群优化遗传算法”（COR-ACO-GA）的新方法，以构建能够建模和模拟在相关因素影响下的能源需求波动的专家系统。所提出的方法有两个主要阶段，第一阶段使用遗传算法生成专家系统的数据库，第二阶段采用蚁群优化来学习语言模糊规则，从而使数据库和规则之间的协作程度更高基础增加，因此算法的性能提高。我们通过将COR-ACO-GA的能力应用于伊朗的年度电力需求，天然气需求和石油产品需求的三个案例研究，来评估其能力。结果表明，与自适应神经模糊推理系统（ANFIS）和人工神经网络（ANN）相比，COR-ACO-GA可以提供更准确，稳定的结果，并且可以帮助决策者在未来一段时间内做出适当的决策和计划。

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