Learning FCMs with multi-local and balanced memetic algorithms for forecasting industrial drying processes

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• 作者：Jose L. Salmeron^a ; ^b ; ^c ; ^{salmeron@acm.org}Author Vitae ; Antonio Ruiz-Celma^d ; ^{antoniorcelma@gmail.com}Author Vitae ; Angel Mena^eAuthor Vitae
• 关键词：Fuzzy ; Cognitive MapsMachine learning ; Industrial drying ; Memetic algorithm
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：5 April 2017
• 年：2017
• 卷：232
• 期：Complete
• 页码：52-57
• 全文大小：593 K
• 卷排序：232

文摘

In this paper, we propose a Fuzzy Cognitive Map (FCM) learning approach with a multi-local search in balanced memetic algorithms for forecasting industrial drying processes. The first contribution of this paper is to propose a FCM model by an Evolutionary Algorithm (EA), but the resulted FCM model is improved by a multi-local and balanced local search algorithm. Memetic algorithms can be tuned with different local search strategies (CMA-ES, SW, SSW and Simplex) and the balance of the effort between global and local search. To do this, we applied the proposed approach to the forecasting of moisture loss in industrial drying process. The thermal drying process is a relevant one used in many industrial processes such as food industry, biofuels production, detergents and dyes in powder production, pharmaceutical industry, reprography applications, textile industries, and others. This research also shows that exploration of the search space is more relevant than finding local optima in the FCM models tested.