Artificial Immune Network Approach with Beta Differential Operator Applied to Optimization of Heat Exchangers

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• 作者：Viviana Cocco Mariani (12) viviana.mariani@pucpr.br
  Leandro dos Santos Coelho (23)
  Anderson Duck (1) andersonduck@gmail.com
  Fabio Alessandro Guerra (4) guerra@lactec.org.br
  Ravipudi Venkata Rao (5) ravipudirao@gmail.com
• 关键词：artificial immune system &#8211 ; optimization &#8211 ; opt ; aiNET &#8211 ; differential evolution &#8211 ; beta probability distribution &#8211 ; heat exchangers design
• 刊名：Lecture Notes in Computer Science
• 出版年：2012
• 出版时间：2012
• 年：2012
• 卷：7597
• 期：1
• 页码：166-177
• 全文大小：253.6 KB
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• 作者单位：1. PPGEM, Pontifical Catholic University of Parana (PUCPR), Imaculada Concei莽茫o, 1155, Zip code 80215-901 Curitiba, PR, Brazil2. Department of Electrical Engineering, Federal University of Parana (UFPR), Zip code 81531-980 Curitiba, PR, Brazil3. PPGEPS, Pontifical Catholic University of Parana (PUCPR), Imaculada Concei莽茫o, 1155, Zip code 80215-901 Curitiba, PR, Brazil4. Electricity Department, DPEL/DVSE/LACTEC - Institute of Technology for Development, Zip code 81531-980 Curitiba, PR, Brazil5. Sardar Vallabhbhai National Institute of Technology, Surat, 395 007 Gujarat, India
• ISSN：1611-3349

文摘

The artificial immune systems combine these strengths have been gaining significant attention due to its powerful adaptive learning and memory capabilities. A meta-heuristic approach called opt-aiNET (artificial immune network for optimization) algorithm, a well-known immune inspired algorithm for function optimization, is adopted in this paper. The opt-aiNET algorithm evolves a population, which consists of a network of antibodies (considered as candidate solutions to the function being optimized). These undergo a process of evaluation against the objective function, clonal expansion, mutation, selection and interaction between themselves. In this paper, a proposed modified opt-aiNET approach using based on mutation operator inspired in differential evolution and beta probability distribution (opt-BDaiNET) is described and validated to three benchmark functions and to shell and tube heat exchanger optimization based on the minimization from economic view point. Simulations are conducted to verify the efficiency of proposed opt-BDaiNET algorithm and the results obtained for two case studies are compared with those obtained by using genetic algorithm and particle swarm optimization. In this application domain, the opt-aiNET and opt-BDaiNET were found to outperform the previously best-known solutions available in the recent literature.