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

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.