Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Journal of Global Optimization
An Evolutionary Immune Network for Data Clustering
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
A Note on the Extended Rosenbrock Function
Evolutionary Computation
Overview of artificial immune systems for multi-objective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Predication based immune network for multimodal function optimization
Engineering Applications of Artificial Intelligence
A Survey of artificial immune applications
Artificial Intelligence Review
Omni-aiNet: an immune-inspired approach for omni optimization
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
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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.