Fundamentals of Computer Alori
Fundamentals of Computer Alori
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
An Evolutionary Immune Network for Data Clustering
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
Application areas of AIS: The past, the present and the future
Applied Soft Computing
Study on Chaos Immune Network Algorithm for Multimodal Function Optimization
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03
A sequential niche technique for multimodal function optimization
Evolutionary Computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
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For the problem of serious resources waste, indeterminate direction of local search and degeneration in the original opt-aiNet, a novel association based immune network is proposed for multimodal function optimization. The hexabasic model mimics natural phenomenon in immune system such as clonal selection, affinity maturation, immune network, immune memory and immune association. The antibody population scale is semi-fixed reducing the time and space required to execute it. The information of the antibody population and the memory cells population is effective utilized to point out the direction of local search, to regulate the ratio between local search and global search, and to enhance the affinity of new antibodies. The elitist selection mechanism is adopted to ensure the convergence and stability of our algorithm respectively. The experiments on 10 benchmark functions show that when compared with opt-aiNet method, the new algorithm is capable of improving the search performance significantly in global convergence, convergence speed, computational cost, search ability, solution quality and algorithm stability.