title: A danger-theory-based immune network optimization algorithm.
abstract: Existing artificial immune optimization algorithms reflect a number of shortcomings, such as premature convergence and poor local search ability. This paper proposes a danger-theory-based immune network optimization algorithm, named dt-aiNet. The danger theory emphasizes that danger signals generated from changes of environments will guide different levels of immune responses, and the areas around danger signals are called danger zones. By defining the danger zone to calculate danger signals for each antibody, the algorithm adjusts antibodies' concentrations through its own danger signals and then triggers immune responses of self-regulation. So the population diversity can be maintained. Experimental results show that the algorithm has more advantages in the solution quality and diversity of the population. Compared with influential optimization algorithms, CLONALG, opt-aiNet, and dopt-aiNet, the algorithm has smaller error values and higher success rates and can find solutions to meet the accuracies within the specified function evaluation times.
authors.affiliation: College of Computer Science, Sichuan University, Chengdu 610065, China. zhangruiruisw@gmail.com
authors.forename: Ruirui
authors.initials: R
authors.lastname: Zhang
authors.forename: Tao
authors.initials: T
authors.lastname: Li
authors.forename: Xin
authors.initials: X
authors.lastname: Xiao
authors.forename: Yuanquan
authors.initials: Y
authors.lastname: Shi
mesh_terms: D000465:Algorithms; D000906:Antibodies / Q000276:immunology; D002999:Clone Cells; D006801:Humans; D007107:Immune System / Q000502:physiology*; D007111:Immunity, Cellular / Q000276:immunology*; D018448:Models, Immunological; D016571:Neural Networks, Computer; D015203:Reproducibility of Results
chemical_list: D000906:Antibodies