title: Vaccine-enhanced artificial immune system for multimodal function optimization.
abstract: This paper emulates a biological notion in vaccines to promote exploration in the search space for solving multimodal function optimization problems using artificial immune systems (AISs). In this method, we first divide the decision space into equal subspaces. The vaccine is then randomly extracted from each subspace. A few of these vaccines, in the form of weakened antigens, are then injected into the algorithm to enhance the exploration of global and local optima. The goal of this process is to lead the antibodies to unexplored areas. Using this biologically motivated notion, we design the vaccine-enhanced AIS for multimodal function optimization, achieving promising performance.
authors.affiliation: Intelligent Systems and Control Laboratory, School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK 74078, USA. kumlach@okstate.edu
authors.forename: Kumlachew M
authors.initials: KM
authors.lastname: Woldemariam
authors.forename: Gary G
authors.initials: GG
authors.lastname: Yen
mesh_terms: D000465:Algorithms; D000915:Antibody Affinity; D001185:Artificial Intelligence; D006801:Humans; D007107:Immune System; D018448:Models, Immunological; D014612:Vaccines / Q000276:immunology
chemical_list: D014612:Vaccines