Sie befinden Sich nicht im Netzwerk der Universität Paderborn. Der Zugriff auf elektronische Ressourcen ist gegebenenfalls nur via VPN oder Shibboleth (DFN-AAI) möglich. mehr Informationen...
Target coverage optimisation of wireless sensor networks using a multi-objective immune co-evolutionary algorithm
Ist Teil von
International journal of systems science, 2011-09, Vol.42 (9), p.1531-1541
Ort / Verlag
Taylor & Francis Group
Erscheinungsjahr
2011
Link zum Volltext
Quelle
Taylor & Francis Journals Auto-Holdings Collection
Beschreibungen/Notizen
Target coverage is an important topic of wireless sensor networks. The target cover can be modelled as a minimal multi-objective vertex cover model with constraint of network connection. In order to search the optimal solution of the target cover set, we propose a multi-objective immune co-evolutionary algorithm (MOICEA) for target coverage. The MOICEA is inspired from the biological mechanisms of immune systems including clonal proliferation, hypermutation, co-evolution, immune elimination and memory mechanism. The affinity between antibody and antigen is used to measure the optimal target cover, and the affinity between antibodies is used to evaluate the diversity of population and to instruct the population evolution process. In order to examine the effectiveness of the MOICEA, we compare its performance with that of integer linear program and genetic algorithm in terms of four objectives while maintaining network connectivity. The experiment results show that the MOICEA can obtain promising performance in efficiently searching optimal vertex set by comparing with other approaches.