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We present an aggressive task allocation strategy for an Artificial Hormone System (AHS). The AHS is a completely decentralized operation principle for a middleware which can be used to allocate tasks in a system of heterogeneous processing elements (PEs) or cores. Tasks are scheduled according to suitability of the heterogeneous PEs, current PE load and task relationships. In addition, the AHS provides properties like self-configuration, self-optimization and self-healing by task allocation. The AHS is able to guarantee real-time bounds regarding these self-X-properties. The aggressive task allocation strategy presented in this paper allows to halve the worst case execution times for the self-X-properties compared to previous strategies thus improving the suitability of the AHS for hard real-time systems.