Evolutionary Computation with Spatial Receding Horizon Control to Minimize Network Coding Resources

Figure 4

Patterns of sub-solutions in TRHC and SRHC.

(a) In temporal receding horizon control, no matter what kind of method, deterministic method or population-based algorithm, is used as the online optimizer, the sub-solutions for the past time instants will have been uniquely decided and executed, and the sub-solutions for the future time instants will be optimized only based on the unchangeable consequence of the past sub-solutions. Therefore, short-sighted behaviours are common in temporal receding horizon control, because the unchangeable past sub-solutions might not be optimal or even good in terms of the performance over the entire time scope. A combination of spatial receding horizon control and deterministic method has the same solution pattern (except the time axis is replaced by a spatial axis)

(b) In spatial receding horizon control combined with population-based algorithm, the sub-solutions for decided spatial steps are not fixed or executed. Based on some top different solutions (e.g., the best 10 different solutions) calculated in the last rune of optimization, a sub-solution pool is set up for decided spatial steps. Different candidates in the pool may have different sub-solutions for a same decided spatial step. Therefore, in the current run of optimization, besides calculating the sub-solutions for the spatial steps within the current spatial receding horizon (CSRH), it also needs, for the sake of optimality, to choose a candidate from the pool for those decided spatial steps