The adaptive optimization of C2 organization decision layer structure based on nested improved simulated annealing algorithm

The C2 organization decision layer structure adaptive optimization problem (DLSAOP) is studied. Three C2 organization decision layer structure (DLS) performance measures, including decision workload, decision quality and decision gain, are presented. Then a constrained optimization model for DLSAOP is established. DLSAOP is a novel combinational optimization problem with strong constraints and two variants. A nested improved simulated annealing (NISA) algorithm is designed for the problem. The standard simulated annealing (SA) algorithm has shortcomings of poor convergence, local repeatedly searching and early stagnation under strong constraints, for which diversified temperature controlling, tabu object and evaluation function containing items of constraint-violate-punishment are incorporated into NISA. A nesting approach is presented to combine the two improved SA algorithms for the two variants. At last, the computational experiment illustrates that DLSAOP modeling and optimization can remarkably improve DLS performance and the result of NISA has better quality and stability than other algorithms.