Hybrid evolutionary algorithms for network-centric command and control

摘要

Network-centric force optimization is the problem of threat engagement and dynamic Weapon-Target Allocation (WTA) across the force. The goal is to allocate and schedule defensive weapon resources over a given period of time so as to achieve certain battle management objectives subject to resource and temporal constraints. The problem addresses in this paper is one of dynamic WTA and involves optimization across both resources (weapons) and time. We henceforth refer to this problem as the Weapon Allocation and Scheduling problem (WAS). This paper addresses and solves the WAS problem for two separate battle management objectives: (1) Threat Kill Maximization (TKM), and (2) Asset Survival Maximization (ASM). Henceforth, the WAS problems for the above objectives are referred to as the WAS-TKM and WAS-ASM, respectively. Both WAS problems are NP-complete problem and belong to a class of multiple-resource-constrained optimal scheduling problems. While the above objectives appear to be intuitively similar from a battle management perspective, the two optimal scheduling problems are quite different in their complexity. We present a hybrid genetic algorithm (GA) that is a combination of a traditional genetic algorithm and a simulated annealing-type algorithm for solving these problems. The hybrid GA approach proposed here uses a simulated annealing-type heuristics to compute the fitness of a GA-selected population. This step also optimizes the temporal dimension (scheduling) under resource and temporal constraints and is significantly different for the WAS-TKM and WAS-ASM problems. The proposed method provides schedules that are near optimal in short cycle times and have minimal perturbation from one cycle to the next.