Dynamic Resource Scheduling for C2 Organizations Based on Multi-Objective Optimization

Abstract

The dynamic resource scheduling problem is a field of intense research in command and control organization mission planning. This paper analyzes the emergencies in the battlefield first and divides them into three categories: the changing of task attributes, reduction of available platforms, and change in the number of tasks. To deal with these emergencies, in this paper, we built a series of multiobjective optimization models that maximizes the task execution quality and minimizes the cost of plan adjustment. To solve the model, we proposed an improved multi-objective evolutionary algorithm. A type of mapping operator and an improved crowding-distance sorting method are designed for the algorithm. Finally, the availability of the model and the solving algorithm were proved through a series of experiments. The Pareto frontier for the multi-objective dynamic resource scheduling problem can be found effectively, and the algorithm proposed in this paper shows better convergence compared with the AMP-NSGA-II algorithm.INDEX TERMS Command and control organization, dynamic resource scheduling, multi-objective evolutionary algorithm, multi-objective optimization.

Holonic-C2 Organization Structure Generation Method Based on Clustering Optimization Algorithms

“…The center location of the task cluster, C k , is Lc k = (Lx c k , Ly c k ). The task-cluster resource requirement vector calculation method is expressed by (1) and (2).…”

“…If yes, update the cluster decision matrix, x(x ik = 1 and the remaining elements in the i-th row are zero.). Update the center-point position and distance matrix, Dc, according to (2) and (3).Update the deviation vector, dev.…”

“…The command and control (C2) organization [1], [2] is a task execution consortium formed by the organic association of specific organizational tasks for dealing with specific mission tasks. In the military field, C2 organization refers to a military organization that uses operational resources in an orderly manner to well accomplish the mission.…”

A multipopulation evolutionary framework with Steffensen’s method for dynamic multiobjective optimization problems

“…With multiple conflicting objectives, multiobjective optimization problems (MOPs) [1] have been successfully solved by various evolutionary algorithms (EAs), such as NSGA-II [2], SPEA2 [3], MOPSO [4], MOEA/D [5], ACO [6], and so forth. Since lots of real-world MOPs need to be optimized in dynamic environments [7][8][9][10][11], how to extend multiobjective evolutionary algorithms (MOEAs) to solve dynamic multiobjective optimization problems (DMOPs) has attracted more and more attention. For static MOEAs, the goal is to find accurate and well-distributed Pareto-optimal fronts (PFs).…”

Service Deployment of C4ISR Based on Genetic Simulated Annealing Algorithm

“…From the perspective of architecture, in order to realize the flexible construction and agile evolution of the system [2], and considering the distribution, heterogeneity, and different control methods of the various weapons, the future C4ISR is bound to adopt the SOA (Service-Oriented Architecture) in which the functional modules such as detection, early warning, command, and control in various arms are serviced and packaged through a unified method. In this way, the functional modules that are tightly coupled in the same field can be decoupled, so as to realize the call of each functional module across the arms mode.…”