A bi-objective dynamic collaborative task assignment under uncertainty using modified MOEA/D with heuristic initialization

Abstract

The collaborative task assignment involved in Command and Control Systems is a key problem to be solved. The existing researches have their limitations to the natures of dynamic, uncertainty, flexibility and cooperation in a defensive scenario. Aiming at these, we formulate a bi-objective multi-stage task assignment model. The cooperation between sensor platforms and weapon platforms is considered. Also a Soyster robust model is introduced to handle uncertainty in a real time assignment process. Multi-objective evolutionary algorithm based on decomposition (MOEA/D) is adopted for the purpose of command flexibility. Currently, research focusing on multi-objective heuristics is relatively lacking. In this paper, we present a novel constructive heuristic for initializing the population. It successively adds quaternions into the assignment scheme to construct a solution set along the Pareto front, which is an interesting heuristic framework for multi-objective problems. We have also modified MOEA/D with nadir-based Tchebycheff and utilized the proposed neighbor matching strategy to gain better performance. Since algorithms are sensitive to their parameters, the Taguchi method with a novel response metric is utilized to calibrate the parameters. Numerical experiments demonstrate the superiority of the proposed algorithm and the necessity of a robust model.