Synthesis constraints optimized genetic algorithm for autonomous task planning and allocating in MAS

Now, autonomous tasks planning and allocating (TPA) in Multi Agent System (MAS) has been one key and fundamental problem to promote the intelligent level of such system. Autonomous TPA means that, all tasks should be (re)planned and (re)allocated automatically according to the synthesis constraints and the dynamic environment aspects, such as the changing mission, status of each member, and topology, etc. In this article, the formal descriptions of hierarchical tasks and models of logic constraints are studied firstly. And then, some new methods are proposed to evaluate the efficiency of synthesis constraints. Moreover, the key elements, e.g. task allocation vector (TAV), are designed with the theory of genetic algorithm (GA), and a TPA problem can be mapped to the solving model of GA. Based on above, the crossover and mutation operators of GA are optimized with the domain knowledge to perfect the solving efficiency and quality while ensuring the randomicity of evolution. The simulation results show that the solving quality and velocity are improved with studied methods.