Research-based The "Ant Colony" Algorithm Multi-satellite Mission Planning Joint Imaging Problems

The study found that the answers provided by the biosphere is applied to actualproblem solving has proven to be a successful method, and has formed a special branchof science-bionics. Biologists carefully study found ants in the course of the campaign,leaving the substance of a "pheromone" on its path through indirect communication, theother ants in the course of the campaign can sense this substance, and thus to guide theforward direction, and through this collaboration to find the shortest path from nest tofood. Affected by this phenomenon inspired by the Italian scholar Marco Dorigosimulated ant colony foraging behavior of a population-based evolutionary algorithm,ant colony algorithm.With the development of space technology, the number and types of earth observationsatellites gradually increased, through Earth observation satellite-derived informationhas been widely applied to various fields of human life and production, the demand forthe corresponding task is also a rapid increase, however, satellite payload capacity andground receiving resource capacity constraints can not meet all user needs. Papers willbe the introduction of the ant colony algorithm to earth observation satellite missionplanning areas, improvement and adaptation and algorithm optimization for a variety ofuser requests the allocation of resources and time to achieve the ability to give full playto the satellite system, rational and efficient use of valuable The objectives of thesatellite resources.The main content is as follows:Describes the composition of the earth observation satellite platform, the analysisof a variety of characteristics and the use of constraints of the satellite payload, the earthobservation satellite observations. Then, many different types of satellite resources anduser observation mission of the Unified Modeling description of the scheduling problemof multi-satellite imaging mission planning, the main features of the input and output isdiscussed in detail.Asked the mathematical model of planning and scheduling, to determine thevehicle routing model. First multi-satellite joint imaging mission planning constraints analysis, establish the constraints and the basic assumptions on this basis to establish ajoint imaging of multi-satellite mission planning mathematical model of the problem.This chapter of the ant colony algorithm based on principle, the characteristics ofthe study. Brief introduction to the basic ant colony algorithm, in order to avoid the antcolony algorithm is easily trapped into local optimal solution, defects, design process,through the analysis of the lack of ant colony algorithm and problem-solvingmechanism based on the elitist strategy with Max-Min ant colony system. satelliteimagery task scheduling algorithm.Finally, simulation results for the different size of the problem, different resources,different task distribution under the conditions of the problem, to verify the validity andapplicability of the algorithm used in this study.