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mixed integer programming and heuristic scheduling for space communication networksIn this paper, we propose to solve the constrained optimization problem in two phases. The first phase uses heuristic methods such as the ant colony method, particle swarming optimization, and genetic algorithm to seek a near optimal solution among a list of feasible initial populations. The final optimal solution can be found by using the solution of the first phase as the initial condition to the SQP algorithm. We demonstrate the above problem formulation and optimization schemes with a large-scale network that includes the DSN ground stations and a number of spacecraft of deep space missions.
Document ID
Document Type
Conference Paper
External Source(s)
Lee, Charles H.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Cheung, Kar-Ming
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
May 26, 2015
Publication Date
March 3, 2012
Subject Category
Communications and Radar
Meeting Information
IEEE Aerospace Conference(Big Sky, MT)
Distribution Limits
Communication Network Scheduling
Heuristic Optimization
Particle Swarm Optimization

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IDRelationTitle20130001861See AlsoMixed Integer Programming and Heuristic Scheduling for Space Communication Networks