MOEA based memetic algorithms for multi-objective satellite range scheduling problem

Abstract Satellite range scheduling plays a very important role in guaranteeing the normal operation and the real-time control of in-orbit satellites. Although there appears a stronger demand for multi-objective optimization of satellite monitoring departments, multiple scheduling criteria are rarely considered simultaneously. To address the multi-objective satellite range scheduling problem (MOSRSP), a general MOEA based memetic algorithm (MOEA-MA) framework is proposed, which optimizes the failure rate of ground-satellite communication requests and the load-balance degree of remote-tracking antennas. Based on a novel decision model for MOSRSP, the conflict-resolution and load-balance operators and the tabu search metaheuristic are designed to implement the local search operations in the MOEA-MA. Different types of the MOEAs, including the domination-based MOEAs, decomposition-based MOEAs and metric-based MOEAs are adopted to implement the evolutionary operations in the MOEA-MA. The highlight of this paper is the effective application of the MOEA-MAs to practical scheduling problems, where the two most concerning objectives are well addressed. The MOEA-MAs that adopt five well-known MOEAs are given and examined by the Benchmarks problems. Computational results indicate that the MOEA-MAs outperform the original MOEAs in terms of the metrics of coverage, hypervolume and spacing, which show good performance and application prospect for the MOSRSP.

[1]  Carlos A. Coello Coello,et al.  Improved Metaheuristic Based on the R2 Indicator for Many-Objective Optimization , 2015, GECCO.

[2]  Kalyanmoy Deb,et al.  An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints , 2014, IEEE Transactions on Evolutionary Computation.

[3]  Javier Del Ser,et al.  Weighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planning , 2019, Swarm Evol. Comput..

[4]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[5]  Amir Hajjam El Hassani,et al.  A memetic algorithm for multi-objective optimization of the home health care problem , 2019, Swarm Evol. Comput..

[6]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[7]  Qingfu Zhang,et al.  A new learning-based adaptive multi-objective evolutionary algorithm , 2019, Swarm Evol. Comput..

[8]  Donald A. Parish.  A Genetic Algorithm Approach to Automating Satellite Range Scheduling , 1994 .

[9]  Fatos Xhafa,et al.  A Simulated Annealing Algorithm for Ground Station Scheduling Problem , 2013, 2013 16th International Conference on Network-Based Information Systems.

[10]  Kalyanmoy Deb,et al.  A Hybrid Framework for Evolutionary Multi-Objective Optimization , 2013, IEEE Transactions on Evolutionary Computation.

[11]  L. Darrell Whitley,et al.  Scheduling Space–Ground Communications for the Air Force Satellite Control Network , 2004, J. Sched..

[12]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[13]  Nicolas Zufferey,et al.  Graph colouring approaches for a satellite range scheduling problem , 2008, J. Sched..

[14]  Yan-Jie Song,et al.  Learning-guided nondominated sorting genetic algorithm II for multi-objective satellite range scheduling problem , 2019, Swarm Evol. Comput..

[15]  Fabrizio Marinelli,et al.  A Lagrangian heuristic for satellite range scheduling with resource constraints , 2011, Comput. Oper. Res..

[16]  Adele E. Howe,et al.  Understanding Algorithm Performance on an Oversubscribed Scheduling Application , 2006, J. Artif. Intell. Res..

[17]  Hui Li,et al.  An improved MOEA/D algorithm for multi-objective multicast routing with network coding , 2017, Appl. Soft Comput..

[18]  Wali Khan Mashwani,et al.  Multiobjective memetic algorithm based on decomposition , 2014, Appl. Soft Comput..

[19]  Abraham P. Punnen,et al.  Satellite downlink scheduling problem: A case study , 2015 .

[20]  Eckart Zitzler,et al.  HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization , 2011, Evolutionary Computation.

[21]  D. Arivudainambi,et al.  Memetic algorithm for minimum energy broadcast problem in wireless ad hoc networks , 2013, Swarm Evol. Comput..

[22]  Jing Liu,et al.  A multi-objective memetic algorithm based on decomposition for big optimization problems , 2016, Memetic Comput..

[23]  Na Zhang,et al.  Ant colony algorithm for satellite control resource scheduling problem , 2018, Applied Intelligence.

[24]  Karthikeyan Venkitusamy,et al.  Multi objective evolutionary algorithm for designing energy efficient distribution transformers , 2018, Swarm Evol. Comput..

[25]  Timothy D Gooley.  Automating the Satellite Range Scheduling Process , 1993 .

[26]  Yalin Chen,et al.  A modified MOEA/D approach to the solution of multi-objective optimal power flow problem , 2016, Appl. Soft Comput..

[27]  Qingfu Zhang,et al.  An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition , 2015, IEEE Transactions on Evolutionary Computation.

[28]  Ling Wang,et al.  A competitive memetic algorithm for multi-objective distributed permutation flow shop scheduling problem , 2017, Swarm and Evolutionary Computation.

[29]  Dexian Huang,et al.  Designing Neural Networks Using PSO-Based Memetic Algorithm , 2007, ISNN.

[30]  L. Darrell Whitley,et al.  Satellite Range Scheduling: A Comparison of Genetic, Heuristic and Local Search , 2002, PPSN.

[31]  Richard Curran,et al.  Delft University of Technology An improved MOEA/D algorithm for bi-objective optimization problems with complex Pareto fronts and its application to structural optimization , 2017 .

[32]  Xin-She Yang,et al.  Bio-inspired computation: Where we stand and what's next , 2019, Swarm Evol. Comput..

[33]  Qingfu Zhang,et al.  Comparison between MOEA/D and NSGA-III on a set of novel many and multi-objective benchmark problems with challenging difficulties , 2019, Swarm Evol. Comput..

[34]  Ye Tian,et al.  PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum] , 2017, IEEE Computational Intelligence Magazine.

[35]  Yu Liu,et al.  Satellite range scheduling with the priority constraint: An improved genetic algorithm using a station ID encoding method , 2015 .

[36]  Ye Tian,et al.  An Indicator-Based Multiobjective Evolutionary Algorithm With Reference Point Adaptation for Better Versatility , 2018, IEEE Transactions on Evolutionary Computation.

[37]  Qingfu Zhang,et al.  Problem Specific MOEA/D for Barrier Coverage with Wireless Sensors , 2017, IEEE Transactions on Cybernetics.

[38]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[39]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[40]  L. Darrell Whitley,et al.  AFSCN scheduling: How the problem and solution have evolved , 2006, Math. Comput. Model..

[41]  Yu Lei,et al.  A memetic algorithm based on MOEA/D for the examination timetabling problem , 2018, Soft Comput..

[42]  R. Scott Erwin,et al.  On the tractability of satellite range scheduling , 2015, Optim. Lett..

[43]  Hao Chen,et al.  Multi-satellite data downlink resource scheduling algorithm for incremental observation tasks based on evolutionary computation , 2015, 2015 Seventh International Conference on Advanced Computational Intelligence (ICACI).

[44]  Mostafa Zandieh,et al.  Bi-objective group scheduling in hybrid flexible flowshop: A multi-phase approach , 2010, Expert Syst. Appl..

[45]  Qiang Li,et al.  High-performance technique for satellite range scheduling , 2017, Comput. Oper. Res..