Skip to main content
SpringerLink
Log in

Multiobjective evolutionary algorithm for frequency assignment problem in satellite communications

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Satellite communications technology leads to an important improvement in our life and world. The frequency assignment problem (FAP) is a fundamental problem in satellite communication system for providing high-quality transmissions. The whole goal of the FAP in satellite communication system is to minimize co-channel interference between two satellite systems by rearranging frequency assignment. Recently, many metaheuristics, including neural networks and evolutionary algorithms, are proposed for this NP-complete problem. All such algorithms formulate the FAP as a single-objective problem, although it obviously has two objectives and thus essentially is a multiobjective optimization problem. This study explicitly formulates FAP as a multiobjective optimization problem and presents a multiobjective evolutionary algorithm based on decomposition (MOEA/D) with a problem-specific subproblem-dependent heuristic assignment (SHA), called MOEA/D-SHA, for the multiobjective FAP. Simulation results show that the MOEA/D-SHA outperforms significantly general-purpose MOEA/D, and an off-the-shelf multiobjective algorithm, i.e., NSGA-II. The advantages of the MOEA/D-SHA over the state-of-the-art single-objective approaches are also shown.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Aardal KI, van Hoesel SPM, Koster AMCA, Mannino C, Sassano A (2007) Models and solution techniques for frequency assignment problems. Ann Oper Res 153(1):79–129

  • Alcalá-Fdez J, Sánchez L, García S (2008) KEEL: a software tool to assess evolutionary algorithms to data mining problems. Soft Comput 13(3):307–318

  • Bechikh S, Said LB, Ghédira K (2011) Searching for knee regions of the Pareto front using mobile reference points. Soft Comput 15(9):1807–1823

    Article  Google Scholar 

  • Castro-Gutierrez J, Landa-Silva D, Pérez JM (2011) Nature of real world multi-objective vehicle routing with evolutionary algorithms. In: Proceedings of IEEE International Conference on SMC, pp 257–264

  • Czyzzak P, Jaszkiewicz A (1998) Pareto simulated annealing: a metaheuristic technique for multiple-objective combinatorial optimization. J Multi-Criteria Decis Anal 7(1):34–47

    Article  Google Scholar 

  • Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evolut Comput 15(1):4–31

    Article  Google Scholar 

  • Deb K, Jain H (2014) An evolutionary many-objective optimization algorithm using reference-point based non-dominated sorting approach, part I: solving problems with box constraints. In: Proceedings of IEEE Transactions on Evolutionary Computation, 2014 (in press)

  • Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evolut Comput 6(2):182–197

    Article  Google Scholar 

  • Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evolut Comput 1(1):3–18

    Article  Google Scholar 

  • Funabiki N, Nishikawa S (1997) A gradual neural-network approach for frequency assignment in satellite communication systems. IEEE Trans Neural Netw 8(6):1359–1370

    Article  Google Scholar 

  • Garcia-Najera A, Bullinaria J (2011) An improved multi-objective evolutionary algorithm for the vehicle routing problem with time windows. Comput Oper Res 38(1):287–300

    Article  MATH  MathSciNet  Google Scholar 

  • Hopfield JJ, Tank DW (1985) Neural computation of decisions in optimization problems. Biol Cybern 52:141–152

    MATH  MathSciNet  Google Scholar 

  • Ishibuchi H, Hitotsuyanagi Y, Tsukamoto N, Nojima Y (2008) Use of heuristic local search for single-objective optimization in multiobjective memetic algorithms. In: Proceedings of parallel problem solving from nature-PPSN X, pp 743–752

  • Ishibuchi H, Yoshida T, Murata T (2003) Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE Trans Evol Comput 7(2):204–223

    Article  Google Scholar 

  • Jaszkiewicz A (2003) Do multiple objective metaheuristics deliver on their promises? A computational experiment on the set covering problem. IEEE Trans Evolut Comput 7(2):133–143

    Article  MathSciNet  Google Scholar 

  • Ke L, Zhang Q, Battiti R (2013) MOEA/D-ACO: a multiobjective evolutionary algorithm using decomposition and ant colony. IEEE Trans Cybern 43(6):1845–1859

    Article  Google Scholar 

  • Konstantinidis A, Yang K (2011a) Multi-objective energy-efficient dense deployment in wireless sensor networks using a hybrid problem-specific MOEA/D. Appl Soft Comput 11(6):4117–4134

    Article  Google Scholar 

  • Konstantinidis A, Yang K (2011b) Multi-objective k-connected deployment and power assignment in WSNs using a problem-specific constrained evolutionary algorithm based on decomposition. Comput Commun 34(1):83–98

    Article  Google Scholar 

  • Kurokawa T, Kozuka S (1993) A proposal of neural networks for the optimum frequency assignment problem. Trans IEICE J76–B–II(10):811–819 (in Japanese)

    Google Scholar 

  • Lacomme P, Prins C, Sevaux M (2006) A genetic algorithm for a biobjective capacitated arc routing problem. Comput Oper Res 33(12):3473–3493

    Article  MATH  Google Scholar 

  • Li H, Landa-silva D, Gandibleux X (2010) Evolutionary multi-objective optimization algorithms with probabilistic representation based on pheromone trails. In: Proceedings of the IEEE congress on evolutionary computation, pp 2307–2314

  • Li H, Zhang Q (2009) Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II. IEEE Trans Evolut Comput 12(2):284–302

    Article  Google Scholar 

  • Li H, Landa-Silva D (2011) An adaptive evolutionary multi-objective approach based on simulated annealing. Evolut Comput 19(4):561–595

    Article  Google Scholar 

  • Liu W, Shi H, Wang L (2007) Minimizing interference in satellite communications using chaotic neural networks. In: Proceeding of the third international conference on natural computation, pp 441–444

  • Luna F, Estébanez C et al (2011) Optimization algorithms for large-scale real-world instances of the frequency assignment problem. Soft Comput 15(5):975–990

    Article  Google Scholar 

  • Maximiano MS, Vega-Rodríguez MA et al (2009) Multiobjective frequency assignment problem using the MO-VNS and MO-SVNS algorithms. In: Proceedings of the world congress on nature and biologically inspired computing, pp 221–226

  • Maximiano MS, Vega-Rodríguez MA et al (2009) Parameter analysis for differential evolution with Pareto tournaments in a multiobjective frequency assignment problem. In: Proceedings of the 10th international conference on intelligent data engineering and automated learning, pp 799–806

  • Maximiano MS, Vega-Rodríguez MA et al (2013) A new multiobjective artificial bee colony algorithm to solve a real-world frequency assignment problem. Neural Comput Appl 22(7–8):1447–1459

    Article  Google Scholar 

  • Mei Y, Tang K, Yao X (2011) Decomposition-based memetic algorithm for multi-objective capacitated arc routing problem. IEEE Trans Evolut Comput 15(2):151–165

    Article  Google Scholar 

  • Mizuike T, Ito Y (1989) Optimization of frequency assignment. IEEE Trans Commun 37(10):1031–1041

    Article  Google Scholar 

  • Mizuike T, Ito Y (1986) Optimum frequency assignment for reduction of cochannel interference. Trans IEICE J69–B(9):921–932 (in Japanese)

    Google Scholar 

  • Pelton JN, Oslund RJ, Marshall P (2004) Communications satellites: global change agents. Lawrence Erlbaum, Mahwah

    Google Scholar 

  • Peng W, Zhang Q, Li H (2009) Comparison between MOEA/D and NSGA-II on the multi-objective travelling salesman problem. In: Proceedings of studies in computational intelligence, multi-objective memetic algorithms, vol 171, pp 309–324

  • Peng W, Zhang Q (2012) Network topology planning using MOEA/D with objective-guided operators. In: Proceedings of parallel problem solving from nature-PPSN II, pp 62–71

  • Praditwong K, Harman M, Yao X (2011) Software module clustering as a multi-objective search problem. IEEE Trans Softw Eng 37(2):264–282

    Article  Google Scholar 

  • Qu B-Y, Li H, Zhao S-Z, Suganthan PN, Zhang Q (2011) Multiobjective evolutionary algorithms: a survey of the state-of-the-art. Swarm Evolut Comput 1(1):32–49

    Article  Google Scholar 

  • Saha S, Sur-Kolay S, Dasgupta P, Bandyopadhyay S (2009) MAkE: multiobjective algorithm for k-way equipartitioning of a point set. Appl Soft Comput 9(2):711–724

    Article  Google Scholar 

  • Salcedo-Sanz S, Santiago-Mozos R, B-Calz’on C (2004) A hybrid Hopfield network-simulated annealing approach for frequency assignment in satellite communications systems. IEEE Trans Syst Man Cybern-B Cybern 34(2):1108–1116

    Article  Google Scholar 

  • Salcedo-Sanz S, B-Calz’on C (2005) A hybrid neural-genetic algorithm for the frequency assignment problem in satellite communications. Appl Intell 22(3):207–217

    Article  MATH  Google Scholar 

  • Salman AA, Ahmad I, Omran MGH, Mohammad MG (2010) Frequency assignment problem in satellite communications using differential evolution. Comput Oper Res 37(12):2152–2163

    Article  MATH  MathSciNet  Google Scholar 

  • Shim VA, Tan KC, Cheong CY (2012) A hybrid estimation of distribution algorithm with decomposition for solving the multiobjective multiple traveling salesman problem. IEEE Trans Syst Man Cybern Part C Rev Appl 42(5):682–691

    Article  Google Scholar 

  • Storn RM, Price KV (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  MATH  MathSciNet  Google Scholar 

  • Talbi E-G, Basseur M, Nebro AJ, Alba E (2012) Multi-objective optimization using metaheuristics: non-standard algorithms. Int Trans Oper Res 19(1—-2):283–305

    Article  MATH  MathSciNet  Google Scholar 

  • Tan KC, Chew YH, Lee LH (2006) A hybrid multiobjective evolutionary algorithm for solving vehicle routing problem with time windows. Comput Optim Appl 34(1):115–151

    Article  MATH  MathSciNet  Google Scholar 

  • Wang J, Cai Y, Yin J (2011) Multi-start stochastic competitive Hopfield neural network for frequency assignment problem in satellite communications. Expert Syst Appl 38(1):131–145

  • Wang L, Liu W, Shi H (2008) Noisy chaotic neural networks with variable thresholds for the frequency assignment problem in satellite communications. IEEE Trans Syst Man Cybern Part C Rev Appl 38(2):209–217

    Article  MATH  Google Scholar 

  • Wang Z, Tang K, Yao X (2010) Multi-objective approaches to optimal testing resource allocation in modular software systems. IEEE Trans Reliab 59(3):563–575

  • Whalen DJ (2008) Communication satellites: making the global village possible. Available at: http://www.hq.nasa.gov/office/pao/History/satcomhistory.html

  • Zhang Q, Liu W, Li H (2009) The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances. In: Proceedings of IEEE congress on evolutionary computation, pp 1–6

  • Zhang Q, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evolut Comput 11(6):712–731

    Article  Google Scholar 

  • Zitzler E, Laumanns M, Thiele L (2002) SPEA2: improving the strength Pareto evolutionary algorithm. In: Proceedings of evolution methods for design, optimization and control applications to industrial problems, pp 95–100

  • Zitzler E, Thiele L (1999) Multi-objective evolutionary algorithms: a comparative study and the strength Pareto approach. IEEE Trans Evolut Comput 3(4):257–271

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank Salman et al. (2010) for sending the original source code of the DE for FAP. This work was supported in part by the National Natural Science Foundation of China (60805026, 61070076, 61305085), and the Zhujiang New Star of Science and Technology in Guangzhou City (2011J2200093).

Author information

Authors and Affiliations

  1. Department of Computer Science, Sun Yat-sen University, Guangzhou, 510006, People’s Republic of China

    Jiahai Wang

  2. College of Computer Science and Technology, Huaqiao University, Xiamen, 361021, People’s Republic of China

    Yiqiao Cai

Authors
  1. Jiahai Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yiqiao Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jiahai Wang.

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, J., Cai, Y. Multiobjective evolutionary algorithm for frequency assignment problem in satellite communications. Soft Comput 19, 1229–1253 (2015). https://doi.org/10.1007/s00500-014-1337-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-014-1337-2

Keywords

Search

Navigation