Multi Objective PSO with Passive Congregation for Load Balancing Problem

  • Mohammad Marufuzzaman
  • Muneed Anjum Timu
  • Jubayer Sarkar
  • Aminul Islam
  • Labonnah Farzana Rahman
  • Lariyah Mohd Sidek

Abstract

High-level architecture (HLA) and Distributed Interactive Simulation (DIS) are commonly used for the distributed system. However, HLA suffers from a resource allocation problem and to solve this issue, optimization of load balancing is required. Efficient load balancing can minimize the simulation time of HLA and this optimization can be done using the multi-objective evolutionary algorithms (MOEA). Multi-Objective Particle Swarm Optimization (MOPSO) based on crowding distance (CD) is a popular MOEA method used to balance HLA load. In this research, the efficiency of MOPSO-CD is further improved by introducing the passive congregation (PC) method. Several simulation tests are done on this improved MOPSO-CD-PC method and the results showed that in terms of Coverage, Spacing, Non-dominated solutions and Inverted generational distance metrics, the MOPSO-CD-PC performed better than the previous MOPSO-CD algorithm. Hence, it can be a useful tool to optimize the load balancing problem in HLA.

References

[1] Dahmani, N.; Krichen, S. (2016). Solving a load balancing problem with a multi-objective particle swarm optimisation approach: application to aircraft cargo transportation, International Journal of Operational Research, 27(1-2), 62-84, 2016.
https://doi.org/10.1504/IJOR.2016.10000057

[2] Ding, S.; Chen, C.; Xin, B.; Pardalos, P.M. (2018). A bi-objective load balancing model in a distributed simulation system using NSGA-II and MOPSO approaches, Applied Soft Computing, 63(C), 249-267, 2018.
https://doi.org/10.1016/j.asoc.2017.09.012

[3] Ficco, M.; Avolio, G.; Palmieri, F.; Castiglione, A. (2016). An HLA-based framework for simulation of large-scale critical systems, Concurrency and Computation: Practice and Experience, 28(2), 400-419, 2016.
https://doi.org/10.1002/cpe.3472

[4] Hossain,M. S.; Sidek,L. M.; Marufuzzaman,M.; Zawawi,M. H. (2018). Passive congregation theory for particle swarm optimization (PSO): An application in reservoir system operation, International Journal of Engineering and Technology (UAE), 7(4:35), 383-387, 2018.
https://doi.org/10.14419/ijet.v7i4.35.22767

[5] Li, L.; Wang, W.; Li, W.; Xu, X.; Zhao, Y. (2016). A novel ranking-based optimal guides selection strategy in MOPSO, Procedia Computer Science, 91, 1001-1010, 2016.
https://doi.org/10.1016/j.procs.2016.07.135

[6] Marufuzzaman M.; Reaz M.B.I.; Ali M.A.M.; Rahman L.F. (2015). A time series based sequence prediction algorithm to detect activities of daily living in smart home, Methods of information in medicine, 54(3), 262-270, 2015.
https://doi.org/10.3414/ME14-01-0061

[7] Marufuzzaman M.; Reaz M.B.I.; Rahman L.F.; Farayez A.A. (2015). A Location Based Sequence Prediction Algorithm for Determining Next Activity in Smart Home, Journal of Engineering Science & Technology Review, 10(2), 161-165, 2017.
https://doi.org/10.25103/jestr.102.19

[8] Marufuzzaman, M.; Al Karim, S.; Rahman, M. S.; Zahid, N. M.; Sidek, L. M. (2019). A review on reliability, security and memory management of numerous operating systems, Indonesian Journal of Electrical Engineering and Informatics, 7(3), 577-585, 2019.
https://doi.org/10.11591/ijeei.v7i3.987

[9] Mishra, S. K.; Panda, G.; Majhi, R.; (2014). A comparative performance assessment of a set of multiobjective algorithms for constrained portfolio assets selection, Swarm and Evolutionary Computation, 16, 38-51, 2014.
https://doi.org/10.1016/j.swevo.2014.01.001

[10] Nasonov, D.; Butakov, N.; Melnik, M.; Visheratin, A.; Linev, A.; Shvets, P.; Sobolev, S.; Mukhina, K. (2018). The multi-level adaptive approach for efficient execution of multi-scale distributed applications with dynamic workload, In: Voevodin V., Sobolev S. (eds) Supercomputing. RuSCDays 2018. Communications in Computer and Information Science, 965, Springer, Cham. 600-611, 2018.
https://doi.org/10.1007/978-3-030-05807-4_58

[11] Patel, D.K.; Tripathy, D.; Tripathy, C.R.(2016). Survey of load balancing techniques for Grids, Journal of Network and Computer Applications, 65(2016), 103-109, 2016.
https://doi.org/10.1016/j.jnca.2016.02.012

[12] Silva, T.WB.; Morais, D.C.; Andrade, H.G.R.; Lima, A.M.N.; Melcher, E.U.K.; Brito, A.V. (2018). Environment for integration of distributed heterogeneous computing systems, Journal of Internet Services and Applica, 9(4), 1-17, 2018.
https://doi.org/10.1186/s13174-017-0072-1

[13] Souravlas, S. (2019). ProMo: A Probabilistic Model for Dynamic Load-Balanced Scheduling of Data Flows in Cloud Systems, Electronics, 8(990), 1-15, 2019.
https://doi.org/10.3390/electronics8090990

[14] Xu, X.; Hu, Z.; Su, Q.; Xiong, Z. (2018). Multi-objective Collective Decision Optimization Algorithm for Economic Emission Dispatch Problem, Complexity, 2018(1027193), 1-20, 2018.
https://doi.org/10.1155/2018/1027193

[15] Zhou, A.; Qu, B.Y.; Li, H.; Zhao, S.Z.; Suganthan, P.N.; Zhang, Q. (2011). Multi-objective evolutionary algorithms: A survey of the state of the art, Swarm and Evolutionary Computation, 1, 32-49, 2011.
https://doi.org/10.1016/j.swevo.2011.03.001

[16] Zitzler, E.; Thiele, L. (1998). Multiobjective optimization using evolutionary algorithms - a comparative case study, In International conference on parallel problem solving from nature, 292-301, Springer, Berlin, 1998.
https://doi.org/10.1007/BFb0056872
Published
2021-09-14
How to Cite
MARUFUZZAMAN, Mohammad et al. Multi Objective PSO with Passive Congregation for Load Balancing Problem. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 16, n. 5, sep. 2021. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/4274>. Date accessed: 18 oct. 2021. doi: https://doi.org/10.15837/ijccc.2021.5.4274.