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Quasi-oppositional Harmony Search Algorithm Approach for Ad Hoc and Sensor Networks

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Nature Inspired Computing for Wireless Sensor Networks

Part of the book series: Springer Tracts in Nature-Inspired Computing ((STNIC))

Abstract

Wireless communication technologies are under the process of rapid development. In the past few years, this field has experienced a steep growth in teaching and research activity, especially, in the area of wireless ad hoc and sensor network. In recent studies, continuously research work is going on for the optimum design of the metering and low-power devices for daily life usages. Wireless sensor networks (WSNs) may be one of the options to meet the above requirement. It deals with the major issues of energy limitations, challenges of handling traffic and lifetime of the battery. Network lifetime, energy efficiency, energy consumption and solving routing problems are some of the problems that need to be discussed with WSN. To achieve these constraints, an effective optimization method may be used as an effective and useful tool. Most of the optimization techniques are inspired by some phenomenon found in nature. One of them is quasi-oppositional harmony search algorithm. Although it is under developing stage, it is still a powerful optimization technique. It has the potential ability to solve various engineering optimization problems. It has many advantages that make it applicable to use in WSN and its related work.

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References

  1. Kulkarni RV, Venayagamoorthy GK (2010) Particle swarm optimization in wireless-sensor networks: a brief survey. IEEE Trans Syst Man Cybern Part C Appl Rev 41(2):262–267

    Article  Google Scholar 

  2. Aggarwal R, Mittal A, Kaur R (2016) Various optimization techniques used in wireless sensor networks. Int Res J Eng Technol 3(6):2085–2090

    Google Scholar 

  3. Das SK, Yadav AK, Tripathi S (2017) IE2M: design of intellectual energy efficient multicast routing protocol for ad-hoc network. Peer-to-Peer Netw Appl 10(3):670–687

    Article  Google Scholar 

  4. Yadav AK, Das SK, Tripathi S (2017) EFMMRP: design of efficient fuzzy based multi-constraint multicast routing protocol for wireless ad-hoc network. Comput Netw 118:15–23

    Article  Google Scholar 

  5. Das SK, Tripathi S (2018) Intelligent energy-aware efficient routing for MANET. Wirel Netw 24(4):1139–1159

    Article  Google Scholar 

  6. Das SK, Tripathi S (2019) Energy efficient routing formation algorithm for hybrid ad-hoc network: a geometric programming approach. Peer-to-Peer Netw Appl 12(1):102–128

    Article  Google Scholar 

  7. Seifi H, Sepasian MS (2011) Electric power system planning: issues, algorithms and solutions. Springer, Berlin

    Book  Google Scholar 

  8. Engelbrecht AP (2007) Computational intelligence: an introduction. Wiley, London

    Book  Google Scholar 

  9. Chatterjee S, Hore S, Dey N, Chakraborty S, Ashour AS (2017) Dengue fever classification using gene expression data: a PSO based artificial neural network approach. In: Proceedings of the 5th international conference on frontiers in intelligent computing: theory and applications. Springer, Singapore, pp 331–341

    Google Scholar 

  10. Jagatheesan K, Anand B, Samanta S, Dey N, Ashour AS, Balas VE (2017) Particle swarm optimisation-based parameters optimisation of PID controller for load frequency control of multi-area reheat thermal power systems. Int J Adv Intell Paradigms 9(5–6):464–489

    Article  Google Scholar 

  11. Kaliannan J, Baskaran A, Dey N, Ashour AS (2016) Ant colony optimization algorithm based PID controller for LFC of single area power system with non-linearity and boiler dynamics. World J Model Simul 12(1):3–14

    Google Scholar 

  12. Jagatheesan K, Anand B, Dey KN, Ashour AS, Satapathy SC (2018) Performance evaluation of objective functions in automatic generation control of thermal power system using ant colony optimization technique-designed proportional–integral–derivative controller. Electr Eng 100(2):895–911

    Article  Google Scholar 

  13. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulations 76:60–68

    Article  Google Scholar 

  14. Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. Appl Math Comput 188:1567–1579

    MathSciNet  MATH  Google Scholar 

  15. Lee KS, Geem ZW (2004) A new structural optimization method based on the harmony search algorithm. Comput Struct 82:781–798

    Article  Google Scholar 

  16. Kim JH, Geem ZW, Kim ES (2001) Parameter estimation of the nonlinear Muskingum model using harmony search. J Am Water Resour Assoc 37:1131–1138

    Article  Google Scholar 

  17. Geem ZW, Kim JH, Loganathan GV (2002) Harmony search optimization: application to pipe network design. Int J Model Simul 22:125–133

    Article  Google Scholar 

  18. Omran MGH, Mahdavi M (2008) Global-best harmony search. Appl Math Comput 198:643–656

    MathSciNet  MATH  Google Scholar 

  19. Pan QK, Suganthan PN, Tasgetiren MF, Liang JJ (2010) A self-adaptive global best harmony search algorithm for continuous optimization problems. Appl Math Comput 216:830–848

    MathSciNet  MATH  Google Scholar 

  20. Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In: Proceedings of international conference on computational intelligence for modelling, control and automation, vol 1, pp 695–701

    Google Scholar 

  21. Tizhoosh HR (2005) Reinforcement learning based on actions and opposite actions. In: Proceedings of ICGST international conference on artificial intelligence and machine learning, Egypt

    Google Scholar 

  22. Tizhoosh HR (2006) Opposition-based reinforcement learning. J Adv Comput Intell Inf 10:578–585

    Article  Google Scholar 

  23. Ventresca M, Tizhoosh HR (2006) Improving the convergence of backpropagation by opposite transfer functions. In: Proceedings of IEEE world congress on computational intelligence, Vancouver, BC, Canada, pp 9527–9534

    Google Scholar 

  24. Rahnamayan S, Tizhoosh HR, Salama MMA (2008) Opposition-based differential evolution. IEEE Trans Evol Comput 12:64–79

    Article  Google Scholar 

  25. Nandi M, Shiva CK, Mukherjee V (2017) TCSC based automatic generation control of deregulated power system using quasi-oppositional harmony search algorithm. Eng Sci Technol 20(4):1380–1395

    Google Scholar 

  26. Shiva CK, Mukherjee V (2016) Automatic generation control of hydropower systems using a novel quasi-oppositional harmony search algorithm. Electr Power Compon Syst 44(13):1478–1491

    Article  Google Scholar 

  27. Shiva CK, Mukherjee V (2015) A novel quasi-oppositional harmony search algorithm for automatic generation control of power system. Appl Soft Comput 35:749–765

    Article  Google Scholar 

  28. Moradi-Dalvand M, Mohammadi-Ivatloo B, Najafi A, Rabiee A (2012) Continuous quick group search optimizer for solving non-convex economic dispatch problems. Electr Power Syst Res 93:93–105

    Article  Google Scholar 

  29. He S, Wu QH, Saunders J (2009) Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Trans Evol Comput 13(5):973–990

    Article  Google Scholar 

  30. Haghrah A, Mohammadi-ivatloo B, Seyedmonir S (2015) Real coded genetic algorithm approach with random transfer vectors-based mutation for short-term hydro-thermal scheduling. IET Gener Trans Distrib 9:75–89

    Article  Google Scholar 

  31. Mazinani A, Mazinani SM, Mirzaie M (2019) FMCR-CT: an energy-efficient fuzzy multi cluster-based routing with a constant threshold in wireless sensor network. Alex Eng J 58(1):127–141

    Article  Google Scholar 

  32. Taherian M, Karimi H, Kashkooli AM, Esfahanimehr A, Jafta T, Jafarabad M (2015) The design of an optimal and secure routing model in wireless sensor networks by using PSO algorithm. Proc Comput Sci 73:468–473

    Article  Google Scholar 

  33. Arya R, Sharma SC (2015) Analysis and optimization of energy of sensor node using ACO in wireless sensor network. Proc Comput Sci 45:681–686

    Article  Google Scholar 

  34. Wang H, Agoulmine N, Ma M, Jin Y (2010) Network lifetime optimization in wireless sensor networks. IEEE J Sel Areas Commun 28(7):1127–1137

    Article  Google Scholar 

  35. Simon G, Volgyesi P, Maróti M, Ledeczi A (2003) Simulation-based optimization of communication protocols for large-scale wireless sensor networks. In: IEEE aerospace conference, vol 3, pp 31339–31346

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, S R Engineering College Warangal, Ananthsagar, Hasanparthy, Warangal, Telangana, 506371, India

    Chandan Kumar Shiva & Ritesh Kumar

Authors
  1. Chandan Kumar Shiva
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  2. Ritesh Kumar
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Corresponding author

Correspondence to Chandan Kumar Shiva .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Maulana Abul Kalam Azad University, Kolkata, West Bengal, India

    Debashis De

  2. Department of Computer Science and Engineering, Institute of Engineering and Management, Kolkata, West Bengal, India

    Amartya Mukherjee

  3. School of Computer Science and Engineering, National Institute of Science and Technology, Berhampur, Odisha, India

    Santosh Kumar Das

  4. Department of Information Technology, Techno India College of Technology, Kolkata, India

    Nilanjan Dey

Appendix

Appendix

1.1 Parameters of QOHS

Number of parameters depends on problem variables, population size = 50, total number of iteration = 100, HMCR = 0.9, PARmin = 0.45, PARmax = 0.98, BWmin = 0.0005, BWmax = 50, Jr = 0.8,

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Shiva, C.K., Kumar, R. (2020). Quasi-oppositional Harmony Search Algorithm Approach for Ad Hoc and Sensor Networks. In: De, D., Mukherjee, A., Kumar Das, S., Dey, N. (eds) Nature Inspired Computing for Wireless Sensor Networks. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-15-2125-6_9

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  • DOI: https://doi.org/10.1007/978-981-15-2125-6_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-2124-9

  • Online ISBN: 978-981-15-2125-6

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