Skip to main content
SpringerLink
Log in

Bio-inspired Algorithm for Multi-objective Optimization in Wireless Sensor Network

  • Chapter
  • First Online:
Nature Inspired Computing for Wireless Sensor Networks

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

Abstract

In recent days bio-inspired computing is playing an important role in the area of research. Especially bio-inspired algorithms which are inspired by the behavior of nature are massively used to perform optimization. Wireless Sensor Networks (WSN) are playing vital role in all sectors. Some crucial issues of WSN are clustering, optimal routing, dynamic allocation of motes, energy and lifetime optimization. Researchers are working for several years to resolve issues of WSN for better quality of service. Bio-inspired algorithms like Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) are playing important role in solving the issues of WSN. Still some algorithms are insufficiently studied. Bio-inspired computing is gradually gaining interest from researchers for its intelligence and adaptive nature. Although these algorithms have perceived a lot of attention from researchers in current years, the domain-specific understanding still needs to be improved for its establishment. In this chapter bio-inspired algorithms are discussed concisely with their importance in the field of wireless sensor networks.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Das SK, Samanta S, Dey N, Kumar R (2020) Design frameworks for wireless networks. In: Lecture notes in networks and systems. Springer, pp 1–439. ISBN: 978-981-13-9573-4

    Google Scholar 

  2. Mukherjee A, Dey N, Kausar N, Ashour AS, Taiar R, Hassanien AE (2019) A disaster management specific mobility model for flying ad-hoc network. In: Emergency and disaster management: concepts, methodologies, tools, and applications. IGI Global, pp 279–311

    Google Scholar 

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

    Article  Google Scholar 

  4. Das SK, Tripathi S (2017) Energy efficient routing formation technique for hybrid ad hoc network using fusion of artificial intelligence techniques. Int J Commun Syst 30(16):e3340

    Article  Google Scholar 

  5. Dey N, Ashour AS, Shi F, Fong SJ, Sherratt RS (2017) Developing residential wireless sensor networks for ECG healthcare monitoring. IEEE Trans Consum Electron 63(4):442–449

    Article  Google Scholar 

  6. Darwish A (2018) Bio-inspired computing: algorithms review, deep analysis, and the scope of applications. Future Comput Inf J 3(2):231–246

    Article  MathSciNet  Google Scholar 

  7. Kar AK (2016) Bio inspired computing–a review of algorithms and scope of applications. Expert Syst Appl 59:20–32

    Article  Google Scholar 

  8. Del Ser J, Osaba E, Molina D, Yang XS, Salcedo-Sanz S, Camacho D, Das S, Suganthan PN, Coello CAC, Herrera F (2019) Bio-inspired computation: where we stand and what’s next. Swarm Evol Comput 48:220–250

    Article  Google Scholar 

  9. Dorigo M, Birattari M (2010) Ant colony optimization. Springer, US, pp 36–39

    Google Scholar 

  10. Mohan BC, Baskaran R (2012) A survey: ant colony optimization based recent research and implementation on several engineering domain. Expert Syst Appl 39(4):4618–4627

    Article  Google Scholar 

  11. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyesuniversity, engineering faculty, computer engineering department, vol 200, pp 1–10

    Google Scholar 

  12. Yue Y, Cao L, Luo Z (2019) Hybrid artificial bee colony algorithm for improving the coverage and connectivity of wireless sensor networks. Wirel Pers Commun 1–14

    Google Scholar 

  13. Lu Y, Sun N, Pan X (2019) Mobile sink-based path optimization strategy in wireless sensor networks using artificial bee colony algorithm. IEEE Access 7:11668–11678

    Article  Google Scholar 

  14. Mann PS, Singh S (2019) Improved artificial bee colony metaheuristic for energy-efficient clustering in wireless sensor networks. Artif Intell Rev 51(3):329–354

    Article  Google Scholar 

  15. Saad E, Elhosseini M, Haikal AY (2019) Culture-based Artificial Bee Colony with heritage mechanism for optimization of wireless sensors network. Appl Soft Comput

    Google Scholar 

  16. Zhang X, Zhang X, Han L (2019) An energy efficient internet of things network using restart artificial bee colony and wireless power transfer. IEEE Access 7:12686–12695

    Article  Google Scholar 

  17. Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin, Heidelberg, pp 65–74

    Chapter  Google Scholar 

  18. Menad H, Amine A (2018) Bio-inspired algorithms for medical data analysis. In: Handbook of research on biomimicry in information retrieval and knowledge management. IGI Global, pp 251–275

    Google Scholar 

  19. Hong WC, Li MW, Geng J, Zhang Y (2019) Novel chaotic bat algorithm for forecasting complex motion of floating platforms. Appl Math Model

    Google Scholar 

  20. Osaba E, Yang XS, FisterJr I, Del Ser J, Lopez-Garcia P, Vazquez-Pardavila AJ (2019) A discrete and improved bat algorithm for solving a medical goods distribution problem with pharmacological waste collection. Swarm Evol Comput 44:273–286

    Article  Google Scholar 

  21. Ng CK, Wu CH, Ip WH, Yung KL (2018) A smart bat algorithm for wireless sensor network deployment in 3-D environment. IEEE Commun Lett 22(10):2120–2123

    Article  Google Scholar 

  22. Lyu S, Li Z, Huang Y, Wang J, Hu J (2019) Improved self-adaptive bat algorithm with step-control and mutation mechanisms. J Comput Sci 30:65–78

    Article  MathSciNet  Google Scholar 

  23. Sharma S, Verma S, Jyoti K (2019) A new bat algorithm with distance computation capability and its applicability in routing for WSN. In: Soft computing and signal processing. Springer, Singapore, pp 163–171

    Google Scholar 

  24. Cui Z, Cao Y, Cai X, Cai J, Chen J (2018) Optimal LEACH protocol with modified bat algorithm for big data sensing systems in Internet of Things. J Parallel Distrib Comput

    Google Scholar 

  25. Gandomi AH, Yang XS, Alavi AH, Talatahari S (2013) Bat algorithm for constrained optimization tasks. Neural Comput Appl 22(6):1239–1255

    Article  Google Scholar 

  26. Yang XS (2013) Bat algorithm: literature review and applications. arXiv preprint arXiv:1308.3900

  27. Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713

    Article  Google Scholar 

  28. Gupta GP, Jha S (2018) Biogeography-based optimization scheme for solving the coverage and connected node placement problem for wireless sensor networks. Wirel Netw 1–11

    Google Scholar 

  29. Lalwani P, Banka H, Kumar C (2018) BERA: a biogeography-based energy saving routing architecture for wireless sensor networks. Soft Comput 22(5):1651–1667

    Article  Google Scholar 

  30. Elhoseny M, Tharwat A, Yuan X, Hassanien AE (2018) Optimizing K-coverage of mobile WSNs. Expert Syst Appl 92:142–153

    Article  Google Scholar 

  31. Kaushik A, Indu S, Gupta D (2018) Optimizing and enhancing the lifetime of a wireless sensor network using biogeography based optimization. International conference on application of computing and communication technologies. Springer, Singapore, pp 260–272

    Chapter  Google Scholar 

  32. Senniappan V, Subramanian J (2018) Biogeography-Based Krill Herd algorithm for energy efficient clustering in wireless sensor networks for structural health monitoring application. J Ambient Intell Smart Environ 10(1):83–93

    Article  Google Scholar 

  33. Chu SC, Tsai PW, Pan JS (2006) Cat swarm optimization. In: Pacific Rim international conference on artificial intelligence. Springer, Berlin, Heidelberg, pp 854–858

    Google Scholar 

  34. Tsai PW, Pan JS, Chen SM, Liao BY (2012) Enhanced parallel cat swarm optimization based on the Taguchi method. Expert Syst Appl 39(7):6309–6319

    Article  Google Scholar 

  35. Temel S, Unaldi N, Kaynak O (2013) On deployment of wireless sensors on 3-D terrains to maximize sensing coverage by utilizing cat swarm optimization with wavelet transform. IEEE Trans Syst Man Cybern: Syst 44(1):111–120

    Article  Google Scholar 

  36. Kong L, Chen CM, Shih HC, Lin, CW, He BZ, Pan JS (2014) An energy-aware routing protocol using cat swarm optimization for wireless sensor networks. In: Advanced technologies, embedded and multimedia for human-centric computing. Springer, Dordrecht, pp 311–318

    Google Scholar 

  37. Kong L, Pan JS, Tsai PW, Vaclav S, Ho JH (2015) A balanced power consumption algorithm based on enhanced parallel cat swarm optimization for wireless sensor network. Int J Distrib Sens Netw 11(3):729680

    Article  Google Scholar 

  38. Soto R, Crawford B, Aste Toledo A, Castro C, Paredes F, Olivares R (2019) Solving the manufacturing cell design problem through binary cat swarm optimization with dynamic mixture ratios. Comput Intell Neurosci

    Google Scholar 

  39. Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: 2009 world congress on nature & biologically inspired computing (NaBIC). IEEE, pp 210–214

    Google Scholar 

  40. Ghosh A, Chakraborty N (2019) Cascaded cuckoo search optimization of router placement in signal attenuation minimization for a wireless sensor network in an indoor environment. Eng Optim 1–20

    Google Scholar 

  41. Yu X, Hu M (2019) Hop-count quantization ranging and hybrid cuckoo search optimized for DV-HOP in WSNs. Wirel Pers Commun 1–16

    Google Scholar 

  42. Meng X, Chang J, Wang X, Wang Y (2019) Multi-objective hydropower station operation using an improved cuckoo search algorithm. Energy 168:425–439

    Article  Google Scholar 

  43. Chi R, Su YX, Zhang DH, Chi XX, Zhang HJ (2019) A hybridization of cuckoo search and particle swarm optimization for solving optimization problems. Neural Comput Appl 31(1):653–670

    Article  Google Scholar 

  44. Wu Z, Zhao X, Ma Y, Zhao X (2019) A hybrid model based on modified multi-objective cuckoo search algorithm for short-term load forecasting. Appl Energy 237:896–909

    Article  Google Scholar 

  45. Shehab M, Khader AT, Al-Betar MA (2017) A survey on applications and variants of the cuckoo search algorithm. Appl Soft Comput 61:1041–1059

    Article  Google Scholar 

  46. Binh HTT, Hanh NT, Dey N (2018) Improved cuckoo search and chaotic flower pollination optimization algorithm for maximizing area coverage in wireless sensor networks. Neural Comput Appl 30(7):2305–2317

    Article  Google Scholar 

  47. Meng X, Liu Y, Gao X, Zhang H (2014) A new bio-inspired algorithm: chicken swarm optimization. In: International conference in swarm intelligence. Springer, Cham, pp 86–94

    Google Scholar 

  48. Yu X, Zhou L, Li X (2019) A novel hybrid localization scheme for deep mine based on wheel graph and chicken swarm optimization. Comput Netw 154:73–78

    Article  Google Scholar 

  49. Deb S, Gao XZ, Tammi K, Kalita K, Mahanta P (2019) Recent studies on chicken swarm optimization algorithm: a review (2014–2018). Artif Intell Rev 1–29

    Google Scholar 

  50. Al Shayokh M, Shin SY (2017) Bio inspired distributed WSN localization based on chicken swarm optimization. Wirel Pers Commun 97(4):5691–5706

    Article  Google Scholar 

  51. Aziz A, Singh K, Osamy W, Khedr AM (2019) Effective algorithm for optimizing compressive sensing in IoT and periodic monitoring applications. J Netw Comput Appl 126:12–28

    Article  Google Scholar 

  52. Movva P, Rao PT (2019) Novel two-fold data aggregation and MAC scheduling to support energy efficient routing in wireless sensor network. IEEE Access 7:1260–1274

    Article  Google Scholar 

  53. Wang GG, Deb S, Coelho LDS (2015) Elephant herding optimization. In: 2015 3rd international symposium on computational and business intelligence (ISCBI). IEEE, 1–5

    Google Scholar 

  54. Strumberger I, Beko M, Tuba M, Minovic M, Bacanin N (2018) Elephant herding optimization algorithm for wireless sensor network localization problem. In: technological innovation for resilient systems: 9th IFIP WG 5.5/SOCOLNET advanced doctoral conference on computing, electrical and industrial systems, DoCEIS 2018, Costa de Caparica, Portugal, May 2–4, 2018, Proceedings 9. Springer International Publishing, pp 175–184

    Google Scholar 

  55. Correia S, Beko M, da Silva Cruz L, Tomic S (2018) Elephant herding optimization for energy-based localization. Sensors 18(9):2849

    Google Scholar 

  56. Strumberger I, Minovic M, Tuba M, Bacanin N (2019) Performance of elephant herding optimization and tree growth algorithm adapted for node localization in wireless sensor networks. Sensors 19(11):2515

    Article  Google Scholar 

  57. Tuba E, Dolicanin-Djekic D, Jovanovic R, Simian D, Tuba M (2019) Combined elephant herding optimization algorithm with K-means for data clustering. In: Information and communication technology for intelligent systems. Springer, Singapore, pp 665–673

    Google Scholar 

  58. Li J, Guo L, Li Y, Liu C (2019) Enhancing elephant herding optimization with novel individual updating strategies for large-scale optimization problems. Mathematics 7(5):395

    Article  Google Scholar 

  59. Li XL (2002) An optimizing method based on autonomous animats: fish-swarm algorithm. Syst Eng-Theory Pract 22(11):32–38

    Google Scholar 

  60. Neshat M, Sepidnam G, Sargolzaei M, Toosi AN (2014) Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artif Intell Rev 42(4):965–997

    Article  Google Scholar 

  61. Zheng ZX, Li JQ, Duan PY (2019) Optimal chiller loading by improved artificial fish swarm algorithm for energy saving. Math Comput Simul 155:227–243

    Article  MathSciNet  Google Scholar 

  62. Qin N, Xu J (2018) An adaptive fish swarm-based mobile coverage in WSNs. Wirel Commun Mob Comput

    Google Scholar 

  63. Li X, Keegan B, Mtenzi F (2018) energy efficient hybrid routing protocol based on the artificial fish swarm algorithm and ant colony optimisation for WSNs. Sensors 18(10):3351

    Article  Google Scholar 

  64. Yin H, Zhang Y, He X (2018) WSN nodes placement optimization based on a weighted centroid artificial fish swarm algorithm. Algorithms 11(10):147

    Article  MATH  Google Scholar 

  65. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  66. Kohli M, Arora S (2018) Chaotic grey wolf optimization algorithm for constrained optimization problems. J Comput Des Eng 5(4):458–472

    Google Scholar 

  67. Krishnanand KN, Ghose D (2005) Detection of multiple source locations using a glowworm metaphor with applications to collective robotics. In: Proceedings 2005 IEEE swarm intelligence symposium, 2005. SIS 2005, IEEE. pp 84–91

    Google Scholar 

  68. Krishnanand KN, Ghose D (2009) Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions. Swarm Intell 3(2):87–124

    Article  Google Scholar 

  69. Krishnanand KN, Ghose D (2008) Theoretical foundations for rendezvous of glowworm-inspired agent swarms at multiple locations. Robot Auton Syst 56(7):549–569

    Article  Google Scholar 

  70. Liao WH, Kao Y, Li YS (2011) A sensor deployment approach using glowworm swarm optimization algorithm in wireless sensor networks. Expert Syst Appl 38(10):12180–12188

    Article  Google Scholar 

  71. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249

    Article  Google Scholar 

  72. Zhao H, Zhao H, Guo S (2016) Using GM (1, 1) optimized by MFO with rolling mechanism to forecast the electricity consumption of inner mongolia. Appl Sci 6(1):20

    Article  Google Scholar 

  73. Khalilpourazari S, Pasandideh SHR (2017) Multi-item EOQ model with nonlinear unit holding cost and partial backordering: moth-flame optimization algorithm. J Ind Prod Eng 34(1):42–51

    Google Scholar 

  74. Kennedy J, Eberhart R (1995) Particle swarm optimization (PSO). In: Proceeding IEEE international conference on neural networks, Perth, Australia, pp 1942–1948

    Google Scholar 

  75. Poli R (2008) Analysis of the publications on the applications of particle swarm optimisation. J Artif Evol Appl

    Google Scholar 

  76. 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 

  77. Wachowiak MP, Smolíková R, Zheng Y, Zurada JM, Elmaghraby AS (2004) An approach to multimodal biomedical image registration utilizing particle swarm optimization. IEEE Trans Evol Comput 8(3):289–301

    Article  Google Scholar 

  78. Yeh WC, Chang WW, Chung YY (2009) A new hybrid approach for mining breast cancer pattern using discrete particle swarm optimization and statistical method. Expert Syst Appl 36(4):8204–8211

    Article  Google Scholar 

  79. Muthukaruppan S, Er MJ (2012) A hybrid particle swarm optimization based fuzzy expert system for the diagnosis of coronary artery disease. Expert Syst Appl 39(14):11657–11665

    Article  Google Scholar 

  80. Jordehi AR (2019) Binary particle swarm optimisation with quadratic transfer function: a new binary optimisation algorithm for optimal scheduling of appliances in smart homes. Appl Soft Comput

    Google Scholar 

  81. Nouiri M, Bekrar A, Jemai A, Niar S, Ammari AC (2018) An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem. J Intell Manuf 29(3):603–615

    Article  Google Scholar 

  82. Lynn N, Ali MZ, Suganthan PN (2018) Population topologies for particle swarm optimization and differential evolution. Swarm Evol Computation 39:24–35

    Article  Google Scholar 

  83. Aydoğan EK, Delice Y, Özcan U, Gencer C, Bali Ö (2019) Balancing stochastic U-lines using particle swarm optimization. J Intell Manuf 30(1):97–111

    Article  Google Scholar 

  84. Tam NT, Hai DT (2018) Improving lifetime and network connections of 3D wireless sensor networks based on fuzzy clustering and particle swarm optimization. Wirel Netw 24(5):1477–1490

    Article  Google Scholar 

  85. Vijayalakshmi K, Anandan P (2018) A multi objective Tabu particle swarm optimization for effective cluster head selection in WSN. Cluster Comput 1–8

    Google Scholar 

  86. Kaur T, Kumar D (2018) Particle swarm optimization-based unequal and fault tolerant clustering protocol for wireless sensor networks. IEEE Sens J 18(11):4614–4622

    Article  Google Scholar 

  87. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  88. El Aziz MA, Ewees AA, Hassanien AE (2017) Whale optimization algorithm and moth-flame optimization for multilevel thresholding image segmentation. Expert Syst Appl 83:242–256

    Article  Google Scholar 

  89. Nasiri J, Khiyabani FM (2018) A whale optimization algorithm (WOA) approach for clustering. Cogent Math Stat 5(1):1483565

    Article  MathSciNet  MATH  Google Scholar 

  90. Ahmed MM, Houssein EH, Hassanien, AE, Taha A, Hassanien E (2019) Maximizing lifetime of large-scale wireless sensor networks using multi-objective whale optimization algorithm. Telecommun Syst 1–17

    Google Scholar 

  91. Valayapalayam Kittusamy SR, Elhoseny M, Kathiresan S (2019) An enhanced whale optimization algorithm for vehicular communication networks. Int J Commun Syst p.e3953

    Google Scholar 

  92. Hassan MK, El Desouky AI, Elghamrawy SM, Sarhan AM (2019) A Hybrid Real-time remote monitoring framework with NB-WOA algorithm for patients with chronic diseases. Future Gener Comput Syst 93:77–95

    Article  Google Scholar 

  93. Verma GK, Ranga V (2018) Whale optimizer to repair partitioned heterogeneous wireless sensor networks. Int J Grid Distrib Comput 11(5):11–28

    Article  Google Scholar 

  94. Yang XS (2014) Nature-inspired optimization algorithms. Elsevier

    Google Scholar 

  95. Parvin H, Moradi P, Esmaeili S (2019) TCFACO: trust-aware collaborative filtering method based on ant colony optimization. Expert Syst Appl 118:152–168

    Article  Google Scholar 

  96. Li Y, Soleimani H, Zohal M (2019) An improved ant colony optimization algorithm for the multi-depot green vehicle routing problem with multiple objectives. J Cleaner Prod

    Google Scholar 

  97. Sun Z, Wei M, Zhang Z, Qu G (2019) Secure routing protocol based on multi-objective ant-colony-optimization for wireless sensor networks. Appl Soft Comput 77:366–375

    Article  Google Scholar 

  98. Wang J, Cao J, Sherratt RS, Park JH (2018) An improved ant colony optimization-based approach with mobile sink for wireless sensor networks. J Supercomputing 74(12):6633–6645

    Article  Google Scholar 

  99. Guleria K, Verma AK (2019) Meta-heuristic Ant Colony optimization based unequal clustering for wireless sensor network. Wirel Pers Commun 105(3):891–911

    Article  Google Scholar 

  100. Ghosh N, Banerjee I, Sherratt RS (2019) On-demand fuzzy clustering and ant-colony optimisation based mobile data collection in wireless sensor network. Wirel Netw 25(4):1829–1845

    Article  Google Scholar 

  101. Dahan F, El Hindi K, Mathkour H, AlSalman H (2019) Dynamic flying ant colony optimization (DFACO) for solving the traveling salesman problem. Sensors 19(8):1837

    Article  Google Scholar 

  102. Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2014) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 42(1):21–57

    Article  Google Scholar 

  103. Giveki D, Salimi H, Bahmanyar G, Khademian Y (2012) Automatic detection of diabetes diagnosis using feature weighted support vector machines based on mutual information and modified cuckoo search. arXiv preprint arXiv:1201.2173

  104. Ashour AS, Samanta S, Dey N, Kausar N, Abdessalemkaraa WB, Hassanien AE (2015) Computed tomography image enhancement using cuckoo search: a log transform based approach. J Signal Inform Process 6(03):244

    Article  Google Scholar 

  105. Wang GG, Deb S, Gao XZ, Coelho LDS (2016) A new metaheuristic optimisation algorithm motivated by elephant herding behaviour. Int J Bio-Inspired Comput 8(6):394–409

    Article  Google Scholar 

  106. Kaushik A, Indu S, Gupta D (2019) A grey wolf optimization approach for improving the performance of wireless sensor networks. Wirel Pers Commun 1–21

    Google Scholar 

  107. Kaushik A, Indu S, Gupta D (2019) A grey wolf optimization based algorithm for optimum camera placement. Wirel Pers Commun 1–25

    Google Scholar 

  108. Zapotecas-Martínez S, García-Nájera A, López-Jaimes A (2019) Multi-objective grey wolf optimizer based on decomposition. Expert Syst Appl 120:357–371

    Article  Google Scholar 

  109. Tu Q, Chen X, Liu X (2019) Multi-strategy ensemble grey wolf optimizer and its application to feature selection. Appl Soft Comput 76:16–30

    Article  Google Scholar 

  110. Mirjalili S, Saremi S, Mirjalili SM, Coelho LDS (2016) Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Syst Appl 47:106–119

    Article  Google Scholar 

  111. Ray A, De D (2016) An energy efficient sensor movement approach using multi-parameter reverse glowworm swarm optimization algorithm in mobile wireless sensor network. Simul Model Pract Theory 62:117–136

    Article  Google Scholar 

  112. Wang Y, Cui Z, Li W (2019) A novel coupling algorithm based on glowworm swarm optimization and bacterial foraging algorithm for solving multi-objective optimization problems. Algorithms 12(3):61

    Article  MathSciNet  MATH  Google Scholar 

  113. Salkuti SR, Kim SC (2019) Congestion management using multi-objective glowworm swarm optimization algorithm. J Electr Eng Technol 1–11

    Google Scholar 

  114. Song L, Zhao L, Ye J (2019) DV-hop node location algorithm based on GSO in wireless sensor networks. J Sens

    Google Scholar 

  115. Antoniou P, Pitsillides A, Blackwell T, Engelbrecht A, Michael L (2013) Congestion control in wireless sensor networks based on bird flocking behavior. Comput Netw 57(5):1167–1191

    Article  Google Scholar 

  116. Bharathi MA, Mallikarjuna M, VijayaKumar BP (2012) Bio-inspired approach for energy utilization in wireless sensor networks. Procedia Eng 38:3864–3868

    Article  Google Scholar 

  117. Saleem M, Ullah I, Farooq M (2012) BeeSensor: an energy-efficient and scalable routing protocol for wireless sensor networks. Inf Sci 200:38–56

    Article  Google Scholar 

  118. Miloud M, Abdellatif R, Lorenz P (2019) Moth flame optimization algorithm range-based for node localization challenge in decentralized wireless sensor network. Int J Distrib Syst Technol (IJDST) 10(1):82–109

    Article  Google Scholar 

  119. Mittal N (2019) Moth flame optimization based energy efficient stable clustered routing approach for wireless sensor networks. Wirel Pers Commun 104(2):677–694

    Article  Google Scholar 

  120. Khan MF, Aadil F, Maqsood M, Bukhari SHR, Hussain M, Nam Y (2019) Moth flame clustering algorithm for internet of vehicle (MFCA-IoV). IEEE Access 7:11613–11629

    Article  Google Scholar 

  121. Sapre S, Mini S (2018) Moth flame based optimized placement of relay nodes for fault tolerant wireless sensor networks. In: 2018 9th international conference on computing, communication and networking technologies (ICCCNT), IEEE. pp 1–6

    Google Scholar 

  122. Ray A, De D (2016) Energy efficient clustering protocol based on K-means (EECPK-means)-midpoint algorithm for enhanced network lifetime in wireless sensor network. IET Wirel Sensor Syst 6(6):181–191

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Sarojini Naidu College for Women, 30 Jessore Road, Kolkata, 700028, West Bengal, India

    Anindita Raychaudhuri

  2. Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Haringhata, Nadia, 721249, West Bengal, India

    Debashis De

Authors
  1. Anindita Raychaudhuri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Debashis De
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anindita Raychaudhuri .

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

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Raychaudhuri, A., De, D. (2020). Bio-inspired Algorithm for Multi-objective Optimization in Wireless Sensor Network. 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_12

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-2125-6_12

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-2124-9

  • Online ISBN: 978-981-15-2125-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation