Skip to main content
SpringerLink
Log in

Multi-objective Optimization Algorithms

  • Chapter
  • First Online:
Large-Scale Integrated Energy Systems

Part of the book series: Energy Systems in Electrical Engineering ((ESIEE))

  • 382 Accesses

Abstract

In the LSIES, multiple benefits of different operating interests are taken into consideration. Hence, the planning and operation of LSIES are formulated as multi-objective optimization problems, which should be tackled using the multi-objective optimization algorithms. This chapter presents three multi-objective optimization algorithms, i.e., the multi-objective group search optimizer with adaptive covariance and Lévy flights (MGSO-ACL), multi-objective group search optimizer with adaptive covariance and chaotic search (MGSOACC), and multi-objective evolutionary predator and prey strategy (EPPS). Simulation studies conducted on benchmark functions are also carried out to investigate the performance of these algorithms. In later chapters, these algorithms are employed to deal with the planning and operating problems of LSIES.

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 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 129.99
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

References

  • Aragón V, Esquivel S, Coello CC (2015) An immune algorithm with power redistribution for solving economic dispatch problems. Inf Sci 295:609–632

    Article  MathSciNet  Google Scholar 

  • Auger A, Hansen N (2012) Tutorial CMA-ES: evolution strategies and covariance matrix adaptation. In: GECCO (Companion), pp 827–848

    Google Scholar 

  • Basu M (2008) Dynamic economic emission dispatch using nondominated sorting genetic algorithm-II. Int J Electric Power Energy Syst 30(2):140–149

    Article  Google Scholar 

  • Basu M (2011) Economic environmental dispatch using multi-objective differential evolution. Appl Soft Comput 11(2):2845–2853

    Article  Google Scholar 

  • Chakraborty S, Ito T, Senjyu T, Saber AY (2012) Unit commitment strategy of thermal generators by using advanced fuzzy controlled binary particle swarm optimization algorithm. Int J Electric Power Energy Syst 43(1):1072–1080

    Article  Google Scholar 

  • Chen WN, Zhang J, Lin Y, Chen N, Zhan ZH, Chung HSH, Li Y, Shi YH (2013) Particle swarm optimization with an aging leader and challengers. IEEE Trans Evol Comput 17(2):241–258

    Article  Google Scholar 

  • Chung C, Yu H, Wong KP (2011) An advanced quantum-inspired evolutionary algorithm for unit commitment. IEEE Trans Power Syst 26(2):847–854

    Article  Google Scholar 

  • Civicioglu P (2012) Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Comput Geosci 46:229–247

    Article  Google Scholar 

  • Civicioglu P (2013a) Artificial cooperative search algorithm for numerical optimization problems. Inf Sci 229:58–76

    Article  MATH  Google Scholar 

  • Civicioglu P (2013b) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219(15):8121–8144

    Article  MathSciNet  MATH  Google Scholar 

  • Civicioglu P, Besdok E (2013) A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artif Intell Rev 39(4):315–346

    Article  Google Scholar 

  • Conover WJ, Conover W (1980) Practical Nonparametric Statistics. Wiley, New York

    MATH  Google Scholar 

  • Das S, Suganthan P (2010) Problem definitions and evaluation criteria for CEC 2011 competition on testing evolutionary algorithms on real world optimization problems. Jadavpur University, Nanyang Technological University, Kolkata, India

    Google Scholar 

  • de Athayde Costa e Silva M, Klein CE, Mariani VC, dos Santos Coelho L (2013) Multiobjective scatter search approach with new combination scheme applied to solve environmental/economic dispatch problem. Energy 53(0):14–21

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Deb K, Saxena DK (2005) On finding pareto-optimal solutions through dimensionality reduction for certain large-dimensional multi-objective optimization problems. Kangal report 2005011

    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 Evol Comput 1(1):3–18

    Article  Google Scholar 

  • Dixon AFG (1959) An experimental study of the searching behaviour of the predatory coccinellid beetle Adalia decempunctata. J Animal Ecol 28(2):259–281

    Article  Google Scholar 

  • Durillo JJ, Nebro AJ, Coello Coello CA, Garcia-Nieto J, Luna F, Alba E (2010) A study of multiobjective metaheuristics when solving parameter scalable problems. IEEE Trans Evol Comput 14(4):618–635

    Article  MATH  Google Scholar 

  • Gent MR, Lamont JW (1971) Minimum emission dispatch. IEEE Trans Power Appar Syst 90(6):2650–2660

    Article  Google Scholar 

  • Glotić A, Zamuda A (2015) Short-term combined economic and emission hydrothermal optimization by surrogate differential evolution. Appl Energy 141:42–56

    Article  Google Scholar 

  • Guo CX, Zhan JP, Wu QH (2012) Dynamic economic emission dispatch based on group search optimizer with multiple producers. Electric Power Syst Res 86:8–16

    Article  Google Scholar 

  • Hansen N, Müller SD, Koumoutsakos P (2003) Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evol Comput 11(1):1–18

    Article  Google Scholar 

  • Hansen N, Ostermeier A (1996) Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation. In: 1996 Proceedings of IEEE International Conference on Evolutionary Computation. IEEE, pp 312–317

    Google Scholar 

  • 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 

  • Hota P, Barisal A, Chakrabarti R (2010) Economic emission load dispatch through fuzzy based bacterial foraging algorithm. Int J Electric Power Energy Syst 32(7):794–803

    Article  Google Scholar 

  • Jia DL, Zheng GX, Khan MK (2011) An effective memetic differential evolution algorithm based on chaotic local search. Inf Sci 181:3175–3187

    Article  Google Scholar 

  • Juste K, Kita H, Tanaka E, Hasegawa J (1999) An evolutionary programming solution to the unit commitment problem. IEEE Trans Power Syst 14(4):1452–1459

    Article  Google Scholar 

  • Kazarlis SA, Bakirtzis A, Petridis V (1996) A genetic algorithm solution to the unit commitment problem. IEEE Trans Power Syst 11(1):83–92

    Article  Google Scholar 

  • Lee C, Liu C, Mehrotra S, Shahidehpour M (2014) Modeling transmission line constraints in two-stage robust unit commitment problem. IEEE Trans Power Syst 29(3):1221–1231

    Article  Google Scholar 

  • Li C, Yang S, Nguyen TT (2012) A self-learning particle swarm optimizer for global optimization problems. IEEE Trans Syst Man Cybern Part B Cybern 42(3):627–646

    Article  Google Scholar 

  • Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295

    Article  Google Scholar 

  • Liao TJ, Stutzle T (2013) Benchmark results for a simple hybrid algorithm on the CEC 2013 benchmark set for real-parameter optimization. In: Proceedings of IEEE congress on evolutionary computation, pp 1938–1944

    Google Scholar 

  • Loshchilov I (2013) CMA-ES with restarts for solving CEC 2013 benchmark problems. In: Proceedings of IEEE congress on evolutionary computation, pp 369–376

    Google Scholar 

  • Murugan P, Kannan S, Baskar S (2009) Application of NSGA-II algorithm to single-objective transmission constrained generation expansion planning. IEEE Trans Power Syst 24(4):1790–1797

    Article  Google Scholar 

  • Mustard D (1964) Numerical integration over the n-dimensional spherical shell. Math Comput 18(88):578–589

    MathSciNet  MATH  Google Scholar 

  • Niknam T, Narimani MR, Aghaei J, Azizipanah-Abarghooee R (2012) Improved particle swarm optimisation for multi-objective optimal power flow considering the cost, loss, emission and voltage stability index. IET Gener Transm Distrib 6(6):515–527

    Article  Google Scholar 

  • O’Brien WJ, Evans BI, Howick GL (1986) A new view of the predation cycle of a planktivorous fish, white crappie (pomoxis annularis). Can J Fish Aquat Sci 43(10):1894–1899

    Article  Google Scholar 

  • Omran MGH, Clerc M (2011) Standard particle swarm optimisation, http://www.particleswarm.info/

  • Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417

    Article  Google Scholar 

  • Rao PN, Rao KP, Nanda J (1982) An e-coupled fast load flow method. In: Mahalanabis AK (ed) Theory and application of digital control, pp 601–606. Pergamon

    Google Scholar 

  • Reynolds AM, Smith AD, Reynolds DR, Carreck NL, Osborne JL (2007) Honeybees perform optimal scale-free searching flights when attempting to locate a food source. J Exp Biol 210(21):3763–3770

    Article  Google Scholar 

  • Roy PK (2013) Solution of unit commitment problem using gravitational search algorithm. Int J Electric Power Energy Syst 53:85–94

    Article  Google Scholar 

  • Saxena DK, Duro JA, Tiwari A, Deb K, Zhang Q (2013) Objective reduction in many-objective optimization: linear and nonlinear algorithms. IEEE Trans Evol Comput 17(1):77–99

    Article  Google Scholar 

  • Sayah S, Hamouda A, Bekrar A (2014) Efficient hybrid optimization approach for emission constrained economic dispatch with nonsmooth cost curves. Int J Electric Power Energy Syst 56:127–139

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Simopoulos DN, Kavatza SD, Vournas CD (2006) Unit commitment by an enhanced simulated annealing algorithm. IEEE Trans Power Syst 21(1):68–76

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  • Strogatz SH (2014) Nonlinear dynamics and chaos: with applications to physics, biology, chemistry, and engineering. Westview Press

    Google Scholar 

  • Talatahari S, Azar BF, Sheikholeslami R, Gandomi A (2012) Imperialist competitive algorithm combined with chaos for global optimization. Commun Nonlinear Sci Numer Simul 17:1312–1319

    Article  MathSciNet  MATH  Google Scholar 

  • Ting T, Rao M, Loo C (2006) A novel approach for unit commitment problem via an effective hybrid particle swarm optimization. IEEE Trans Power Syst 21(1):411–418

    Article  Google Scholar 

  • Varadarajan M, Swarup KS (2008) Solving multi-objective optimal power flow using differential evolution. IET Gener Transm Distrib 2(5):720–730

    Article  Google Scholar 

  • Venkatesh P, Gnanadass R, Padhy NP (2003) Comparison and application of evolutionary programming techniques to combined economic emission dispatch with line flow constraints. IEEE Trans Power Syst 18(2):688–697

    Article  Google Scholar 

  • Viana EM, de Oliveira EJ, Martins N, Pereira JLR, de Oliveira LW (2013) An optimal power flow function to aid restoration studies of long transmission segments. IEEE Trans Power Syst 28(1):121–129

    Article  Google Scholar 

  • Viswanathan GM, Buldyrev SV, Havlin S, Luz MGED, Raposo EP, Stanley HE (1999) Optimizing the success of random searches. Nature 401(6756):911–914

    Article  Google Scholar 

  • Wang LF, Singh CN (2008) Balancing risk and cost in fuzzy economic dispatch including wind power penetration based on particle swarm optimization. Electric Power Syst Res 78(8):1361–1368

    Article  Google Scholar 

  • Wang H, Yao X (2016) Objective reduction based on nonlinear correlation information entropy. Soft Comput 20(6):2393–2407

    Article  Google Scholar 

  • Wu QH, Lu Z, Li MS, Ji TY (2008) Optimal placement of facts devices by a group search optimizer with multiple producer. In: 2008 evolutionary computation (IEEE World Congress on Computational Intelligence), CEC 2008. IEEE (2008), pp 1033–1039

    Google Scholar 

  • Wu QH, Liao HL (2013) Function optimisation by learning automata. Inf Sci 220:379–398

    Article  MathSciNet  Google Scholar 

  • Yang XS (2010) Firefly algorithm, levy flights and global optimization. In: Research and development in intelligent systems XXVI. Springer, London, pp 209–218

    Google Scholar 

  • Yang L, Jian J, Zhu Y, Dong Z (2015) Tight relaxation method for unit commitment problem using reformulation and lift-and-project. IEEE Trans Power Syst 30:13–23

    Article  Google Scholar 

  • Zhan ZH, Zhang J, Li Y, Chung HH (2009) Adaptive particle swarm optimization. IEEE Trans Syst Man Cybern Part B Cybern 39(6):1362–1381

    Article  Google Scholar 

  • Zhao B, Guo CX, Bai BR, Cao YJ (2006) An improved particle swarm optimization algorithm for unit commitment. Int J Electric Power Energy Syst 28(7):482–490

    Article  Google Scholar 

  • Zhao C, Wang J, Watson JP, Guan Y (2013) Multi-stage robust unit commitment considering wind and demand response uncertainties. IEEE Trans Power Syst 28(3):2708–2717

    Article  Google Scholar 

  • Zheng JH, Chen JJ, Wu QH, Jing ZX (2015) Multi-objective optimization and decision making for power dispatch of a large-scale integrated energy system with distributed DHCs embedded. Appl Energy 154:369–379

    Article  Google Scholar 

  • Zheng H, Jian J, Yang L, Quan R (2015) A deterministic method for the unit commitment problem in power systems. Comput Oper Res 1:1–7

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electric Power Engineering, South China University of Technology, Guangzhou, China

    Qing-Hua Wu & Jiehui Zheng

  2. School of Electric Power Engineering, South China University of Technology, Guangzhou, China

    Zhaoxia Jing

  3. China Electric Power Research Institute, Beijing, China

    Xiaoxin Zhou

Authors
  1. Qing-Hua Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiehui Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhaoxia Jing
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaoxin Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qing-Hua Wu .

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Wu, QH., Zheng, J., Jing, Z., Zhou, X. (2019). Multi-objective Optimization Algorithms. In: Large-Scale Integrated Energy Systems. Energy Systems in Electrical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-13-6943-8_3

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-6943-8_3

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6942-1

  • Online ISBN: 978-981-13-6943-8

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics

Search

Navigation