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Artificial Intelligence Based Optimization Techniques: A Review

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Intelligent Computing Techniques for Smart Energy Systems

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 607))

Abstract

Many artificial intelligence based optimization techniques have been introduced since the early 60s. This paper provides a brief review of some of the well-known optimization techniques, e.g., Genetic Algorithm, Particle Swarm Algorithm, and Ant Colony Optimization and recently developed techniques, e.g., BAT Algorithm and Elephant Herding Optimization. All these techniques are population-based search algorithms, in which the initial population is created randomly initializing input parameters within the specified range. They approach toward the best solution inspired by the behavior of natural entities. All of these techniques have a potential to provide optimal or near-optimal solutions.

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References

  1. Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm intelligence: from natural to artificial systems. J Artif Soc Soc Simul 4

    Google Scholar 

  2. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Michigan

    Google Scholar 

  3. Jong KD (1975) An analysis of the behavior of a class of genetic adaptive systems, Doctoral Dissertation. The University of Michigan, Ann Arbor

    Google Scholar 

  4. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley

    Google Scholar 

  5. Muselli M, Ridella S (1992) Global optimization of functions with the interval genetic algorithm. Complex Syst 6:193–212

    MathSciNet  MATH  Google Scholar 

  6. Lobo FG, Goldberg DE (1997) Decision making in a hybrid genetic algorithm. In: IEEE international conference on evolutionary computation. IEEE Press, Piscataway, pp 122–125

    Google Scholar 

  7. Lin WM, Cheng FS, Tsay MT (2000) Distribution feeder reconfiguration with refined genetic algorithm. IEE Gener Transm Distrib 147(6):349–354

    Article  Google Scholar 

  8. Zhu JZ (2002) Optimal reconfiguration of electrical distribution network using the refined genetic algorithm. Electr Power Syst Res 62(1):37–42

    Article  Google Scholar 

  9. Huang YC (2002) Enhanced genetic algorithm-based fuzzy multi-objective approach to distribution network reconfiguration. IEE Gener Transm Distrib 149(5):615–620

    Article  Google Scholar 

  10. Mendoza JE, Lopez ME, Coello CA, Lopez EA (2009) Microgenetic multiobjective reconfiguration algorithm considering power losses and reliability indices for medium voltage distribution network. IET Gener Transm Distrib 3(9):825–840

    Article  Google Scholar 

  11. Gupta N, Swarnkar A, Niazi KR, Bansal RC (2010) Multi-objective reconfiguration of distribution systems using adaptive genetic algorithm in fuzzy framework. IET Gener Transm Distrib 4(12):1288–1298

    Article  Google Scholar 

  12. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: IEEE international conference on neural networks, vol 4. Piscataway, pp 1942–1948

    Google Scholar 

  13. Shi Y, Eberhart RC (1998) A modified particle swarm optimizer. In: IEEE computation intelligence world congress, pp 69–73

    Google Scholar 

  14. Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: IEEE congress on evolutionary computation. IEEE Press, Piscataway, pp 1945–1950

    Google Scholar 

  15. Sivanagaraju S, Rao JV, Raju PS (2008) Discrete particle swarm optimization to network reconfiguration for loss reduction and load balancing. Electr Power Comp Syst 36(5):513–524

    Article  Google Scholar 

  16. Niknam T, Farsani EA (2010) A hybrid self-adaptive particle swarm optimization and modified shuffled frog leaping algorithm for distribution feeder reconfiguration. Eng Appl Artif Intell 23(8):1340–1349

    Article  Google Scholar 

  17. Wu WC, Tsai MS (2011) Application of enhanced integer coded particle swarm optimization for distribution system feeder reconfiguration. IEEE Trans Power Syst 26(3)

    Google Scholar 

  18. Gupta N, Swarnkar A, Niazi KR (2011) Reconfiguration of distribution systems for real power loss minimization using adaptive particle swarm optimization. Electr Power Comp Syst 39(4):317–330

    Article  Google Scholar 

  19. Dorigo M (1992) Optimization, learning and natural algorithms. Ph.D. thesis, Politechnico di Milano, Italy

    Google Scholar 

  20. Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning to the travelling salesman problem. IEEE Trans Evol Comput 1(1):53–66

    Article  Google Scholar 

  21. Stutzle T, Hoos HH (2000) Max–Min ant system. Future Gener Comput Syst 16(8):889–914

    Article  Google Scholar 

  22. Blum C, Dorigo M (2004) The hyper-cube framework for ant colony optimization. IEEE Trans Syst Man Cybern B Cybern 34(2):1161–1172

    Article  Google Scholar 

  23. Das S, Gosavi SV, Hsu WH, Vaze SA (2002) An ant colony approach for the steiner tree problem. In: Genetic and evolutionary computing conference, Morgan Kaufmann, pp 9–13

    Google Scholar 

  24. Su CT, Chang CF, Chiou JP (2005) Distribution network reconfiguration for loss reduction by ant colony search algorithm. Electr Power Syst Res 75(2–3):190–199

    Article  Google Scholar 

  25. Ahuja A, Das S, Pahwa A (2007) An AIS-ACO hybrid approach for multi-objective distribution system reconfiguration. IEEE Trans Power Syst 22(3):1101–1111

    Article  Google Scholar 

  26. Chang CF (2008) Reconfiguration and capacitor placement for loss reduction of distribution systems by ant colony search algorithm. IEEE Trans Power Syst 23(4):1747–1755

    Article  Google Scholar 

  27. Carpaneto E, Chicco G (2008) Distribution system minimum loss reconfiguration in the hyper-cube ant colony optimization framework. Electr Power Syst Res 78(12):2037–2045

    Article  Google Scholar 

  28. Swarnkar A, Gupta N, Niazi KR (2011) Adapted ant colony optimization for efficient reconfiguration of balanced and unbalanced distribution systems for loss minimization. Swarm Evol Comput (Elsevier) 1(3):129–137

    Article  Google Scholar 

  29. Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NISCO 2010), studies in computational intelligence, vol 284. Springer, Berlin, pp 65–74

    Google Scholar 

  30. Yang XS (2011) Bat algorithm for multi-objective optimization. Int J Bio-Inspir Comput 3(5):267–274

    Article  Google Scholar 

  31. Kanwar N, Gupta N, Niazi KR, Swarnkar A, Bansal RC (2015) Multi-objective optimal DG allocation in distribution networks using bat algorithm. In: Third southern african solar energy conference-2015. Kruger National Park, South Africa

    Google Scholar 

  32. Wang GG, Deb S, Coelho LS (2015) Elephant herding optimization. In: 3rd international symposium on computational and business intelligence, Bali, Indonesia, pp 1–5

    Google Scholar 

  33. Meena NK, Parashar S, Swarnkar A, Gupta N, Niazi KR (2018) Improved elephant herding optimization for multiobjective DER accommodation in distribution systems. IEEE Trans Industr Inf 14(3):1029–1039

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science Engineering, Manipal University Jaipur, Jaipur, 303007, India

    Agrani Swarnkar

  2. Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, India

    Anil Swarnkar

Authors
  1. Agrani Swarnkar
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  2. Anil Swarnkar
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Corresponding author

Correspondence to Agrani Swarnkar .

Editor information

Editors and Affiliations

  1. College of Engineering and Science, Victoria University, Footscray, VIC, Australia

    Akhtar Kalam

  2. Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Khaleequr Rehman Niazi

  3. Manipal University Jaipur, Jaipur, Rajasthan, India

    Amit Soni

  4. Manipal University Jaipur, Jaipur, Rajasthan, India

    Shahbaz Ahmed Siddiqui

  5. Department of Information Technology, Manipal University Jaipur, Jaipur, Rajasthan, India

    Ankit Mundra

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Swarnkar, A., Swarnkar, A. (2020). Artificial Intelligence Based Optimization Techniques: A Review. In: Kalam, A., Niazi, K., Soni, A., Siddiqui, S., Mundra, A. (eds) Intelligent Computing Techniques for Smart Energy Systems. Lecture Notes in Electrical Engineering, vol 607. Springer, Singapore. https://doi.org/10.1007/978-981-15-0214-9_12

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  • DOI: https://doi.org/10.1007/978-981-15-0214-9_12

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0213-2

  • Online ISBN: 978-981-15-0214-9

  • eBook Packages: EngineeringEngineering (R0)

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