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Chemo-Inspired GA for Non-convex Economic Load Dispatch

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Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 817))

Abstract

Powered by the chemotactic step of bacterial foraging optimization (BFO), a new hybrid genetic algorithm is proposed in this paper for solving nonlinear constrained optimization problems. In the recent past, researchers attempted to hybridize the GA and BFO for improving the quality of the solution. However, this hybridization unnecessarily increases the computational burden as some of the mechanisms/steps are seem to be technically repeated. It is due to the fact that the internal mechanism of selection in GA and the reproduction in BFO; and the elitism in GA and elimination-dispersal step in BFO is almost similar. Undoubtedly, chemotactic step plays the vital role in the better performance of BFO. Therefore in this present study, only the chemotactic step of BFO is considered for hybridization with GA. Further, it is designed to tackle constrained optimization problems and is named as chemo-inspired genetic algorithm for constrained optimization (CGAC). Here in this paper, it is applied to solve economic load dispatch (ELD) problem, and finally, the result comparison has been done with other state-of-the-art algorithms to validate the superiority of CGAC.

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References

  1. Lowery, G.: Generating unit commitment by dynamic programming. IEEE Trans. Power Appar. Syst. PAS 85(5), 422–426 (1996)

    Article  Google Scholar 

  2. Bard, J.F.: Short term scheduling of the thermal electric generators using Lagrangian relaxation. Oper. Res. 36(5), 756–766 (1988)

    Article  Google Scholar 

  3. Chen, C.L., Wang, S.C.: Branch and bound scheduling for thermal generating units. IEEE Trans. Energy Convers. 8(2), 184–189 (1993)

    Article  Google Scholar 

  4. Fan, J.I.Y., Zhang, L.: Real time economic dispatch with line flow and emission constraints using quadratic programming. IEEE Trans. power Syst. 13, 320–325 (1998)

    Article  Google Scholar 

  5. Dodu, J.C., Martin, P., Merlin, A., Pouget, J.: An optimal formulation and solution of Short range operating problems for a power system with flow constraints. Proc. IEEE 60(1), 54–63 (1972)

    Article  Google Scholar 

  6. Parikh, J., Chattopadhyay, D.: A multi area linear programming approach for analysis of economic operation of the Indian power system. IEEE Trans. Power Syst. 11, 52–58 (1996)

    Article  Google Scholar 

  7. Chiang, C.L.: Genetic based algorithm for power economic load dispatch. IEEE Proc. Gener. Trans. Distrib. 1(2), 261–269 (2007)

    Article  Google Scholar 

  8. Chaturvedi, K.T., Pandit, M., Srivastava, L.: PSO with crazy particles for non-convex economic dispatch. Appl. Soft Comput. 9, 962–969 (2009)

    Article  Google Scholar 

  9. Ghasemi, A.: A fuzzified multi objective interactive honey bee mating optimization for environmental/economic power dispatch with valve point effect. Electr. Power Syst. Res. 49, 308–321 (2013)

    Article  Google Scholar 

  10. Safari, A., Shayeghi, H.: Iteration PSO procedure for economic load dispatch with generator constraints. Expert Syst. Appl. 38, 6043–6048 (2011)

    Google Scholar 

  11. Yang, X.S., Hosseini, S.S.S., Gandomi, A.H.: Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect. Appl. Soft Comput. 12, 1180–1186 (2012)

    Article  Google Scholar 

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

    Article  Google Scholar 

  13. Mandal, B., Ray, P.K., Mandal, S.: Economic load dispatch using kill-herd algorithm. Electr. Power Syst. Res. 57, 1–10 (2014)

    Article  Google Scholar 

  14. Jiejin, C., Xiaoqian, M., Lixiang, L., Haipeng, P.: Chaotic PSO for economic dispatch considering the generator constraints. Energy Convers. Manag. 48, 645–653 (2007)

    Google Scholar 

  15. Swain, R.K., Sahu, N.C., Hota, P.K.: Gravitational search algorithm for optimal economic dispatch. In: 2nd International Conference on Communication, Computing and Security (ICCCS-2012); Proc. Technol. 6, 411–419. Elsevier (2012)

    Google Scholar 

  16. Panigrahi, B.K., Yadav, S.R., Agarwal, S., Tiwari, M.K.: A clonal algorithm to solve economic load dispatch. Electr. Power Syst. Res. 77, 1381–1389 (2007)

    Article  Google Scholar 

  17. Hosseinnezhad, V., Babaei, E.: Economic load dispatch using \( \theta - \)PSO. Electr. Power Syst. Res. 49, 160–169 (2013)

    Google Scholar 

  18. Al-Summait, J.S., Al-Othman, A.K., Sykulski, J.K.: Application of pattern search method to power system valve point ELD. Electr. Power Syst. Res. 29, 720–730 (2007)

    Google Scholar 

  19. Wang, L., Li, L.P.: An effective differential harmony search algorithm for solving non-convex economic load dispatch problems. Electr. Power Syst. Res. 44, 832–843 (2013)

    Article  Google Scholar 

  20. Reddy, A.S., Vaisakh, K.: Shuffled differential evolution for large scale economic dispatch. Electr. Power Syst. Res. 96, 237–245 (2013)

    Google Scholar 

  21. Bhattacharya, A., Chattopadhyay, P.K.: Solving economic emission load dispatch problems using hybrid differential evolution. Appl. Soft Comput. 11, 2526–2537 (2011)

    Article  Google Scholar 

  22. Sayah, S., Hamouda, A.: A hybrid differential evolution algorithm based on PSO for non convex economic dispatch problems. Appl. Soft Comput. 13, 1608–1619 (2013)

    Article  Google Scholar 

  23. Jiang, S., Ji, Z., Shen, Y.: A novel hybrid PSO and gravitational search algorithm for solving economic emission load dispatch problems with various practical constraints. Electr. Power Syst. Res. 55, 628–644 (2014)

    Google Scholar 

  24. Victoire, T.A.A., Jeyakumar, A.E.: Hybrid PSO-SQP for economic dispatch with valve-point effect. Electr. Power Syst. Res. 71, 51–59 (2004)

    Article  Google Scholar 

  25. Das, K.N., Mishra, R.: Chemo-inspired genetic algorithm for function optimization. Appl. Math. Comput. 220, 394–404 (2013)

    MATH  Google Scholar 

  26. Das, K.N., Mishra, R.: A performance study of chemo inspired genetic algorithm on benchmark functions. In: Proceedings of 7th International Conference on Bio-inspired Computing: Theories and Applications (BICTA-2012); Adv. Intell. Syst. Comput. 2 489–501 (2013)

    Google Scholar 

  27. Mishra, R., Das, K.N.: Chemo-inspired genetic algorithm and application to model order reduction problem. In: Proceedings of 5th International Conference on Soft Computing for Problem solving (SocProS-2015), IIT Roorkee, Saharanpur Campus, AISC series. Springer (2015)

    Google Scholar 

  28. Mishra, R., Das, K.N: A novel chemo-inspired GA for solving constrained optimization problem. In: International Conference on Computing, Communication and Automation (ICCCA2015), ISBN:978-1-4799-8890-7/15/$31.00 ©2015 IEEE 156, Galgotias University, Greater Noida, U.P

    Google Scholar 

  29. Mishra, R., Das, K.N.: A Novel Hybrid Genetic Algorithm for Constrained and Unconstrained Function Optimization, Bio-inspired Computing for Information Retrieval Applications. IGI Global Publisher (2015). ISBN 10:1522523758

    Google Scholar 

  30. Cai, J., Li, Q., Li, L., Peng, H., Yang, Y.: A hybrid CPSO-SQP method for economic dispatch considering the valve-point effects. Energy Convers. Manag. 53, 175–181 (2012)

    Article  Google Scholar 

  31. Chaturvedi, K.T., Pandit, M., Srivastava, L.: Self-organizing hierarchical PSO for non-convex economic dispatch. IEEE Trans. Power Syst. 23(3) (2008)

    Article  Google Scholar 

  32. Bhattacharya, A., Chattopadhyay, P.K.: Hybrid differential evolution with biogeography based optimization for solution of economic load dispatch. IEEE Trans. Power Syst. 25(4) (2010)

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Applied Sciences, KIIT University, Bhubaneswar, 751024, India

    Rajashree Mishra

  2. Department of Mathematics, National Institute of Technology, Silchar, 788010, Assam, India

    Kedar Nath Das

Authors
  1. Rajashree Mishra
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  2. Kedar Nath Das
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Corresponding author

Correspondence to Rajashree Mishra .

Editor information

Editors and Affiliations

  1. Department of Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

  2. Department of Mathematics, National Institute of Technology Silchar, Silchar, Assam, India

    Kedar Nath Das

  3. Department of Mathematics and Computer Science, Faculty of Science, Liverpool Hope University, Liverpool, UK

    Atulya Nagar

  4. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  5. School of Basic Sciences, Indian Institute of Technology Bhubaneswar, Bhubaneswar, Odisha, India

    Akshay Kumar Ojha

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Mishra, R., Das, K.N. (2019). Chemo-Inspired GA for Non-convex Economic Load Dispatch. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 817. Springer, Singapore. https://doi.org/10.1007/978-981-13-1595-4_67

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