Dergiler / Hacettepe Journal of Mathematics and Statistics / 2019 / Cilt: 48 - Sayı: 5

Implementation of improved grasshopper optimization algorithm to solve economic load dispatch problems

Sayfa
1570–1589
Yayın Tarihi
2022-10-18
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

IGOA is better in terms of convergence rate and quality of solutions obtained for the problems considered in literature for other metaheuristics.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:The costs of different fuels are increasing gradually, for operation of power production units.
📊
Temel Bulgular & Veri:In this paper, we have updated the Grasshopper Optimization Algorithm (GOA) with a better initialization strategy to balance the search capability of GOA.
💡
Sonuç & Katkı:IGOA is better in terms of convergence rate and quality of solutions obtained for the problems considered in literature for other metaheuristics.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:constrained optimization · metaheuristics · improved grasshopper optimization algorithm (IGOA)

Abstract

The costs of different fuels are increasing gradually, for operation of power production units. Thus new optimization techniques are needed to tackle the complex problems of Economic Load Dispatch (ELD). Metaheuristics are very helpful for policy and decision makers in achieving the best results by minimizing the cost function. In this paper, we have updated the Grasshopper Optimization Algorithm (GOA) with a better initialization strategy to balance the search capability of GOA. The new algorithm is named as Improved Grasshopper Algorithm (IGOA). GOA is inspired by the swarms of grasshopper and mimics their biological behavior. Furthermore, IGOA is used to solve the ELD problems by tacking four case studies from literature. The objective in these problems is to find best decision variables for dispatching the available power with lowest cost, better efficiency and more reliability. To validate the efficiency of our proposed algorithm, we have tested it by solving 4 case studies of ELD with 1263MW, 600MW, 800MW and 2500MW demands respectively. IGOA is better in terms of convergence rate and quality of solutions obtained for the problems considered in literature for other metaheuristics.

🔖

Bu Makaleye Atıf Yap

Sulaiman, M., Masihullah, M., Hussain, Z., Ahmad, S., Mashwani, W. K., Jan, M. A., Khanum, R. A. (2022). Implementation of improved grasshopper optimization algorithm to solve economic load dispatch problems. Hacettepe Journal of Mathematics and Statistics, Cilt: 48 - Sayı: 5, 1570–1589. https://ajindex.com/articles/529751

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (14)

  1. I. Aljarah, A.Z. AlaM, H. Faris, M.A. Hassonah, S. Mirjalili and H. Saadeh, Simulta- neous feature selection and support vector machine optimization using the grasshopper optimization algorithm, Cognitive Computation, 21,1-18, 2018
  2. A. Bhattacharya and P.K. Chattopadhyay, Biogeography-based optimization for different economic load dispatch problems, IEEE transactions on power systems, 25, 1064-1077, 2010
  3. A.H. Bindu and M.D. Reddy, Economic load dispatch using cuckoo search algorithm, Int. Journal Of Engineering Research and Applications, 3, 498-502, 2013
  4. M. Basu, A simulated annealing-based goal-attainment method for economic emission load dispatch of fixed head hydrothermal power systems, International Journal of Electrical Power & Energy Systems, 27, 147-153, 2005
  5. A. Bhattacharya and P.K. Chattopadhyay, Solving complex economic load dispatch problems using biogeography-based optimization, Expert Systems with Applications, 37, pp.3605-3615, 2010
  6. P.H. Chen and H.C. Chang, Large-scale economic dispatch by genetic algorithm, IEEE transactions on power systems, 10, 1919-1926, 1995
  7. H.M. Dubey, M. Pandit, B.K. Panigrahi and M. Udgir, Economic load dispatch by hybrid swarm intelligence based gravitational search algorithm, International Journal of Intelli- gent Systems and Applications, 5, 21, 2013
  8. M. Fesanghary, and M.M. Ardehali, A novel meta-heuristic optimization methodology for solving various types of economic dispatch problem, Energy, 34, 757-766, 2009
  9. Z.W. Geem, J.H. Kim and G.V. Loganathan, A new heuristic optimization algorithm: harmony search. simulation, 76, 60-68, 2001
  10. Z.L. Gaing, Particle swarm optimization to solving the economic dispatch considering the generator constraints, IEEE transactions on power systems. 18, 1187-95, 2003
  11. Y.C. Ho and Q.C. Zhao and D.L. Pepyne, The no free lunch theorems: Complexity and security, IEEE Transactions on Automatic Control, 48, 83-793, 2003
  12. S. Hemamalini and S.P. Simon, Artificial bee colony algorithm for economic load dispatch problem with non-smooth cost functions, Electric Power Components and Systems, 38, 786-803, 2010
  13. A. Lewis, LoCost: a spatial social network algorithm for multi-objective optimisation, In Evolutionary Computation, CEC’09, IEEE Congress, 2866-2870, 2009
  14. S.Z. Mirjalili, S. Mirjalili, S. Saremi, H. Faris and I. Aljarah, Grasshopper optimization algorithm for multi-objective optimization problems, Applied Intelligence, 4