Hybrid ant optimization system for multiobjective economic emission load dispatch problem under fuzziness

Swarm and Evolutionary Computation

Volumn 18, Issue , 2014, Pages 11-21

  Mousa, Abd Allah A ^a,b  

^a Menoufia University   (Egypt)

^b TAIF UNIVERSITY   (Saudi Arabia)

Author keywords

Ant colony optimization; Economic emission load dispatch; Fuzzy numbers; TOPSIS

Indexed keywords

ALGORITHMS; ANT COLONY OPTIMIZATION; ARTIFICIAL INTELLIGENCE; DECISION MAKING; FUZZY SETS; GENETIC ALGORITHMS; INTERNATIONAL TRADE; PARETO PRINCIPLE;

ECONOMIC EMISSION LOAD DISPATCHES; FUZZY NUMBERS; FUZZY REPRESENTATIONS; NONDOMINATED SOLUTIONS; PARETO OPTIMAL SOLUTIONS; STEADY-STATE GENETIC ALGORITHMS; TECHNIQUE FOR ORDER PERFORMANCE BY SIMILARITY TO IDEAL SOLUTION METHODS (TOPSIS); TOPSIS;

MULTIOBJECTIVE OPTIMIZATION;

EID: 84922217338     PISSN: 22106502     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.swevo.2014.06.002     Document Type: Article

References (46)

1. J. Carpentier, Contribution to the economic dispatch problem, Bull. Soc. Francaise Electr. 3 (8) (1962) 431-447.
2. Allen J. Wood, F. Bruce, Power Generation Operation and Control, Wollenberg, John Wiley & Sons, Inc., 2005.
3. R. Yokoyama, S. Bae, T. Morita, H. Sasaki, Multiobjective generation dispatch based on probability security criteria, IEEE Trans. Power Syst. 3 (1) (1988) 317-324.
4. J. S. Dhillon, S. C. Parti, D. P. Kothari, Stochastic economic emission load dispatch, Elect. Power Syst. Res. 26 (1993) 179-186.
5. J. F. Chen, S. D. Chen, Multi objective power dispatch with line flow constraintsusing the fast Newton-Raphson method, IEEE Trans. Energy Convers. 12 (1997) 86-93.
6. R. Jabr, A. Coonick, B. Cory, A homogeneous linear programming algorithm for the security constrained economic dispatch problem, IEEE Trans. Power Syst. 15 (2000) 930-937.
7. S. Takriti, B. Krasenbrink, A decomposition approach for the fuel constrained economic power-dispatch problem, Eur. J. Oper. Res. 112 (1999) 460-466.
8. K. S. Hindi, M. R. Ab Ghani, Dynamic economic dispatch for large-scale power systems: a lagrangian relaxation approach, Electr. Power Syst. Res. 13 (1991) 51-56.
9. L. G. Papageorgiou, E. S. Fraga, A mixed integer quadratic programming formulation for the economic dispatch of generators with prohibited operating zones, Electr. Power Syst. Res. 77 (2007) 1292-1296.
10. Z. X. Liang, J. D. Glover, A zoom feature for a dynamic programming solution to economic dispatch including transmission losses, IEEE Trans. Power Syst. 7 (1992) 544-550.
11. M. S. Osman, M. A. Abo-Sinna, A. A. Mousa, A solution to the optimal power flow using genetic algorithm, J. Appl. Math. Comput. (AMC) 155 (2) (2004) 391-405.
12. M. S. Osman, M. A. Abo-Sinna, A. A. Mousa, IT-CEMOP: an iterative coevolutionary algorithm for multiobjective optimization problem with nonlinear constraints, J. Appl. Math. Comput. (AMC) 183 (2006) 373-389.
13. M. S. Osman, M. A. Abo-Sinna, A. A. Mousa, Epsilon-dominance based multiobjective genetic algorithm for economic emission load dispatch optimization problem, Electr. Power Syst. Res. 79 (2009) 1561-1567.
14. P. H. Chen, H. C. Chang, Large-scale economic dispatch by genetic algorithm, IEEE Trans. Power Syst. 10 (1995) 1919-1926.
15. H. K. Youssef, K. M. El-Naggar, Genetic based algorithm for security constrained power system economic dispatch, Electr. Power Syst. Res. 53 (1) (2000) 47-51.
16. Ching-Tzong Su, Chien-Tung Lin, New approach with a hopfield modeling framework to economic dispatch, IEEE Trans. Power Syst. 15 (2) (2000) 541.
17. W. M. Lin, F. S. Cheng, M. T. Tsay, Non-convex economic dispatch by integrated artificial intelligence, IEEE Trans. Power Syst. 16 (2) (2001) 307-313.
18. T. Jayabarathi, G. Sadasivam, V. Ramachandran, Evolutionary programming based economic dispatch of generators with prohibited operating zones, Electr. Power Syst. Res. 52 (1999) 261-266.
19. J. Kennedy, R. C. Eberhart, A discrete binary version of the particle swarm optimization algorithm, in: Proceedings of the Conference on Systems, Man and Cybernetics (SMC'97), 1997, pp. 4104-4109.
20. R. C. Eberhart, Y. Shi. Comparison between genetic algorithms and particle swarm optimization, in: Proceedings of IEEE International Conference on Evolutionary Computation, 1998, pp. 611-616.
21. J. B. Park, K. S. Lee, J. R. Shin, K. Y. Lee, A particle swarm optimization for economic dispatch with non-smooth cost functions, IEEE Trans. Power Syst. 20 (3) (2005) 34-42.
22. Y. H. Song, C. S. Chou, T. J. Stonham, Combined heat and power economic dispatch by improved ant colony search algorithm, Electr. Power Syst. Res. 52 (2) (1999) 115-121.
23. R. Storn, K. Price, Differential evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces, Technical Report TR-95-012, International Computer Science Institute, Berkeley, CA, 1995.
24. T. Yalcinoz, M. J. Short, Neural networks approach for solving economic dispatch problem with transmission capacity constraints, IEEE Trans. Power Syst. 13 (May) (1998) 307-313.
25. S. Goss, S. Aron, J. L. Deneubourg, J. M. Pasteels, The self-organizing exploratory pattern of the Argentine ant, J. Insect Behav. 3 (1990) 159-168.
26. B. Baran, M. Schaerer, A multiobjective ant colony system for vehicle routing problem with time windows, in: Proceedings of the Twenty first IASTED International Conference on Applied Informatics, Innsbruck, Austria, February 10-13, 2003, pp. 97-102.
27. G. Fuellerer, Karl F. Doerner, Richard F. Hartl, Manuel Iorib, Ant colony optimization for the two-dimensional loading vehicle routing problem, Comput. Oper. Res. 36 (2009) 655-673.
28. M. Dorigo, T. Stützle, Ant Colony Optimization, MIT Press, Cambridge, MA, 2004.
29. T. Niknam, A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convex economic dispatch problem, Appl. Energy 87 (2010) 327-339.
30. T. A. A. Victoire, A. E. Jeyakumar, Hybrid PSO-SQP for economic dispatch with valve-point effect, Electr. Power Syst. Res. 71 (2004) 51-59.
31. P. Subbaraj, R. Rengaraj, S. Salivahanan, Enhancement of combined heat and power economic dispatch using self adaptive real-coded genetic algorithm, Appl. Energy 86 (2009) 915-921.
32. A. A. Mousa, M. A. El-Shorbagy, Waiel. F. Abd El-Wahed, Local search based hybrid particle swarm optimization for multiobjective optimization, Int. J. Swarm Evolut. Comput. 3 (2012) 1-14.
33. S. Coelho Ldos, V. C. Mariani, Combining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve-point effect, IEEE Trans. Power Syst. 21 (2006) 989-996.
34. S.-Z. Zhao, P. N. Suganthan, Q. Zhang, MOEA/D with an ensemble of neighbourhood sizes, IEEE Trans. Evolut. Comput. (TEC) 16 (3) (2012) 442-446.
35. L. Ke, Q. Zhang, R. Battiti, MOEA/D-ACO: a multiobjective evolutionary algorithm using decomposition and ant colony, IEEE Trans. Cybern. 43 (6) (2013) 1845-1859.
36. A. A. Mousa, R. M. Rizk-Allah, W. F. Abd El-Wahed, A hybrid ant colony optimization approach based local search scheme formultiobjective design optimizations, Electr. Power Syst. Res. 81 (2011) 1014-1023.
37. B. S. Kermanshahi, Y. Wu, K. Yasuda, R. Yokoyama, Environmental marginal cost evaluation by non-inferiority surface, IEEE Trans. Power Syst. 5 (4) (1990) 1151-1159.
38. R. Yokoyama, S. H. Bae, T. Morita, H. Sasaki, Multiobjective generation dispatch based on probability security criteria, IEEE Trans. Power Syst. 3 (1) (1988) 317-324.
39. M. Dorigo, T. Stützle, The ant colony optimization metaheuristic: algorithms, applications and advances, in: Handbook of Metaheuristics, in: F. Glover, G. Kochenberger (Eds.), vol. 57 of International Series in operations Research & Management Science, Kluwer Academic Publishers, Norwell, Mass, USA, 2000, pp. 251-285.
40. A. A. Mousa, R. M. Rizk-Allah, W. F. Abd El-Wahed, A hybrid ant colony optimization approach based local search scheme formultiobjective design optimizations, Electr. Power Syst. Res. 81 (2011) 1014-1023.
41. C. L. Hwang, K. Yoon, Multiple Attribute Decision Making: Methods and Applications, Springer-Verlag, New York, 1981.
42. D. L. Olson, Comparison of weights in TOPSIS models, Math. Comput. Model. 40 (2004) (2004) 721-727.
43. R. Zimmerman, D. Gan, MATPOWER: AMatlab power system simulation package, available at: (http://www.pserc.cornell.edu/matpower/).
44. M. A. Abido, A novel multiobjective evolutionary algorithm for environmental/economic power dispatch, Electr. Power Syst. Res. 65 (2003) 71-81.
45. M. A. Abido, A niched Pareto genetic algorithm for multiobjective environmental/economic dispatch, Electr. Power Energy Syst. 25 (2) (2003) 97-105.
46. M. A. Abido, Environmental/economic power dispatch using multiobjective evolutionary algorithms, IEEE Trans. Power Syst. 18 (4) (2003) 1529-1537.