The objective of the Economic Dispatch(ED) Problems

\r\nof electric power generation is to schedule the committed generating

\r\nunits outputs so as to meet the required load demand at minimum

\r\noperating cost while satisfying all units and system equality and

\r\ninequality constraints. This paper presents a new method of ED

\r\nproblems utilizing the Max-Min Ant System Optimization.

\r\nHistorically, traditional optimizations techniques have been used,

\r\nsuch as linear and non-linear programming, but within the past

\r\ndecade the focus has shifted on the utilization of Evolutionary

\r\nAlgorithms, as an example Genetic Algorithms, Simulated Annealing

\r\nand recently Ant Colony Optimization (ACO). In this paper we

\r\nintroduce the Max-Min Ant System based version of the Ant System.

\r\nThis algorithm encourages local searching around the best solution

\r\nfound in each iteration. To show its efficiency and effectiveness, the

\r\nproposed Max-Min Ant System is applied to sample ED problems

\r\ncomposed of 4 generators. Comparison to conventional genetic

\r\nalgorithms is presented.<\/p>\r\n","references":"[1] X. Guan, P.B. Luh, L. Zhang, Nonlinear approximation method in\r\nLagrangian relaxation based algorithms for hydrothermal scheduling,\r\nIEEE Trans. Power Systems, Vol. 10, (2), pp. 772-778, 1995.\r\n[2] A.J. Wood, B.F. Wollenberg, Power Generation, Operation and Control,\r\nJohn Wiley & Sons, New York 1996.\r\n[3] A.M. Chebbo, M.R. Irving, Combined active and reactive dispatch, Proc\r\nIEE, Pt.C, (4), pp. 393-405, 1995.\r\n[4] S. Granville, Optimal reactive dispatch through interior point methods,\r\nIEEE Summer Meeting, Paper No. 92, SM 416-8 PWRS, 1992.\r\n[5] D.C. Walter and G.B. Sheble, \u201cGenetic algorithm solution of economic\r\ndispatch with valve point loading,\u201d IEEE Trans. Power Syst., vol. 8, no. 3,\r\npp. 1325\u20131332, Aug. 1999.\r\n[6] K.P. Wong and C.C. Fung, \u201cSimulated annealing based economic\r\ndispatch algorithm,\u201d Proc. Inst. Elect. Eng. C., Gen., Transm., Distrib.,\r\nvol. 140, no. 6, pp. 505\u2013519, Nov. 1993.\r\n[7] N. Sinha, R. Chakrabarti, and P. K. Chattopadhyay, \u201cEvolutionary\r\nprogramming techniques for economic load dispatch,\u201d IEEE Trans. Evol.\r\nComput., vol. 7, no. 1, pp. 83\u201394, Feb. 2003.\r\n[8] W.M. Lin, F.S. Cheng, and M.T. Tsay, \u201cAn improved tabu search for\r\neconomic dispatch with multiple minima,\u201d IEEE Trans. Power Syst., vol.\r\n17, no. 1, pp. 108\u2013112, Feb. 2002.\r\n[9] T. Stutzle, and H.H. Hoos, Max-Min Ant System, Future Generation\r\nComputer Systems, 16, pp.889-914, 2001.\r\n[10] E. Bonabenn, M. Dorigo, G. Theraulaz, Swarm Intelligence from natural\r\nto Artificial systems, Sante Fe Institute studies in the Sciences of\r\ncomplexity, Oxford University Press, 1999.\r\n[11] M. Dorigo, G. Di Caro, The Ant Colony Optimization Meta-Heuristic, In\r\nD.Come, M.Dorigo, and F.Glover, editors, New Ideas in optimization, Mc\r\nGraw-Hill, 2001.\r\n[12] T. Stutzle, H. Hoos, The MAX-MIN Ant System and local search for the\r\ntraveling Salesman Problem, Proceedings of the IEEE International\r\nconference on Evolutionary Computaion , ICEC \u201997, pp.309-314. 1997.\r\n[13] T. Stutzle, An Ant Approach to the Flow Shop Problem, Proceedings of\r\nthe 6th European Congress on Intelligent Techniques and soft Computing\r\n(EUFIT \u201998), (3), Verlag Mainz, Aachem, pp.1560-1564, 1997.\r\n[14] M. Dorigo, L.M. Gambardella, Ant Algorithms for Discrete Optimization,\r\nArtificial Life, volume 5, no.2, pp.137-172, 1998.\r\n[15] M. Dorigo , V. Maniezzo, and K. Colorni, The ant system: optimization\r\nby a colony of cooperating agents, IEE Transactions on Systems, Man,\r\nand Cybernetics, Part B, Cybernetics, 26(1), pp. 29-44, 2000.\r\n[16] T. Stutzle and H.H. Hoos, MAX-MIN Ant System, Future Generation\r\nComputer Systems, (16), pp.889-914, 2003","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 95, 2014"}