Artificial Bee Colony Optimization Embedded with Simulated Annealing for the Combined Heat and Power Economic Dispatch Problem


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Abstract


This paper presents a new approach for solving the Combined Heat and Economic Dispatch (CHPED) problem using an integrated algorithm based on artificial bee colony algorithm (ABC) and Simulated Annealing (SA). Artificial Bee Colony algorithm (ABC) is inspired by the foraging behavior of honey bee swarm, is a biological inspired optimization. It shows more effective than the other optimization algorithms. However, ABC is good at exploration but poor at exploitation, and its convergence speed is also slow in some cases. To overcome this deficiency, this paper proposes an improved ABC algorithm called ABC-SA algorithm.   In this algorithm ABC is acting as a base level search to direct the search towards the optima region and local searches synergistically combined with simulated Annealing (SA). The performance of the proposed algorithm (ABC-SA) is validated by illustration with test system. The results of the proposed algorithm are compared with those of Practical Swarm Optimization (PSO), ABC, Real –Coded Genetic Algorithm (RCGA), Bee Colony Optimization (BCO), SA and Evolutionary Programming techniques (EP). From numerical results, it is seen that the proposed algorithm is able to provide a better solution at a lesser computational effort.
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Keywords


Artificial Bee Colony Optimization; Cogeneration; Combined Heat and Power Economic Dispatch; Simulated Annealing

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References


Fj. Rooijerj , R.A.M Amerongen., Static economic dispatch for co-generation systems, IEEE. Transpower syst,Vol.9(issue);1392-8, 1994.

T.Gou , MI .Henwood, M ,Van Ouijen, An algorithm for combined heat and power economic dispatch, IEEE Trans Power System;Vol.11 (issue4):1778-84, 1996.

A .Rong , R.Lahdelma, An efficient envelope-based branch-and-bound algorithm for non convex combined heat and power production planning,EurJ oper Res;,Vol.183(1):1412-31, 2007.

Yh .Song ,QY. Xuan, Combined heat and power economic dispatch using genetic algorithm based penalty function method. Electrical power comp syst,Vol. 26(issue4):363-72, 1998.

M.Sudhakaran, SMR .Slochanal, Integrating genetic algorithm and tabu economic dispatch search for combined heat and power, Power System Stability control:67-612003

A,Vasebi, M Fesanghary, SMT .Bathaee. Combined heat and power economic dispatch by harmony search algorithm, Int J electrical power energy system,V.ol29:713-9, 2007.

P.Subbharaj, R Rengaraj, R .Salivahanan, Enhancement of combined heat and power economic dispatch using self adaptive real-coded genetic algorithm, Applied energy, Vol.86:915-921,2008.

YH .Song , CS .Chou , TJ .Stonham, Combined heat and power dispatch by improved ant colony search algorithm,Electrical Power Syst res Vol.52:115-21, 1999.

CT.Su, CL.Chaiang, An incorporated algorithm for combined heat and power economic dispatch, Electric power syst res ,Vol.69(2-3);187-95,2004.

LF .Wang, C .Singh. Stochastic combined heat and power dispatch based on multi-objective particle swarm optimization., Int J Electric power Energy syst, Vol.30:22634, 2008.

M. Basu. Bee colony optimization for combined heat and power economic dispatch ,Expertsysetm with applications Vol.38:13527-31, 2011.

Seyyed Soheil Sadat Hosseini, Ali .Jafarnejad,Amir Hossein Behronz,Amir Hossein Gandomi. Combined heat and power economic dispatch by mesh adaptive direct search algorithm.,Expert system with applications,Vol.38:6556-64, 2011.

C.LChiang,AN.Chia Wang .Hybrid differential evolution for cogeneration economic dispatch problem. Proc of the 9¬¬¬th con.on. Machine Learning and Cybernetics,Qingdao, 2010

Eberhart R, Shi Y, Kennedy J. Swarm intelligence.San Francisco, Morgan Kaufmann, 2001.

Bonbeau.E, Dorigo M, Theraulaz G. Swarm Intelligence: From natural to artificial systems.(new York:oxford university press ,1999).

S.Camazine, J .Deneubourg, M.R. Franks, J .Syneyd, G,.Therayla E.Bonabeau ,Self-organisation in biological system (princeton: Princeton university press,2003).

D .Karaboga, B .Busturk, A powerful and efficient algorithm for numerical opimization, Ericyes University, Kayseri, turkey, technical report_Tr06, 2005.

D .Karaboga, B.Busturk ,A powerful and efficient algorithm for numerical function optimization artificial bee colony optimization, Journal of global optimization, Vol.39:459-471, 2007.

D. Karaboga. An idea based on honey bee swarm for numerical optimization, Technical Report_ TR06,Ericyes university, engineering faculty, computer engineering department, Turkey,2005.

D. Karaboga, B.akay,A comparative study of artificial bee colony, applied mathematics and computation,Vol.214 :108-132, 2009.

D. Karaboga, B.Basturk, on the performance of artificial bee colony (ABC) algorithm Applied soft computing,Vol. 8:687-697, 2008.

A. Singh, An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem, Applied soft computing ,Vol.9 :625-631, 2009.

F.Kang ,j.Li, O.Xu, Structural inverse analysis by hybrid simplex artificial bee colony algorithms, computers and structures ,Vol.87:861-70, 2009.

N.Karaboga ,A new design method based on artificial bee colony algorithm for digital IIR filters, Journal of the Franklin institute ,Vol.346:328-48, 2009.

Li. GuoQiang, Niu .Peifeng, Xingjun Xiao,Development and investigation of efficient artificial bee colony algorithm for numerical function optimization. Applied soft computing ,Vol.12:320-332, 2012.

KP .Wang, CC .Fung. Simulated annealing based economic dispatch algorithm. Proc Inst Electr Engg Conference ,Vol.140:509-15, 1995.

LJ .Fogel, AJ. Owons, MJ .Walsh. Artificial intelligence through simulated Evaluation.( New York- John wiley and sons, 1996).

D. Mitra, F. Romeo, A. Sangiovanni- Vincentelli,Convergence and finite-time behaviour of simulated annealing.in proc.24th conf. Decision and Control,pp:761-67, Dec.1985.

M. Locatelli. Convergence properties of simulated annealing for continuous global optimization.J.Appl.Probab,Vol.33:1127-40, 1996.


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