Using grid computing to accelerate optimization solution: A system of systems approach

This paper presents a system of systems approach to implement high-speed searching to solve complex optimization problems. In most optimization techniques, parallel computation is not effective due to the complexity in algorithm. Here, the search space is distributed within processing power of high performance computing resources. The methodology takes advantage of the Bees Algorithm and adapts it for best performance in a cluster grid computing environment. The effectiveness of the approach is verified by solving the problem of efficiency in a power plant by economizing fuel cost, efficiency of transmission losses and environmental hazardous emissions.

[1]  W. R. Brownlee.  Co-ordination of Incremental Fuel Costs and Incremental Transmission Losses by Functions of Voltage Phase Angles [includes discussion] , 1954, Transactions of the American Institute of Electrical Engineers. Part III: Power Apparatus and Systems.

[2]  Weiguo Fan,et al.  Parallel Algorithms for Solving Large-Scale Nonlinear Optimization Problems , 2001 .

[3]  Julio Solano-González,et al.  A Parallel Hill Climbing Algorithm for Pushing Dependent Data in Clients–Providers–Servers Systems , 2002, Proceedings ISCC 2002 Seventh International Symposium on Computers and Communications.

[4]  Volker Rehbock,et al.  Multilevel Optimization of Optimal Control Problems , 2001 .

[5]  Duc Truong Pham,et al.  Application of the Bees Algorithm to PCB assembly optimisation , 2007 .

[6]  R. Bruce Shipley,et al.  Exact Economic Dispatch - Digital Computer Solution [includes discussion] , 1956, Transactions of the American Institute of Electrical Engineers. Part III: Power Apparatus and Systems.

[7]  Kalyanmoy Deb,et al.  Evolutionary Multi-objective Environmental/Economic Dispatch: Stochastic Versus Deterministic Approaches , 2005, EMO.

[8]  D.T. Pham,et al.  Optimising Neural Networks for Identification of Wood Defects Using the Bees Algorithm , 2006, 2006 4th IEEE International Conference on Industrial Informatics.

[9]  M. A. Abido.  Environmental/economic power dispatch using multiobjective evolutionary algorithms , 2003 .

[10]  H. Sasaki,et al.  Multiobjective optimal generation dispatch based on probability security criteria , 1988 .

[11]  D.T. Pham,et al.  Application of the Bees Algorithm to the Training of Learning Vector Quantisation Networks for Control Chart Pattern Recognition , 2006, 2006 2nd International Conference on Information & Communication Technologies.

[12]  Ji Young Lee,et al.  Multi-objective Environmental/Economic Dispatch Using the Bees Algorithm with Weighted Sum , 2008 .

[13]  Donald Yeung,et al.  Hill-climbing SMT processor resource distribution , 2009, TOCS.

[14]  Duc Truong Pham,et al.  Optimisation of a fuzzy logic controller using the Bees Algorithm , 2009, Int. J. Comput. Aided Eng. Technol..

[15]  H. H. Happ,et al.  Optimal Power Dispatch , 1974 .

[16]  M. Abido.  Environmental/economic power dispatch using multiobjective evolutionary algorithms , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).