Abstract—Multi-Objective Optimization of a benchmark cogeneration problem known as CGAM cogeneration system has been carried out from Exergetic, Economic and Environmental aspects simultaneously. One of the most suitable optimization techniques, known as GA, has been developed here. For using this approach a hybrid fitness function is defined and used to find the optimal solutions with respect to the aforementioned objective functions. This Multi-Objective GA with normalized hybrid fitness function has been considered to decrease the computational cost. CGAM Problem designs a cogeneration plant which delivers 30 MW of electricity and 14 kg/s of saturated steam at 20 bars. The thermodynamic modeling has been implemented comprehensively while economic analysis of this system conducted. Consideration of Five decision variables in modeling process made the final optimal solution more realistic in comparison with previous studies in this field. Finally the result of optimization is compared with conventional and multi objective Particle swarm optimization method and the advantages of proposed method are shown.
Index Terms—Multivariable systems; CGAM cogeneration system; Genetic Algorithm; Multi-Objective Particle Swarm Optimizer (MOPSO).
Abdorreza Alavi Gharahbagh is with the department of electrical and computer engineering, Islamic azad university, shahrood branch, Po box 36155/163, shahrood, IRAN. (e-mail: R_alavi@iau_shahrood.ac.ir).
Meisam Babaie is with the department of mechanical engineering, K.N Toosi University of Technology, Tehran, Iran. (e-mail: firstname.lastname@example.org).
Davoud Abbasi is with Islamic azad university, shahrood branch, shahrood, IRAN. (e-mail: email@example.com).
Cite: Abdorreza Alavi Gharahbagh, Meisam Babaie, and Davoud Abbasi, "An Improved Genetic Algorithm Application in a Multi-Objective Design of a Benchmark Cogeneration System," International Journal of Machine Learning and Computing vol. 1, no. 3, pp. 263-268, 2011.