BibTex RIS Kaynak Göster
Yıl 2017, Cilt: 7 Sayı: 1, 33 - 73, 13.06.2017
https://doi.org/10.17678/beuscitech.322141

Öz

Kaynakça

  • Amini M, Bazargan M (2014). Two objective optimization in shell-and-tube heat exchangers using genetic algorithm. Appl Therm Eng 69, 278-285.
  • Arora R, Kaushik SC, Kumar R, Arora R (2016). Soft computing based multi objective optimization of Brayton cycle power plant with isothermal heat addition using evolutionary algorithm and decision making. Appl Soft Comput 46, 267 – 283.
  • Arora R, Kaushik SC, Arora R (2016). Thermodynamic modelling and multi-objective optimization of two stage thermoelectric generator in electrically series and parallel configuration. Appl Therm Eng 103, 1312-1323.
  • Askarzadeh A (2014). Bird mating optimizer: An optimization algorithm inspired by bir mating strategies. Commun Nonlinear Sci 19,1213-1228.
  • Asadi M, Song Y, Sunden G, Xie G (2014). Economic optimization design of shell and tube heat exchangers by a cuckoo search algorithm. Appl Therm Eng 73, 1032-1040.
  • Brest J, Greiner S, Boscovic B, Mernik M, Zumer V (2006). Self adaptive control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE T Evol Comput 10, 646-657.
  • Civicioğlu P (2013). Artifical cooperative search algorithm for numerical optimization problems. Inform Sci 229, 58-76.
  • Civicioglu P (2013). Backtracking Search Optimization Algorithm for numerical optimization problems. Appl Math Comput 219, 8121-8144.
  • Civicioglu P (2012). Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Comput Geo 46, 229 – 247.
  • Edwards MF, Changal AA, Parott DL (1974). Heat transfer and pressure drop characteristics of a plate heat exchanger using Newtonian and non-Newtonan liquids. Chem Eng 286-293.
  • Erol OK, Eksin I (2006). A new optimization method: Big Bang – Big Crunch. Adv Eng Softw. 37, 106-111.
  • Fettaka S, Thibault J, Gupta Y (2013). Design of shell and tube heat exchangers using multi objective optimization. Int J Heat Mass Transfer. 60, 343-354.
  • Focke WW (1986). Selecting optimum plate heat exchanger surface pattern. J Heat Transfer 108, 153-160.
  • Galeazzo FCC, Miura RY, Gut JAW, Tadini CC (2006). Experimental and numerical heat transfer in a plate heat exchanger. Chem Eng Sci 61, 7133-7138.
  • Gamperle R, Müller SD, Koumoutsakos P (2002). A parameter study for differential evolution. In: Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, WSEAS Press, Interlaken, Switzerland, pp. 293-298.
  • Gandomi AH, Yang XS (2012). Evolutionary boundary constraint handling scheme. Neural Comput Appl 21, 1449-1462.
  • Geem ZW, Kim JH, Loganthan GV (2001). A new heuristic optimization algorithm: Harmony Search. Simulation 76, 60 – 68.
  • Gherasim I, Taws M, Galanis N, Nguyen CT (2011). Heat transfer and fluid flow in a plate heat exchanger part I. Experimental investigation. Int J Therm Sci 50, 1492-1498.
  • Gong W, Fialho A, Cai Z, Li H (2011). Adaptive strategy selection in differential evolution for numerical optimization:an empirical study. Inform. Sci. 181, 5364-5386.
  • Guo J, Cheng L, Xu M (2009). Optimization design of shell and tube heat exchanger by entropy generation minimization and genetic algorithm. Appl Therm Eng 29, 2954-2960.
  • Gut JAW, Pinto JM (2004). Opitmal configuration design for plate heat exchangers. Int J Heat Mass Transfer 47, 4833-4848.
  • Gut JAW, Fernandes R, Pinto JM, Tadini CC (2004). Thermal model validation of plate heat exchangers with generalized configurations. Chem Eng Sci 59, 4591 – 4600.
  • Hadidi A, Nazari A (2013). Design and economic optimization of shell and tube heat exchangers using biogeography based (BBO) algorithm. Appl Therm Eng 51, 1263 – 1272.
  • Hadidi A, Hadidi M, Nazari A (2013). A new design approach for shell and tube heat exchangers using imperialist competitive algorithm (ICA) from economic point of view. Energy Convers Manage 67, 66-74.
  • Hajabdollahi F, Hajabdolahi Z, Hajabdollahi H (2013). Optimum design of gasket plate heat exchanger using multimodal genetic algorithm. Heat Transfer Res 44,1-19.
  • Han XH, Cui LQ, Chen SJ, Chen GM, Wang Q (2010). A numerical and experimental study of chevron, corrugated plate exchangers. Int Comm Heat Mass Transfer 37, 1008-1014.
  • Jassim EW, Newell TA, Chato JC (2006). Refrigerant pressure drop in chevron and bumpy style flat plate heat exchangers. Exp Therm Fluid Sci 30, 213-222.
  • Jia G, Wang Y, Cai Z, Jin Y (2013). An improved (µ+ ) – constraind differential evolution for constrained optimization. Inform Sci 222, 302-322.
  • Kakaç S, Liu H, Pramuanjaroenkij A (2012). Heat exchangers: selection, rating, and thermal design. Taylor& Francis, Boca Raton.
  • Kandlikar SG, Shah RK (1989). Multi pass heat exchangers effectiveness-NTU results and guidelines for selecting pass arrangements. J Heat Transfer 111, 300-313.
  • Kennedy J, Eberhart R (1995). Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, 1942 – 1948.
  • Khosravi R, Khosravi A, Nahavandi S, Hajabdollahi H (2015). Effectiveness of evolutionary algorithms for optimization of heat exchangers. Energy Convers Manage 89, 281 – 288.
  • Kumar R, Kaushik SC, Kumar R, Hans R (2016). Multi-ojective thermodynamic optimization of an irreversible regenerative Brayton cycle using evolutionary algorithm and decision making. Ain Shams Eng J 7, 741-753.
  • Kumar H (1984). The plate heat exchanger: construction and design. In: 1st UK National Conference on Heat Transfer, University of Leeds, 3-5 July, Institution of Chemical Engineers Symposium Series, pp.1275 – 128.
  • Liang J, Suganthan PN (2005). Dynamic multi-swarm particle swarm optimizer. In:Proceedings 2005 IEEE Swarm Intelligence Symposium, IEEE, pp. 124-129.
  • Luan Z., Zhang G, Tian M, Fan M (2008). Flow resistance and heat trasfer characteristics of a new type plate heat exchanger. J Hydrodyn Ser B 20, 524-529.
  • Martin H (1996). A theoretical approach to predict the performance of chevron-type plate heat exchangers. Chem Eng Process: Process Intens 35, 301-310.
  • May R (1976). Simple mathematical models with very complicated dynamics. Nature 261, 459 – 467.
  • Mirjalili S (2015). Moth-flame optimization algorithm algorithm: A novel nature-inspired heuristic paradigm. Knowl-Based Syst 89, 228 – 249.
  • Mirjalili S, Mirjalili SM, Hatamlou A (2016). Multi-Verse Optimizer: a nature inspired algorithm for global optimization. Neural Comput & Applic 27, 495 – 513.
  • Mohanty DK (2016). Application of firefly algorithm for design optimization of a shell and tube heat exchanger from economic point of view. Int J Therm Sci 102, 228 -238.
  • Mohanty DK (2016). Gravitational search algorithm for economic optimization design of a shell and tube heat exchanger. Appl Therm Eng 107, 184 – 193.
  • Najafi H, Najafi B (2010). Multi-objective optimziation of a plate and frame heat exchanger via genetic algorithm. Heat Mass Transfer. 46, 639-647.
  • Novoa-Hernandez P, Cruz-Corona C, Pelta DA (2013). Self-adaptive, multipopulation differential evolution in dynamic environments. Soft Comput 17, 1861-1881.
  • Patel VK, Rao RV (2010). Design optimization of shell-and tube heat exchanger using particle swarm optimization technique. Appl Therm Eng 30, 1417-1425.
  • Patel V, Savsani V (2014). Optimization of a plate –fin heat exchanger deign through an improved multi objective teaching learning based optimization (MO-ITLBO) algorithm. Chem Eng Res Des 92, 2371 – 2382.
  • Ponce-Ortega JM, Serna-Gonzalez M, Jimenez-Gutierrez A (2009). Use of genetic algorithms for the optimal design of shelland tube heat exchangers. Appl Therm Eng 29, 203 – 209.
  • Qin AK, Huang VL, Suganthan PN (2009). Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE T Evol Comput 13, 398-417.
  • Rao RV, Patel V (2013). Multi objective optimization of heat exchangers using a modified taching learning based optimization algorithm. Appl Math Model 37, 1146 – 1162.
  • Rao RV, Waghmare GG (2015). Multiobjective design optimization of a plate fin heat sink using a teaching learning based optimization algorithm. Appl Therm Eng 76, 521-529.
  • Rönkkönen K, Kukkonen S, Price KV (2005). Real-parameter optimization with differential evolution. In: IEEE Congression Evolutionary Computation, pp.506-513.
  • Sahin AS, Kılıç B, Kılıç U (2011). Design and economic optimization of shell and tube heat exchangers using artificial bee colony (ABC) algorithm. Energy Convers Manage 52, 3356 – 3362.
  • Selbas R, Kızılkan O, Reppich M (2006). A new design approach for shell-and-tube heat exchangers using genetic algorithms from economic point of view. Chem Eng Process: Process Intens 45, 268-275.
  • Storn R, Price K (1997). Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J Global Opt 11, 341- 359.
  • Storn R (1996). On the usage of differential evolution for function optimization. In: Biennial Conference of the North American Fuzzy Information Processing Society (NAFIPS), IEEE, pp.519-523.
  • Sun J, Feng B, Xu WB (2004). Particle swarm optimization with particles having quantum behavior. IEEE Proceedings of Congress on Evolutionary Computation, pp. 325-331.
  • Sun J, Xu W, Feng B (2004). A global search strategy of quantum behaved particle swarm optimization. Cybernetics and Intelligent Systems Proceedings of the 2004, IEEE Conference, pp. 111-116.
  • Tiwari AK, Ghosh P, Sarkar J (2013). Heat transfer and pressure drop characteristics of CeO2/water nanofluid in plate heat exchanger. Appl Therm Eng 57, 24-32.
  • Tiwari AK, Ghosh P, Sarkar J (2013). Performance comparison of the plate heat exchanger using different nanofluids. Exp Therm Fluid Sci 49, 141-151.
  • Turgut OE, Coban MT (2016). Thermal design of spiral heat exchangers and heat pipes through global best algorithm. Heat Mass Transfer. doi:10.1007/s00231-016-1861-y
  • Wang L., Sunden B (2003). Optimal design of plate heat exchangers with and without pressure drop specifications. Appl Therm Eng 23, 295-311.
  • Yadav P, Kumar R, Panda SK, Chang CS (2012). An Intelligent Tuned Harmony Search algorithm for optimisation. Inform Sci 196, 47 – 72.
  • Yang XS (2010). A new metaheuristic bat-inspired algorithm. In: Nature Inspired Cooperative Strategies for Optimization (NISCO 2010) Studies in Computational Intelligence, pp. 65-74.
  • Yu WJ, Zhang J (2011). Multi population differential evolution with adaptive parameter control for global optimization. In: Gecco-2011: Proceedings of the 13th Annual Genetic and Evolutionary Computation Conference, pp.1093-1098.
  • Zaleski T, Klepacka K (1992). Plate heat exchanger method of calculations,charts and guidelines for selecting plate heat exchanger configurations. Chem Eng Process: Process Intens 31, 45-56.
  • Zhang J, Ding X (2011). A multi swarm self-adaptive and cooperative particle swarm optimization. Eng Appl Artif Intel 24, 958-967.
  • Zhao SZ, Suganthan PN, Pan QK, Tasgetiren MF (2011). Dynamic multi-swarm particle swarm optimizer with harmony search. Expert Syst Appl 38, 3735-3742.

Multi-objective thermal design optimization of plate frame heat exchangers through Global Best Algorithm

Yıl 2017, Cilt: 7 Sayı: 1, 33 - 73, 13.06.2017
https://doi.org/10.17678/beuscitech.322141

Öz

This study deals with thermal design of plate frame heat exchangers based on Global Best Algorithm. By utilizing some basic perturbation schemes adopted from Differential Search and Differential Evolution, Global Best Algorithm aims to obtain optimum solution of  any optimization problem with intensifying on exploitiation of the promising solutions rather than exploring of the unvisited paths of the  search domain. Firstly, optimization performance of the proposed algorithm has been benchmarked against variety of well-known optimization algorithms by means of 16 different highly challenging optimization test functions. Then, the proposed method is put into practice to acquire the optimal values of the design variables those optimize the considered problem objectives including overall heat transfer coefficient, total cost and weight of the plate frame heat exchangers seperately as well as simultaneously. Considerable improvement in objective function values is observed as compared to preliminary design in single objective manner.  Pareto frontier  is constructed for dual and triple objective and best optimal solution among the curve is selected by means of the widely-known decision making methods of LINMAP, TOPSIS, and Shannon’s entropy theory. Optimal results obtained from each decision making theory are compared with respect to their corresponding deviation indexes and the best one is preferred.  A sensitivity analysis is then performed to study the variational influences of some design parameters on the considered objective functions. It is observed that selected design variables has a signficant effect on  problem objectives.       

Kaynakça

  • Amini M, Bazargan M (2014). Two objective optimization in shell-and-tube heat exchangers using genetic algorithm. Appl Therm Eng 69, 278-285.
  • Arora R, Kaushik SC, Kumar R, Arora R (2016). Soft computing based multi objective optimization of Brayton cycle power plant with isothermal heat addition using evolutionary algorithm and decision making. Appl Soft Comput 46, 267 – 283.
  • Arora R, Kaushik SC, Arora R (2016). Thermodynamic modelling and multi-objective optimization of two stage thermoelectric generator in electrically series and parallel configuration. Appl Therm Eng 103, 1312-1323.
  • Askarzadeh A (2014). Bird mating optimizer: An optimization algorithm inspired by bir mating strategies. Commun Nonlinear Sci 19,1213-1228.
  • Asadi M, Song Y, Sunden G, Xie G (2014). Economic optimization design of shell and tube heat exchangers by a cuckoo search algorithm. Appl Therm Eng 73, 1032-1040.
  • Brest J, Greiner S, Boscovic B, Mernik M, Zumer V (2006). Self adaptive control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE T Evol Comput 10, 646-657.
  • Civicioğlu P (2013). Artifical cooperative search algorithm for numerical optimization problems. Inform Sci 229, 58-76.
  • Civicioglu P (2013). Backtracking Search Optimization Algorithm for numerical optimization problems. Appl Math Comput 219, 8121-8144.
  • Civicioglu P (2012). Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Comput Geo 46, 229 – 247.
  • Edwards MF, Changal AA, Parott DL (1974). Heat transfer and pressure drop characteristics of a plate heat exchanger using Newtonian and non-Newtonan liquids. Chem Eng 286-293.
  • Erol OK, Eksin I (2006). A new optimization method: Big Bang – Big Crunch. Adv Eng Softw. 37, 106-111.
  • Fettaka S, Thibault J, Gupta Y (2013). Design of shell and tube heat exchangers using multi objective optimization. Int J Heat Mass Transfer. 60, 343-354.
  • Focke WW (1986). Selecting optimum plate heat exchanger surface pattern. J Heat Transfer 108, 153-160.
  • Galeazzo FCC, Miura RY, Gut JAW, Tadini CC (2006). Experimental and numerical heat transfer in a plate heat exchanger. Chem Eng Sci 61, 7133-7138.
  • Gamperle R, Müller SD, Koumoutsakos P (2002). A parameter study for differential evolution. In: Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, WSEAS Press, Interlaken, Switzerland, pp. 293-298.
  • Gandomi AH, Yang XS (2012). Evolutionary boundary constraint handling scheme. Neural Comput Appl 21, 1449-1462.
  • Geem ZW, Kim JH, Loganthan GV (2001). A new heuristic optimization algorithm: Harmony Search. Simulation 76, 60 – 68.
  • Gherasim I, Taws M, Galanis N, Nguyen CT (2011). Heat transfer and fluid flow in a plate heat exchanger part I. Experimental investigation. Int J Therm Sci 50, 1492-1498.
  • Gong W, Fialho A, Cai Z, Li H (2011). Adaptive strategy selection in differential evolution for numerical optimization:an empirical study. Inform. Sci. 181, 5364-5386.
  • Guo J, Cheng L, Xu M (2009). Optimization design of shell and tube heat exchanger by entropy generation minimization and genetic algorithm. Appl Therm Eng 29, 2954-2960.
  • Gut JAW, Pinto JM (2004). Opitmal configuration design for plate heat exchangers. Int J Heat Mass Transfer 47, 4833-4848.
  • Gut JAW, Fernandes R, Pinto JM, Tadini CC (2004). Thermal model validation of plate heat exchangers with generalized configurations. Chem Eng Sci 59, 4591 – 4600.
  • Hadidi A, Nazari A (2013). Design and economic optimization of shell and tube heat exchangers using biogeography based (BBO) algorithm. Appl Therm Eng 51, 1263 – 1272.
  • Hadidi A, Hadidi M, Nazari A (2013). A new design approach for shell and tube heat exchangers using imperialist competitive algorithm (ICA) from economic point of view. Energy Convers Manage 67, 66-74.
  • Hajabdollahi F, Hajabdolahi Z, Hajabdollahi H (2013). Optimum design of gasket plate heat exchanger using multimodal genetic algorithm. Heat Transfer Res 44,1-19.
  • Han XH, Cui LQ, Chen SJ, Chen GM, Wang Q (2010). A numerical and experimental study of chevron, corrugated plate exchangers. Int Comm Heat Mass Transfer 37, 1008-1014.
  • Jassim EW, Newell TA, Chato JC (2006). Refrigerant pressure drop in chevron and bumpy style flat plate heat exchangers. Exp Therm Fluid Sci 30, 213-222.
  • Jia G, Wang Y, Cai Z, Jin Y (2013). An improved (µ+ ) – constraind differential evolution for constrained optimization. Inform Sci 222, 302-322.
  • Kakaç S, Liu H, Pramuanjaroenkij A (2012). Heat exchangers: selection, rating, and thermal design. Taylor& Francis, Boca Raton.
  • Kandlikar SG, Shah RK (1989). Multi pass heat exchangers effectiveness-NTU results and guidelines for selecting pass arrangements. J Heat Transfer 111, 300-313.
  • Kennedy J, Eberhart R (1995). Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, 1942 – 1948.
  • Khosravi R, Khosravi A, Nahavandi S, Hajabdollahi H (2015). Effectiveness of evolutionary algorithms for optimization of heat exchangers. Energy Convers Manage 89, 281 – 288.
  • Kumar R, Kaushik SC, Kumar R, Hans R (2016). Multi-ojective thermodynamic optimization of an irreversible regenerative Brayton cycle using evolutionary algorithm and decision making. Ain Shams Eng J 7, 741-753.
  • Kumar H (1984). The plate heat exchanger: construction and design. In: 1st UK National Conference on Heat Transfer, University of Leeds, 3-5 July, Institution of Chemical Engineers Symposium Series, pp.1275 – 128.
  • Liang J, Suganthan PN (2005). Dynamic multi-swarm particle swarm optimizer. In:Proceedings 2005 IEEE Swarm Intelligence Symposium, IEEE, pp. 124-129.
  • Luan Z., Zhang G, Tian M, Fan M (2008). Flow resistance and heat trasfer characteristics of a new type plate heat exchanger. J Hydrodyn Ser B 20, 524-529.
  • Martin H (1996). A theoretical approach to predict the performance of chevron-type plate heat exchangers. Chem Eng Process: Process Intens 35, 301-310.
  • May R (1976). Simple mathematical models with very complicated dynamics. Nature 261, 459 – 467.
  • Mirjalili S (2015). Moth-flame optimization algorithm algorithm: A novel nature-inspired heuristic paradigm. Knowl-Based Syst 89, 228 – 249.
  • Mirjalili S, Mirjalili SM, Hatamlou A (2016). Multi-Verse Optimizer: a nature inspired algorithm for global optimization. Neural Comput & Applic 27, 495 – 513.
  • Mohanty DK (2016). Application of firefly algorithm for design optimization of a shell and tube heat exchanger from economic point of view. Int J Therm Sci 102, 228 -238.
  • Mohanty DK (2016). Gravitational search algorithm for economic optimization design of a shell and tube heat exchanger. Appl Therm Eng 107, 184 – 193.
  • Najafi H, Najafi B (2010). Multi-objective optimziation of a plate and frame heat exchanger via genetic algorithm. Heat Mass Transfer. 46, 639-647.
  • Novoa-Hernandez P, Cruz-Corona C, Pelta DA (2013). Self-adaptive, multipopulation differential evolution in dynamic environments. Soft Comput 17, 1861-1881.
  • Patel VK, Rao RV (2010). Design optimization of shell-and tube heat exchanger using particle swarm optimization technique. Appl Therm Eng 30, 1417-1425.
  • Patel V, Savsani V (2014). Optimization of a plate –fin heat exchanger deign through an improved multi objective teaching learning based optimization (MO-ITLBO) algorithm. Chem Eng Res Des 92, 2371 – 2382.
  • Ponce-Ortega JM, Serna-Gonzalez M, Jimenez-Gutierrez A (2009). Use of genetic algorithms for the optimal design of shelland tube heat exchangers. Appl Therm Eng 29, 203 – 209.
  • Qin AK, Huang VL, Suganthan PN (2009). Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE T Evol Comput 13, 398-417.
  • Rao RV, Patel V (2013). Multi objective optimization of heat exchangers using a modified taching learning based optimization algorithm. Appl Math Model 37, 1146 – 1162.
  • Rao RV, Waghmare GG (2015). Multiobjective design optimization of a plate fin heat sink using a teaching learning based optimization algorithm. Appl Therm Eng 76, 521-529.
  • Rönkkönen K, Kukkonen S, Price KV (2005). Real-parameter optimization with differential evolution. In: IEEE Congression Evolutionary Computation, pp.506-513.
  • Sahin AS, Kılıç B, Kılıç U (2011). Design and economic optimization of shell and tube heat exchangers using artificial bee colony (ABC) algorithm. Energy Convers Manage 52, 3356 – 3362.
  • Selbas R, Kızılkan O, Reppich M (2006). A new design approach for shell-and-tube heat exchangers using genetic algorithms from economic point of view. Chem Eng Process: Process Intens 45, 268-275.
  • Storn R, Price K (1997). Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J Global Opt 11, 341- 359.
  • Storn R (1996). On the usage of differential evolution for function optimization. In: Biennial Conference of the North American Fuzzy Information Processing Society (NAFIPS), IEEE, pp.519-523.
  • Sun J, Feng B, Xu WB (2004). Particle swarm optimization with particles having quantum behavior. IEEE Proceedings of Congress on Evolutionary Computation, pp. 325-331.
  • Sun J, Xu W, Feng B (2004). A global search strategy of quantum behaved particle swarm optimization. Cybernetics and Intelligent Systems Proceedings of the 2004, IEEE Conference, pp. 111-116.
  • Tiwari AK, Ghosh P, Sarkar J (2013). Heat transfer and pressure drop characteristics of CeO2/water nanofluid in plate heat exchanger. Appl Therm Eng 57, 24-32.
  • Tiwari AK, Ghosh P, Sarkar J (2013). Performance comparison of the plate heat exchanger using different nanofluids. Exp Therm Fluid Sci 49, 141-151.
  • Turgut OE, Coban MT (2016). Thermal design of spiral heat exchangers and heat pipes through global best algorithm. Heat Mass Transfer. doi:10.1007/s00231-016-1861-y
  • Wang L., Sunden B (2003). Optimal design of plate heat exchangers with and without pressure drop specifications. Appl Therm Eng 23, 295-311.
  • Yadav P, Kumar R, Panda SK, Chang CS (2012). An Intelligent Tuned Harmony Search algorithm for optimisation. Inform Sci 196, 47 – 72.
  • Yang XS (2010). A new metaheuristic bat-inspired algorithm. In: Nature Inspired Cooperative Strategies for Optimization (NISCO 2010) Studies in Computational Intelligence, pp. 65-74.
  • Yu WJ, Zhang J (2011). Multi population differential evolution with adaptive parameter control for global optimization. In: Gecco-2011: Proceedings of the 13th Annual Genetic and Evolutionary Computation Conference, pp.1093-1098.
  • Zaleski T, Klepacka K (1992). Plate heat exchanger method of calculations,charts and guidelines for selecting plate heat exchanger configurations. Chem Eng Process: Process Intens 31, 45-56.
  • Zhang J, Ding X (2011). A multi swarm self-adaptive and cooperative particle swarm optimization. Eng Appl Artif Intel 24, 958-967.
  • Zhao SZ, Suganthan PN, Pan QK, Tasgetiren MF (2011). Dynamic multi-swarm particle swarm optimizer with harmony search. Expert Syst Appl 38, 3735-3742.
Toplam 67 adet kaynakça vardır.

Ayrıntılar

Bölüm Makaleler
Yazarlar

Oguz Emrah Turgut

Yayımlanma Tarihi 13 Haziran 2017
Gönderilme Tarihi 26 Kasım 2016
Yayımlandığı Sayı Yıl 2017 Cilt: 7 Sayı: 1

Kaynak Göster

IEEE O. E. Turgut, “Multi-objective thermal design optimization of plate frame heat exchangers through Global Best Algorithm”, Bitlis Eren University Journal of Science and Technology, c. 7, sy. 1, ss. 33–73, 2017, doi: 10.17678/beuscitech.322141.