A Strategy of Differential Evolution with Opposition-based Multi-population Parallel

Abstract

Abstract: Generally, differential evolution (DE) algorithm is easily stuck into local optima as well as suffers from low convergence accuracy when employed for optimization of heat exchanger network. To solve these issues, an opposition-based multi-population parallel differential evolution algorithm is proposed. Firstly, opposite population is built by using initial population. Then, new generation of individuals are generated through information exchange, which is produced by mutated operation between opposite population and its original correspondence. The final step is to retain evolution of multi-population in parallel by applying multi-round opposites, so that the population is enable to keep current solution information and search new solutions in a larger space as well. Computing results of improved DE algorithm on 9sp and 15sp suggests that the method improves population diversity, jumps out local optima and at the same time achieves higher speed and accuracy.

Key words: differential evolution algorithm, opposite population, multi-population, heat exchanger network

CLC Number:

TK124

DUAN Huanhuan, CUI Guomin, CHEN Jiaxing, CHEN Shang. A Strategy of Differential Evolution with Opposition-based Multi-population Parallel[J]. CHINESE JOURNAL OF COMPUTATIONAL PHYSICS, 2016, 33(5): 561-569.

References

[1] FURMAN K C, SAHINIDIS N V. A critical review and annotated bibliography for heat exchanger network synthesis in the 20th century[J]. Industrial & Engineering Chemistry Research, 2002, 41(10): 2335-2370.
[2] HUO Zhaoyi, ZHAO Liang, YIN Hongchao, et al. Simultaneous synthesis of structural-constrained heat exchanger networks with and without stream splits[J]. The Canadian Journal of Chemical Engineering, 2013, 91(5): 830-842.
[3] LINNHOFF B,HINDMARSH E. The pinch design method for heat exchanger networks[J]. Chemical Engineering Science, 1983. 38(5): 745-763.
[4] KESLER M, PARKER R. Optimal networks of heat exchange[J]. Chem Eng Progr Symp Ser, 1969.
[5] LEWIN D R. A generalized method for HEN synthesis using stochastic optimization—II.: The synthesis of cost-optimal networks[J]. Computers & Chemical Engineering, 1998, 22(10): 1387-1405.
[6] PRICE K, STORN R M, LAMPINEN J A. Differential evolution:a practical approach to global optimization[M]. Springer Science & Business Media, 2006.
[7] STOM R, PRICE K. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of Global Optimization, 1997, 11(4): 341-359.
[8] RAHNAMAYAN S, TIZHOOSH H R, SALAMA M M A. A novel population initialization method for accelerating evolutionary algorithms[J]. Computers & Mathematics with Applications, 2007, 53(10): 1605-1614.
[9] ZHANG W J, XIE X F. DEPSO: Hybrid particle swarm with differential evolution operator[C]//IEEE International Conference on Systems Man and Cybernetics, 2003, 4: 3816-3821.
[10] TASGETIREN M F, SUGANTHAN P. A multi-populated differential evolution algorithm for solving constrained optimization problem[C]//Evolutionary Computation, 2006, CEC 2006, IEEE, 2006: 33-40.
[11] RAHNAMAYAN S, TIZHOOSH H R, SALAMA M. Opposition-based differential evolution[J]. Evolutionary Computation, IEEE Transactions, 2008, 12(1): 64-79.
[12] RIVEDI K K, OEILL B K,ROACH J R, et al. A new dual-temperature design method for the synthesis of heat exchanger networks [J].Comput Chem Eng,1989,16(6):667- 685.
[13] TIZHOOSH H R. Opposition-based learning: a new scheme for machine intelligence[C]. IEEE, 2005: 695-701.
[14] SIMON D. Biogeography-based optimization[J]. Evolutionary Computation, IEEE Transactions, 2008, 12(6): 702-713.
[15] DONG M G, NIU Q Z, YANG X. Opposition-based stud genetic algorithm[J]. Computer Engineering, 2009, 35(20): 239-241.
[16] LIU B, QANG L, JIN Y H. Advances in differential evolution[J].Control and Decision, 2007,22(07): 721-730.
[17] QING A. Electromagnetic inverse scattering of multiple perfectly conducting cylinders by differential evolution strategy with individuals in groups (GDES)[J]. Antennas and Propagation, IEEE Transactions, 2004, 52(5): 1223-1229.
[18] BRIONES V, KOKOSSIS A C. Hypertargets: A conceptual programming approach for the optimization of industrial heat networks- I.grassroots design and network complexity[J]. Chemical Engineering Science, 1999(54): 519-539.
[19] AHMAD S. Heat exchanger networks: Cost trade-offs in energy and capital[D]. Manchester:Manchester Institute of Science and Technology, 1985:1-385.
[20] ZHU X, ONEILL B K, ROACH J, et al. A method for automated heat-exchanger network synthesis using block decomposition and nonlinear optimization[J]. Chemical Engineering Research & Design,1995,73:919-930.
[21] LINNHOFF B,AHMAD S. Cost optimum heat exchanger networks Ⅰ. Minimum energy and capital using simple models for capital cost[J]. Comput Chem Eng, 1990, 14 (7):729-750.
[22] WAN Y Q. Synthetic evaluation and system integration of heat exchanger networks[D]. Shanghai: University of Shanghai for Science & Technology,2014.
[23] HU X B, CUI G M, TU W M. Global optimization of filled function method for heat exchanger networks[J]. Chemical Engineering, 2011,39(1):28-31.
[24] FANG D J. Global optimization of heat exchanger networks by differential evolution method[D]. Shanghai: University of Shanghai for Science & Technology, 2014.
[25] ZHANG Q, CUI G M, GUAN X. Optimization of heat exchanger network based on Monte Carlo genetic algorithm[J]. China Petroleum Machinery, 2007,35(5):19-22.