Journal of Environmental Impact Assessment (환경영향평가)

Korean Society of Environmental Impact Assessment (한국환경영향평가학회)
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Application of multi-objective genetic algorithm for waste load allocation in a river basin

오염부하량 할당에 있어서 다목적 유전알고리즘의 적용 방법에 관한 연구

  • Cho, Jae-Heon (Department of Health and Environment, Kwandong University)
  • 조재현 (관동대학교 보건환경학과)
  • Received : 2013.11.01
  • Accepted : 2013.11.26
  • Published : 2013.12.31
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Abstract

In terms of waste load allocation, inequality of waste load discharge must be considered as well as economic aspects such as minimization of waste load abatement. The inequality of waste load discharge between areas was calculated with Gini coefficient and was included as one of the objective functions of the multi-objective waste load allocation. In the past, multi-objective functions were usually weighted and then transformed into a single objective optimization problem. Recently, however, due to the difficulties of applying weighting factors, multi-objective genetic algorithms (GA) that require only one execution for optimization is being developed. This study analyzes multi-objective waste load allocation using NSGA-II-aJG that applies Pareto-dominance theory and it's adaptation of jumping gene. A sensitivity analysis was conducted for the parameters that have significant influence on the solution of multi-objective GA such as population size, crossover probability, mutation probability, length of chromosome, jumping gene probability. Among the five aforementioned parameters, mutation probability turned out to be the most sensitive parameter towards the objective function of minimization of waste load abatement. Spacing and maximum spread are indexes that show the distribution and range of optimum solution, and these two values were the optimum or near optimal values for the selected parameter values to minimize waste load abatement.

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References

  1. 광주광역시, 2009, 제2단계 광주광역시 영산강 수질오염총량관리 기본계획.
  2. 구보영, 정일원, 김태순, 배덕효, 2006, 다목적 유전자알고리즘을 적용한 Tank 모형 매개변수 추정에 관한 연구, 2006 대한토목학회 정기학술대회, 166-169.
  3. 국립환경과학원, 2012, 수질오염총량관리 기술지침.
  4. 김태순, 정일원, 구보영, 배덕효, 2007, 다목적 유전자알고리즘을 이용한 Tank 모형 매개변수 최적화(I): 방법론과 모형구축, 한국수자원학회논문집, 40(9), 677-685. https://doi.org/10.3741/JKWRA.2007.40.9.677
  5. 김태순, 허준행, 2005, NSGA-II를 이용한 한강수계 저수지군 운영방안에 관한 연구, 대한토목학회 2005 정기학술대회, 2376-2379.
  6. 서봉균, 2010, GIE를 이용한 소득원천별 불평등 효과 분석, 사회복지연구, 41(1), 65-84.
  7. 이우성, 정성관, 2012, 공간분석을 활용한 녹지의 불균형평가 및 관리권역 설정 -녹지의 이용적 측면을 중심으로 -, 15(2), 126-138.
  8. 전라남도, 2010, 전라남도 영산강수계 제2단계 오염총량관리 기본계획.
  9. 조재현, 2013, 수질오염총량관리대상 오염심화 하천에 대한 오염부하량 할당 방법, 환경영향평가, 22(2) 157-170.
  10. 조재현, 이창훈, 2009, 영향계수법과 유전알고리즘을 이용한 QUAL2K 모형의 매개변수 최적화, 환경영향평가, 18(2) 99-109.
  11. Bhat, S. A., Gupta, S., Saraf, D. N. and Gupta, S. K., 2006, On-line optimizing control of bulk free radical polymerization reactors under temporary loss of temperature regulation: experimental study on a 1-L batch reactor, Industrial & engineering chemistry research, 45(22), 7530-7539. https://doi.org/10.1021/ie0604526
  12. Boisvert R. N., Ranney C. K., 1991, The budgetary implications of reducing U.S. income inequality through income transfer programs, Department of Agricultural Economics Cornell University.
  13. Cho, J. H., Sung, K. S., Ha, S. R., 2004, A river water quality management model for regional wastewater treatment cost using a genetic algorithm, Journal of Environmental Management, 73, 229-242. https://doi.org/10.1016/j.jenvman.2004.07.004
  14. Copal, N.R., Satyanarayana, S.V., 2011, Cost analysis for removal of VOC from water by pervaporation using NSGA-II, Desalination, 274, 212-219. https://doi.org/10.1016/j.desal.2011.02.013
  15. Deb K. 2001, Multi-objective optimization using evolutionary algorithms, Wiley, Chichester, UK.
  16. Gen, M. and Cheng, R. 1997, Genetic algorithms and engineering design, John Wiley&Sons, New York, 1-2.
  17. Goldberg, D. E., 1989, Genetic algorithms in search, optimization and machine learning, Addison-Wesley, Massachusetts.
  18. Guria C, Bhattacharya P. K., Gupta S. K., 2005, Multi-objective optimization of reverse osmosis desalination units using different adaptations of the non-dominated sorting genetic algorithm (NSGA), Computers and Chemical Engineering, 29, 1977-1995. https://doi.org/10.1016/j.compchemeng.2005.05.002
  19. Kerachian R., Karamouz M., 2007, A stochastic conflict resolution model for water quality management in reservoir-river systems, Advances in Water resources, 30, 866-882. https://doi.org/10.1016/j.advwatres.2006.07.005
  20. Lerman, R., & Yitzhaki, S., 1985, Income inequality effects by income source: A new approach and applications to the United States. The Review of Economics and Statistics, 67(1), 151-156. https://doi.org/10.2307/1928447
  21. Sankararao B., Gupta S. K., 2007, Multi-objective optimization of an industrial fluidized-bed catalytic cracking unit (FCCU) using two jumping gene adaptations of simulated annealing. Computers and Chemical Engineering, 31, 1496-1515. https://doi.org/10.1016/j.compchemeng.2006.12.012
  22. Sun T., Zhangm H., Wang Y., Meng X., Wang C., 2010, The application of environmental Gini coefficient (EGC) in allocating wastewater discharge permit: The case study of watershed total mass control in Tianjin, China, Resources, Conservation and Recycling, 54(9), 601-608 https://doi.org/10.1016/j.resconrec.2009.10.017
  23. Van Veldhuizen, D. A., and Lamont, G. B., 2000, Multiobjective evolutionary algorithms: Analyzing the state-of-art, Evolutionary Computation, 8(2), 125-147. https://doi.org/10.1162/106365600568158

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