Comparative analysis of Simulated Annealing, Simulated Quenching and Genetic Algorithms for optimal reservoir operation

被引：75
|
作者：
Vasan, A. [1 ]
Raju, Komaragiri Srinivasa [2 ]
机构：
[1] Univ Western Ontario, Dept Civil & Environm Engn, London, ON N6A 5B9, Canada
[2] Birla Inst Technol & Sci, Civil Engn Grp, Pilani 333031, Rajasthan, India
来源：
APPLIED SOFT COMPUTING | 2009年 / 9卷 / 01期
关键词：
Genetic Algorithms; Simulated Annealing; Simulated Quenching; Irrigation planning; India; OPTIMIZATION; GROUNDWATER;
D O I：
10.1016/j.asoc.2007.09.002
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The present study deals with the application of non-traditional optimization techniques, namely, Simulated Annealing (SA), Simulated Quenching (SQ) and Real-coded Genetic Algorithms (RGA) to a case study of Mahi Bajaj Sagar Project, India. The objective of the study is to maximize the annual net benefits subjected to various irrigation planning constraints for 75% dependable flow scenario. Extensive sensitivity analysis on various parameters used in above techniques indicated that they yielded same solution corresponding to a set of optimal combination of parameters. It is concluded that SA, SQ and RGA can be utilized for efficient planning of any irrigation system with suitable modi. cations. (C) 2008 Elsevier B.V. All rights reserved.
引用
收藏
页码：274 / 281
页数：8
相关论文
共 50 条
  • [1] Optimal Operation of Reservoir Systems using Simulated Annealing
    Ramesh S. V. Teegavarapu
    Slobodan P. Simonovic
    Water Resources Management, 2002, 16 : 401 - 428
  • [2] Optimal operation of reservoir systems using simulated annealing
    Teegavarapu, RSV
    Simonovic, SP
    WATER RESOURCES MANAGEMENT, 2002, 16 (05) : 401 - 428
  • [3] Method of reservoir optimal operation based on improved simulated annealing genetic algorithm
    Li, Chenming
    Xu, Baohua
    Gao, Hongmin
    Yin, Xueying
    Xu, Lizhong
    Sensors and Transducers, 2013, 159 (11): : 160 - 166
  • [4] A comparative study of simulated annealing and genetic algorithms
    Gomez, A
    de la Fuente, D
    Bautista, J
    Pereira, J
    Ferrer, L
    IC-AI '04 & MLMTA'04 , VOL 1 AND 2, PROCEEDINGS, 2004, : 3 - 9
  • [5] Simulated annealing and genetic algorithms for optimal regression testing
    Mansour, Nashat
    El-Fakih, Khalid
    Journal of Software Maintenance, 1999, 11 (01): : 19 - 34
  • [6] Simulated annealing and genetic algorithms for optimal regression testing
    Mansour, N
    El-Fakih, K
    JOURNAL OF SOFTWARE MAINTENANCE-RESEARCH AND PRACTICE, 1999, 11 (01): : 19 - 34
  • [7] STOCHASTIC RESERVOIR MODELING USING SIMULATED ANNEALING AND GENETIC ALGORITHMS
    SEN, MK
    DATTAGUPTA, A
    STOFFA, PL
    LAKE, LW
    POPE, GA
    SPE FORMATION EVALUATION, 1995, 10 (01): : 49 - 55
  • [8] Stochastic reservoir modeling using simulated annealing and genetic algorithms
    Sen, Mrinal K., 1600, Society of Petroleum Engineers (SPE), Richardson, TX, United States (10):
  • [9] Genetic algorithms and simulated annealing for robustness analysis
    Zhu, XY
    Huang, Y
    Doyle, J
    PROCEEDINGS OF THE 1997 AMERICAN CONTROL CONFERENCE, VOLS 1-6, 1997, : 3756 - 3760
  • [10] Comparative Analysis of Genetic Algorithms and Particle Swarm Optimization Algorithms for Optimal Reservoir Operation
    Yun, Ruan
    ADVANCES IN CIVIL ENGINEERING, PTS 1-4, 2011, 90-93 : 2727 - 2733
12345