Shuffled complex evolution approach for effective and efficient global minimization
Journal of Optimization Theory and Applications
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Paper: The parallel genetic algorithm as function optimizer
Parallel Computing
Letters: A variant of P systems for optimization
Neurocomputing
Parameter estimation of water quality model using particle swarm optimization technique
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Telescoping strategies for improved parameter estimation of environmental simulation models
Computers & Geosciences
| Hi-index | 0.98 |
The standard binary-coded genetic algorithm (GA) has been improved using the three strategies of automatic search space shifting to achieve hill-climbing, automatic search space reduction to effect time-tuning, and the use of independent subpopulation searches coupled with shuffling to deal with the occurrence of multiple regions of attraction. The degrees of search space shifting and reduction are determined by the distribution of the best parameter values in the previous generations and are implemented after every specified number of generations. If the best parameter value in successive generations is clustering in a small part of the search range, a higher level of range reduction is used. The search shift is based on the deviation from the middle of the current search range of the best parameter values of a specified number of previous generations. With each independent subpopulation, a search is performed until an optimum is reached. Shuffling is then performed and new subpopulation search spaces are obtained from the shuffled subpopulations. The improved GA performs remarkably better than the standard GA with three global optimum location problems. The standard GA achieves 11% success with the Hartman function and fails totally with the SIXPAR rainfall-runoff model calibration and the Griewank function while the improved GA effectively locates the global optima. Taking the number of function evaluations used to locate the global optimum as a measure of efficiency, the improved GA is about two times less efficient, three times more efficient, and 34 times less efficient than the shuffled complex evolution (SCE-UA) method for the SIXPAR rainfall-runoff model calibration, the Hartman function, and the Griewank function, respectively. The modified GA can therefore be considered effective but not always efficient.