Probability Distribution-Guided Water Flow-like Algorithm for Continuous Optimization: Application to Feed-forward Neural Network Training
| Autor: | Yu-Hong Lin 林育宏, 林育宏 |
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| Rok vydání: | 2015 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 103 Water flow-like algorithm (WFA) is inspired by the nature of water flow during circulating in the physical space. Initailly, WFA was developed to be a heuristic algorithm for combinatorial optimization. Thanks to WFA’s underlying ideal, this work propose a novel version of WFA for continuous optimization, called probability distribution-guided water flow-like algorithm (PWFA). In PWFA, basins are conceptualized as subspaces in the solution space, which help subflows to stochastically move toward the lowest position (the global optimum). To imitate the behavior of water flow heuristically, the flows perform spltting and moving, merging and precipitation operation to traverse in the space. Moreover, for evaluating PWFA’s performance, a large set of benchmark test functions and other basic optimization techiques from the literature are adopted for numerical test. In addition, the application to the training of feed-forward neural network (FNN) for pattern classification is also present as a test case for this algorithm. For the reason, a system prototype for solving continuous optimization problem and FNN parameter optimization is implemented by this work. The results show, first, that PWFA has a better performance than other optimization methods on uni-modal functions and multi-modal functions with single one optimum, and second, that this algorithm represents a competitive performance to other basic methods as solving multi-modal functions with many optimums. Additionally, the results of the application show PWFA is comparable to several optimization techniques included as well. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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