教師著作
Permanent URI for this collectionhttp://rportal.lib.ntnu.edu.tw/handle/20.500.12235/31268
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Item Global Optimization Using Novel Randomly Adapting Particle Swarm Optimization Approach(2011-10-12) Nai-Jen Li; Wen-June Wang; Chen-Chien Hsu; Chih-Min LinThis paper proposes a novel randomly adapting particle swarm optimization (RAPSO) approach which uses a weighed particle in a swarm to solve multi-dimensional optimization problems. In the proposed method, the strategy of the RAPSO acquires the benefit from a weighed particle to achieve optimal position in explorative and exploitative search. The weighed particle provides a better direction of search and avoids trapping in local solution during the optimization process. The simulation results show the effectiveness of the RAPSO, which outperforms the traditional PSO method, cooperative random learning particle swarm optimization (CRPSO), genetic algorithm (GA) and differential evolution (DE) on the 6 benchmark functions.Item Particle swarm optimization incorporating simplex search and center particle for global optimization(2008-06-27) Chen-Chien Hsu; Chun-Hwui GaoThis paper proposes a hybrid approach incorporating an enhanced Nelder-Mead simplex search scheme into a particle swarm optimization (PSO) with the use of a center particle in a swarm for effectively solving multi-dimensional optimization problems. Because of the strength of PSO in performing exploration search and NM simplex search in exploitation search, in addition to the help of a center particle residing closest to the optimum during the optimization process, both convergence rate and accuracy of the proposed optimization algorithm can be significantly improved. To show the effectiveness of the proposed approach, 18 benchmark functions will be adopted for optimization via the proposed approach in comparison to existing methods.