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- W2375793018 abstract "Traditional optimization methods are unable to deal with the multidimensional non-linear optimization problem which involves a great number of discrete variables and continuous variables. In order to cope with this situation, a self-organizing learning algorithm (SLA)is proposed, in which the parallel search strategy of genetic algorithm and the serial search strategy of simulated annealing algorithm are involved. Additionally, the learning principle of particle swarm optimization and the tabu search strategy are involved in the SLA, wherein the integrated frame work is different from the traditional optimization methods and the interactive learning strategy is involved in the process of random searching. The SLA is divided into two handling courses: self-learning and interdependent-learning. The local optimal solution will be achieved through the self-learning in the process of local searching and the globally optimal solution will be achieved via the interdependent learning based on the mechanism for information sharing. The search strategy and controlled parameters of the SLA are adaptively fixed according to the feedback information from interactive learning with the environment, thus the SLA is selforganizing and intelligent. Experiments for the multidimensional test functions show that SLA is superior to other optimization methods." @default.
- W2375793018 created "2016-06-24" @default.
- W2375793018 creator A5070828394 @default.
- W2375793018 date "2010-01-01" @default.
- W2375793018 modified "2023-09-25" @default.
- W2375793018 title "Self-organizing learning algorithm for multidimensional optimization applications" @default.
- W2375793018 hasPublicationYear "2010" @default.
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