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Abstract It was easy to fall into local optimum when using conventional quantum genetic algorithm to solved continuous function optimization. In order to surmount the above drawback, an improved multi-po- pulation quantum genetic algorithm （IMPQGA） was proposed. The algorithm divided the initial popula- tion into N sub population. Each population had updated itself respectively according to the different quantum rotation gate strategies, and then had exchanged the optimal individuals with the next sub popu- lation. At the same time, in order to jump out of local optimum in time and avoided premature conver- gence, a novel quantum rotation gate was introduced to dynamically adjust the chromosome evolution di- rection with the increasing number of evolution generation. The simulation results showed that IMPQGA had better optimization performance with the multi population quantum genetic algorithm （ MPQGA ）. Compared with the conventional quantum genetic algorithm （QGA）.
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Received: 09 January 2018
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2017, Vol. 30 
