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《Machinery Design & Manufacture》 2015-08
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Optimization of Cleaning Path for Heat Exchange Tube Based on an Adaptive Multiple-Population Genetic Algorithms

PAN Hai-hong;GONG Jian-gang;CHEN Lin;TU Xiao-xiao;College of Mechanical Engineering,Guangxi University;  
To improve cleaning efficiency,localization traveling time of cleaning robot for heat exchange tube of sugar evaporators was studied. Based on analyzing influencing factors of total localization traveling time,taking total resultant displacement as optimization objective,cleaning path optimization mathematical model whose decision variables were the robot's configuration at every tube and sequences of cleaning for tube were constructed with robot joint resolution as the constrained condition. To prevent premature convergence of GA,an adaptive multiple-population genetic algorithm was applied to optimize cleaning path. 84 tubes were used for simulation tests and results showed that total resultant displacement adaptive multiple-population genetic algorithm got than that of GA and multiple-population genetic algorithm of fixed crossover and mutation probability got respectively reduced 39.77% and 20%,comparing with multiple-population genetic algorithm of fixed crossover and mutation probability,generation that the adaptive multiple-population genetic algorithm needed also from 1221 reduced to 1142. Thus,the adaptive multi-population genetic algorithm both in the global search ability and convergence speed achieve better results,optimized cleaning path provides the basis for the cleaning robot control.
【Fund】: 广西自然科学基金(2012GXNSFAA053200);; 南宁市科技攻关(20141021)
【CateGory Index】: TS243.3;TP18
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