摘要
Shuffled frog leaping algorithm( SFLA) was used to solve multi-objective sequencing problem of mixed model assembly line( MMAL). Local convergence can be avoided and optimal solution can be obtained to a certain extent. However,the multi-objective sequencing problem of MMAL is an non-deterministic polynomial hard( NP-hard) problem and the shortcomings are slow convergence rate and low precision. To solve the shortcomings for optimization objectives of minimizing total utility time and keeping average consumption rate of parts, a chaos differential evolution SFLA( CDESFLA) is proposed in this study. Because SFLA is easy to fall into local optimum,the evolution operator of differential evolution algorithms is introduced in SFLA as a local search strategy,and differential mutation operator is introduced in chaotic sequence to prevent premature convergence. The examples show that the proposed CDESFLA is better for convergence accuracy than SFLA,genetic algorithm( GA) and particle swarm optimization( PSO)
Shuffled frog leaping algorithm（ SFLA） was used to solve multi-objective sequencing problem of mixed model assembly line（ MMAL）. Local convergence can be avoided and optimal solution can be obtained to a certain extent. However,the multi-objective sequencing problem of MMAL is an non-deterministic polynomial hard（ NP-hard） problem and the shortcomings are slow convergence rate and low precision. To solve the shortcomings for optimization objectives of minimizing total utility time and keeping average consumption rate of parts, a chaos differential evolution SFLA（ CDESFLA） is proposed in this study. Because SFLA is easy to fall into local optimum,the evolution operator of differential evolution algorithms is introduced in SFLA as a local search strategy,and differential mutation operator is introduced in chaotic sequence to prevent premature convergence. The examples show that the proposed CDESFLA is better for convergence accuracy than SFLA,genetic algorithm（ GA） and particle swarm optimization（ PSO）.
作者
赵小强
季树荣
ZHAO Xiaoqiang;JI Shurong(College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China;Key Laboratory of Advanced Control of Industrial Process in Gansu Province, Lanzhou 730050, China;National Experimental Teaching Center for Electrical and Control Engineering of Lanzhou University of Technology, Lanzhou 730050, China)
基金
National Natural Science Foundation of China(o.61370037)
