Aiming at the multi-objective optimization problem in flexible operations,a multi-objec-tive task satisfaction mathematical model is first constructed.The model takes the minimum machining time,the lowest manufacturing cost and the shortest transportation time as its objectives,and uses the geo-metric average method to solve the comprehensive satisfaction evaluation value after dedimensional opera-tion.Secondly,an improved Logistic chaotic map and flower pollination mechanism employed in particle swarm optimization(LFPSO)is proposed.The inertia weight of the algorithm adopts power function adap-tive adjustment to balance global search ability and local search ability.In order to increase the search per-formance of particle swarm in the early stage,Logistic chaos mapping is added to the inertia weight to en-rich particle diversity.In order to balance global search ability and local search ability,pollen pollination mechanism is introduced as the global search threshold.Finally,the LFPSO algorithm is simulated and compared with the other three algorithms,and the results verify that the LFPSO algorithm has good per-formance and the effectiveness of solving the multi-objective optimization problem of flexible operation.
关键词
粒子群算法/满意度评价值/Logistic混沌映射/花粉授粉阈值/惯性权重幂函数
Key words
particle swarm optimization/satisfaction evaluation value/Logistic chaos mapping/pollina-tion threshold/inertial weight power function
维普
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