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| Bi-objective optimization of network min-cost and max-flow based on genetic algorithm |
| (Computer Science and Technology College, Xi′an University of Science and Technology, Xi′an, Shaanxi 710054, China) |
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Abstract Aimed at the defect of transfering network min-cost and max-flow to single objective optimization, the bi-objective optimization model of network min-cost and max-flow was proposed, and multi-objective genetic algorithm was adopted. The flow values of remain branches were encoded and initialized by multiobjective genetic algorithm, and the flow values of tree branches were calculated by decoding and circuit matrix. Based on network min-cost and max-flow function, nodes capacity and branches capacity restrictions, the generalized bi-objective function were set up according to multi-objective optimization theory. The flow scheme codes were evaluated by the generalized bi-objective function and evolved by evolution arithmetic operators to obtain optimization mincost and max-flow schemes by iterative algorithm. Mine ventilation network was taken as example to conduct the test. The results show that the bi-objective genetic algorithm of network min-cost and maxflow is feasible and effective. The variable number is reduced in this algorithm and algorithm efficiency is improved.
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2011, Vol. 32 
