Abstract
Logistics routing problem is a typical NP hard problem, which is very difficult to solve accurately. On the basis of establishing logistics path optimization model, an immune clone algorithm is proposed. To improve the accuracy of search algorithms, the clonal selection and high frequency variations in the immune algorithm method are introduced. Then the antibody encoding virtual distribution point algorithm is designed to improve search efficiency. The benchmark problem of logistics delivery path optimization is simulated and analyzed. Experimental results show that the proposed immune cloning algorithm expands the range of population search and it have obvious advantages in solving large-scale complex physical distribution optimization problems. Also, the proposed algorithm can solve the optimal distribution of logistics effectively.
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