Based on the goal of maximizing input and output of resources development department and final product supply department, a regularized decision is made for the development of non-renewable resources. The Lagrange rel...
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Based on the goal of maximizing input and output of resources development department and final product supply department, a regularized decision is made for the development of non-renewable resources. The Lagrange relaxation algorithm based on asynchronous subgradient method is used to solve the growth rate of price and output of non-renewable resources under steady state, and the numerical simulation and sensitivity analysis are carried *** innovation plays a decisive role in the regularization development of non-renewable resources. The constraints of resource development priority, production constraints and net present value of production in the final product supply department and resource development department will significantly affect the growth rate of non-renewable resources output. Accordingly, a management strategy for developing regularization and rational allocation of non-renewable resources is put forward.
This paper presents a human-computer cooperation improved ant colony optimization (HCCIACO) algorithm for ship pipe route design (SPRD). SPRD is a conbinatorial optimization problem with various performance constraint...
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This paper presents a human-computer cooperation improved ant colony optimization (HCCIACO) algorithm for ship pipe route design (SPRD). SPRD is a conbinatorial optimization problem with various performance constraints, its hard to fmd an effective solution only by computer. Based on the human-computer cooperation theory, the HCCIACO algorithm takes full advantage of designers' expertise and experience as well as computers' calculation ability. It conbines the artificial sulotion and algorithm solution in the genetic sense of the improved ant colony optimization (IACO) algorithm so that the optimization approach for SPRD in three-dimensional space can be obtained. The improved ant colony optimization simplifies the problem by reducing the complexity in calculation and engineering to some extent. Meanwhile, it guides the algorithm to search effectively for the stable solution which satisfies the engineering requirements. in this paper, the structure and updating method of artificial solution as well as the combination mode of artificial solution and algorithm solution have been researched. Compare with the conventional mathod, HCCIACO algorithm not only improves the convergence speed, but also improves the quality of the solution. Finally, the simulation results demonstrate the feasibility and efficiency of the proposed algorithm.
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