The capacitated arc routing problem (CARP) has attracted considerable attention from researchers due to its broad potential for social applications. This paper builds on, and develops beyond, the cooperative coevolutionary algorithm based on route distance grouping (RDG-MAENS), recently proposed by Mei et al. Although Mei's method has proved superior to previous algorithms, we discuss several remaining drawbacks and propose solutions to overcome them. First, although RDG is used in searching for potential better solutions, the solution generated from the decomposed problem at each generation is not the best one, and the best solution found so far is not used for solving the current generation. Second, to determine which sub-population the individual belongs to simply according to the distance can lead to an imbalance in the number of the individuals among different sub-populations and the allocation of resources. Third, the method of Mei et al. was only used to solve single-objective CARP. To overcome the above issues, this paper proposes improving RDG-MAENS by updating the solutions immediately and applying them to solve the current solution through areas shared, and then according to the magnitude of the vector of the route direction, and a fast and simple allocation scheme is proposed to determine which decomposed problem the route belongs to. Finally, we combine the improved algorithm with an improved decomposition-based memetic algorithm to solve the multiobjective large scale CARP (LSCARP). Experimental results suggest that the proposed improved algorithm can achieve better results on both single-objective LSCARP and multiobjective LSCARP.
In this paper, we present an approach to Large-Scale CARP called Quantum-Inspired Immune Clonal Algorithm (QICA-CARP). This algorithm combines the feature of artificial immune system and quantum computation ground on the qubit and the quantum superposition. We call an antibody of population quantum bit encoding, in QICA-CARP. For this encoding, to control the population with a high probability evolution towards a good schema we use the information on the current optimal antibody. The mutation strategy of quantum rotation gate accelerates the convergence of the original clone operator. Moreover, quantum crossover operator enhances the exchange of information and increases the diversity of the population. Furthermore, it avoids falling into local optimum. We also use the repair operator to amend the infeasible solutions to ensure the diversity of solutions. This makes QICA-CARP approximating the optimal solution. We demonstrate the effectiveness of our approach by a set of experiments and by Comparing the results of our approach with ones obtained with the RDG-MAENS and RAM using different test sets. Experimental results show that QICA-CARP outperforms other algorithms in terms of convergence rate and the quality of the obtained solutions. Especially, QICA-CARP converges to a better lower bound at a faster rate illustrating that it is suitable for solving large-scale CARP.
High speed cutting process is a very complicated process; cutting parameters have a significant effect on cutting process and play a key role in the process of product manufacturing. The overall scheme of high speed cutting parameter optimization and its fault diagnosis have been introduced. The mathematical model of the selected cutting parameters was established and the optimized parameters were obtained by combining the experimental design with the technology of data processing. The statistical description of high speed cutting process control was introduced and the fault diagnosis model of cutting parameter optimization by using the neural network was proposed. Finally, the mathematical model in the present study is validated with a numerical example. The results show that the present method solved the problem of poor universality of high speed cutting data effectively and avoided the inaccuracy of physical and chemical mechanism research. Meanwhile, the present study prevents the passive checks of the cutting and gets better diagnosis of the complicated cutting fault type.
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