Quantum-Inspired Immune Clonal Algorithm for solving large-scale capacitated arc routing problems

Shang, Ronghua and Du, Bingqi and Dai, Kaiyun and Jiao, Licheng and Ghalamzan Esfahani, Amir and Stolkin, Rustam and UNSPECIFIED and UNSPECIFIED (2018) Quantum-Inspired Immune Clonal Algorithm for solving large-scale capacitated arc routing problems. Memetic Computing, 10 (1). pp. 81-102. ISSN 1865-9292

Full content URL: https://doi.org/10.1007/s12293-017-0224-7

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Abstract

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 an 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.

Keywords:Large-scale CARP, quantum rotation gate, quantum crossover operator, the repair operator
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science
ID Code:34759
Deposited On:28 Jun 2019 13:31

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