Immune Clonal Algorithm Based on Directed Evolution for Multi-Objective Capacitated Arc Routing Problem

Ronghua Shang, Bingqi Du, Hongna Ma, Licheng Jiao, Yu Xue, Rustam Stolkin

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)
230 Downloads (Pure)


The capacitated arc routing problem is playing an increasingly important role in our society, engendering increasing attention from the research community. Among the various models, multi-objective capacitated arc routing problem comes much closer to real-world problems. Therefore, this paper proposes an immune clonal algorithm based on directed evolution to solve this problem. Firstly, the proposed algorithm adopts the framework of the immune clonal algorithm and expands the scale of the initial antibody population in the initialization process to increase the diversity of the antibodies. Secondly, the proposed algorithm is combined with a decomposition strategy in the operations of the immune gene. Antibodies are classified to perform the immune genetic operations, which helps the antibody populations to share the neighborhood information in a timely manner. Thirdly, the proposed algorithm applies a novel kind of comparison operator to build the total population, which helps it to evolve in the direction of a better population and improves the quality of the antibodies. Experimental results suggest that the proposed algorithm can generate better non-dominant solutions than several compared state-of-the-art algorithms, especially for large-scale sets.
Original languageEnglish
JournalApplied Soft Computing
Early online date7 Sept 2016
Publication statusE-pub ahead of print - 7 Sept 2016


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