An Immune Algorithm with Dual-level Ranking Mutation for Large-scale Multi-objective Multi-robot Maritime Patrolling

Multi-robot patrolling systems have been studied for guaranteeing maritime safety under severe weather conditions. Their patrolling path planning problem can be established as a large-scale multi-objective optimization problem (LSMOP) due to conflicting objectives and huge numbers of decision variables. Currently, evolutionary algorithms based on divide-and-conquer strategies and dimensionality reduction are frequently used methods to solve such problems. However, the ways that they seek to simplify the problems may ignore the characteristics of decision variables and relevance among them. In this work, we propose a novel mutation operator based on a non-dominated ranking and TextRank-inspired ranking, which forms a dual-level ranking mutation, to enhance the optimization performance of our previous immune algorithm. The new improved algorithm can guide population to mutate towards a more favorable direction by means of the non-dominated ranking and investigate correlations among individuals and genetic positions inspired by TextRank. Comparisons to the state of the art on both of the concerned problem and benchmarks illustrate the desired performance of our proposed method.