A Similarity Evaluation Framework for Multiobjective Multirobot Maritime Patrolling

Abstract

Considering the characteristics of Internet-of-Things enabled multi-robot maritime patrolling problems which are not reflected in general multi-objective optimization benchmarks, this work proposes a similarity evaluation framework to enhance the diversity when the population evolves. A novel masking thinking is proposed to eliminate the effects of dominant and highly correlated genetic positions and thus magnify the impacts of the rest which can distinguish individuals more effectively. The perspective from objective space is also considered to maintain a desired balance between diversity and convergence. With the use of the proposed similarity evaluation framework, individuals contributing more to the diversity can be selected to the next generation and further evolved. It is beneficial to prevent a population from being trapped by local optima and thus explore better Pareto-optimal solutions. The effectiveness of the proposed framework is validated by ablation experiments. Comparisons to the state of the art are also conducted to illustrate its advantages.