Stochastic partially optimized cyclic shift crossover for multi-objective genetic algorithms for the vehicle routing problem with time-windows

Graphical abstractDisplay Omitted HighlightsA stochastic partially optimized cyclic shift crossover (SPOCSX) is presented.BCRC and POCSX are used as reference crossovers to contrast the performances.Experiments show that SPOCSX produces higher quality solutions than POCSX.Experiments show that the execution time of SPOCSX is much lower than that of BCRC.Qualitative analysis shows the competitiveness of the solutions obtained by SPOCSX. This paper presents a stochastic partially optimized cyclic shift crossover operator for the optimization of the multi-objective vehicle routing problem with time windows using genetic algorithms. The aim of the paper is to show how the combination of simple stochastic rules and sequential appendage policies addresses a common limitation of the traditional genetic algorithm when optimizing complex combinatorial problems. The limitation, in question, is the inability of the traditional genetic algorithm to perform local optimization. A series of tests based on the Solomon benchmark instances show the level of competitiveness of the newly introduced crossover operator.