A frequency-based parent selection for reducing the effect of evaluation time bias in asynchronous parallel multi-objective evolutionary algorithms

Parallel evolutionary algorithms (PEAs) have been studied for reducing the execution time of by utilizing parallel computing. An asynchronous PEA (APEA) is a scheme PEAs that increases computational efficiency generating new solution immediately after evaluation completes without idling computing nodes. However, because APEA gives more search opportunities to solutions with shorter times, bias negatively affects performance. To overcome this drawback, paper proposes parent selection method reduce effect in APEAs. The proposed considers frequency and selects so progress population uniform regardless bias. This conducts experiments on multi-objective optimization problems simulate use NSGA-III, well-known algorithm, compare conventional synchronous/asynchronous parallelization. experimental results reveal can while NSGA-III.