Multi-objectiveevolutionaryalgorith

Multi-objectiveevolutionaryalgorithmbasedondecomposition(MOEA/D)providesanexcellent
algorithmic frameworkforsolvingmulti-objectiveoptimizationproblems.Itdecomposesatarget
problem intoasetofscalarsub-problemsandoptimizesthemsimultaneously.Duetoitssimplicity
and outstandingperformance,MOEA/Dhasbeenwidelystudiedandapplied.However,forsolvingthe
multi-objectivevehicleroutingproblemwithtimewindows(MO-VRPTW),MOEA/Dfacesadifficulty
that manysub-problemshaveduplicatedbestsolutions.Itiswell-knownthatMO-VRPTWisa
challenging problemandhasveryfewParetooptimalsolutions.Toaddressthisproblem,anovel
selection operatorisdesignedinthisworktoenhancetheoriginalMOEA/DfordealingwithMO-VRPTW.
Moreover,threelocalsearchmethodsareintroducedintotheenhancedalgorithm.Experimentalresults
indicate thattheproposedalgorithmcanobtainhighlycompetitiveresultsonSolomon'sbenchmark
problems. Especiallyforinstanceswithlongtimewindows,theproposedalgorithmcanobtainmore
diversesetofnon-dominatedsolutionsthantheotheralgorithms.Theeffectivenessoftheproposed
selection operatorisalsodemonstratedbyfurtheranalysis.

ผลลัพธ์ (ไทย) 1:

หลาย objectiveevolutionaryalgorithmbasedondecomposition(MOEA/D) providesanexcellentframeworkforsolvingmulti algorithmic-objectiveoptimizationproblems Itdecomposesatargetปัญหา intoasetofscalarsub-problemsandoptimizesthemsimultaneously Duetoitssimplicityและ outstandingperformance,MOEA/Dhasbeenwidelystudiedandapplied.However,forsolvingtheหลาย objectivevehicleroutingproblemwithtimewindows(MO-VRPTW), MOEA/Dfacesadifficultyที่ manysub-problemshaveduplicatedbestsolutions Itiswell-knownthatMO-VRPTWisaproblemandhasveryfewParetooptimalsolutions.Toaddressthisproblem,anovel ท้าทายเลือก operatorisdesignedinthisworktoenhancetheoriginalMOEA/DfordealingwithMO-VRPTWนอกจากนี้ threelocalsearchmethodsareintroducedintotheenhancedalgorithm Experimentalresultsบ่งชี้ thattheproposedalgorithmcanobtainhighlycompetitiveresultsonSolomon'sbenchmarkปัญหา Especiallyforinstanceswithlongtimewindows, theproposedalgorithmcanobtainmorediversesetofnon-dominatedsolutionsthantheotheralgorithms Theeffectivenessoftheproposedเลือก operatorisalsodemonstratedbyfurtheranalysis

ผลลัพธ์ (ไทย) 2:

Multi-objectiveevolutionaryalgorithmbasedondecomposition (MOEA / D) providesanexcellent
อัลกอริทึม frameworkforsolvingmulti-objectiveoptimizationproblems.Itdecomposesatarget
ปัญหา intoasetofscalarsub-problemsandoptimizesthemsimultaneously.Duetoitssimplicity
และ manysub-problemshaveduplicatedbestsolutions.Itiswell-knownthatMO-VRPTWisa ที่ท้าทาย problemandhasveryfewParetooptimalsolutions.Toaddressthisproblem, anovel เลือก thattheproposedalgorithmcanobtainhighlycompetitiveresultsonSolomon'sbenchmark ปัญหา operatorisalsodemonstratedbyfurtheranalysis

ผลลัพธ์ (ไทย) 3:

หลาย objectiveevolutionaryalgorithmbasedondecomposition ( moea / D ) providesanexcellent
ขั้นตอนวิธี frameworkforsolvingmulti objectiveoptimizationproblems . itdecomposesatarget
ปัญหา intoasetofscalarsub problemsandoptimizesthemsimultaneously . duetoitssimplicity และ moea outstandingperformance
,
/ dhasbeenwidelystudiedandapplied อย่างไรก็ตาม forsolvingtheหลาย objectivevehicleroutingproblemwithtimewindows ( mo-vrptw ) moea / dfacesadifficulty
ที่ manysub problemshaveduplicatedbestsolutions . itiswell knownthatmo vrptwisa
ท้าทาย problemandhasveryfewparetooptimalsolutions . toaddressthisproblem anovel
, การ operatorisdesignedinthisworktoenhancetheoriginalmoea / dfordealingwithmo vrptw .
นอกจากนี้threelocalsearchmethodsareintroducedintotheenhancedalgorithm . experimentalresults
indicate หนังสือพิมพ์ thattheproposedalgorithmcanobtainhighlycompetitiveresultsonsolomon'sbenchmark
. especiallyforinstanceswithlongtimewindows theproposedalgorithmcanobtainmore
, diversesetofnon dominatedsolutionsthantheotheralgorithms . theeffectivenessoftheproposed
เลือก operatorisalsodemonstratedbyfurtheranalysis .