Application Of Decomposition Evolutionary Algorithm In The Allocation Of Inter-organizational Boundaries' Logistics Resource

In real life,people are the pursuit of the smallest cost,the largest income,the two aspects are often conflicting.In essence,this is a typical multi-objective optimization problem.Multi-objective optimization problem refers to the need to deal with multiple conflicting and mutually constrained multiple targets at the same time.The final solution set is not unique,but a series of approximate Pareto optimal solutions.Because the evolutionary algorithm can provide a set of Pareto optimal solutions by once running and is not affected by the mathematical properties of the objective function,the use of evolutionary algorithm to solve the multi-objective optimization problem has become a hotspot in recent years.In this paper,we propose a new method to adjust the size of the neighborhood by adjusting the diversity and convergence of the population in the evolution of the algorithm when the global replacement is introduced in MOEA/D.And into the MOEA/D,and then propose an improved decomposition class multi-objective evolutionary algorithm.In order to verify the effectiveness of the strategy,a series of simulation comparison experiments,the main work around the following aspects:1.Introduce the optimal allocation of logistics resources across the organizational boundaries,and give a mathematical definition of the common resource allocation model and related multi-objective optimization problems.2.This paper uses the improved decomposition class multi-objective algorithm to solve the problem of inter-organizational boundary'logistics resource allocation,combined with the actual logistics problem give a decomposition class algorithmwhich can be used to solve this kind of problem,which includes the choice of specific coding transformation and the performance comparison of some classical MOEA.3.Through a series of simulation experiments,when the global replacement is analyzed,the size of the replacement neighborhood can greatly affect the performance of the algorithm,and then the effect of the replacement neighborhood size on the diversity and convergence of the algorithm is obtained.Based on this,an adaptive adjustment and replacement neighborhood size strategy is proposed.Combined with the global replacement technique,the two methods are proposed in MOEA / D,and an improved decomposition multi-objective evolutionary algorithm.And in the ZDT series,MOP series of test functions on the performance test,and with the NSGA-II,MOEA/D,MOEA/D-GR algorithm performance comparison analysis.