Application Of An Improved Whale Optimization Algorithm In An Interval Supply Chain Network

Whale Optimization Algorithm(WOA)is a heuristic algorithm that imitate whale populations for cooperative predation,WOA has the advantages of simple structure,fewer parameters,and easy operation,However,since the WOA is in the early development stage,the convergence speed and convergence accuracy of the WOA still need to be improved,and in the late iteration of the WOA,the WOA only performs local convergence and may fall into a local optimal solution.In this paper,in view of the above problems,we study from two aspects of improvement of whale optimization algorithm model and application.The specific content is as follows:(1)According to the problem of the convergence speed of the whale optimization algorithm,this paper proposes a TIWOA algorithm based on threshold control,this algorithm uses the normal mutation operator to disturb the population position,compares the fitness of the whale position before and after mutation,and uses the better fitness position as the position of the next iteration of the whale.For the problem of the convergence accuracy of the whale optimization algorithm,the improved TIWOA algorithm transforms the method of randomly generating the initial population into a method based on the combination of equidistant sampling and pseudo-opposed learning to generate the initial population,which increases the position diversity of the initial whale population.In addition,the trigonometric function is used as a random interference method to improve the spiral update of the whale position in the WOA algorithm,which increases the convergence accuracy of the WOA algorithm.In order to solve the problem that the WOA algorithm is liable to fall into local convergence in the later stage,a nonlinear convergence is used Factors control the flow of the algorithm,and use a threshold to constrain the algorithm,so that when the algorithm falls into the local optimal solution,it can jump out of the local optimal solution at the first time,and then perform the global optimal solution again.Test the convergence accuracy,convergence speed,and stability of the improved TIWOA algorithm and WOA algorithm and other improved whale optimization algorithms under 25 standard test functions.The TIWIA algorithm is superior to other algorithms in accuracy and speed,and TIWOA has stability.(2)Supply chain network,as an optimization of the cooperative relationship between enterprises,achieves the goal of mutual benefit and reciprocity.Supply chain network is an advantageous tool to improve the core competitiveness of enterprises and can bring huge benefits to business.Supply chain network equilibrium is a typical non-linear optimization problem.In previous studies,scholars from various countries considered the impact of certain or random demand on the supply chain network,and used the concept of fuzzy optimization to analyze the equilibrium state of the supply chain network.This paper considers that in the actual supply chain network relationship,the specific delivery time is affected by various determining and uncertain factors,and the delivery time should be within a certain interval number.Then it is more appropriate to use interval planning to solve the supply chain network problem.A supply chain network equilibrium problem based on interval planning is established.The equilibrium conditions of the producer,seller,and customer markets are considered.The variational inequality is used to transform the supply chain equilibrium network problem.Finally,the solution of the variational inequality is proved existence and uniqueness.Finally,an example is given to solve the variational inequality using the improved TIWOA algorithm.The validity of the improved TIWOA algorithm is verified.