The Improvement Of Whale Optimization Algorithm And Its Application

In this paper,we mainly study the improvement of the whale algorithm and its application to the improved supply chain network equilibrium problem.The main research contents are as follows:1.In the algorithm improvement section,the spiral update location model is improved for the loss of population diversity of the whale algorithm in the late.At the same time,the whale optimization algorithm is improved by combining existing methods such as opposite learning strategies,random adjustment parameters,and normal mutation operations.Using the opposite learning strategy to initialize the whale population,lay the foundation for the global search.Using the strategy of randomly adjusting the control parameters,it avoids the late fall into local optimum.Normal mutation operator and improved spiral update position model are designed to interfere with the whale population,avoiding the late arrival of the population to an optimal region and increasing the global search ability of the algorithm.This paper uses a large number of international standard test functions to perform low-dimensional and high-dimensional tests on improved whale optimization algorithms.The results show that the IMWOA algorithm is superior to the original WOA algorithm in terms of convergence accuracy and convergence speed,and has universal applicability,stability and ability to solve hyperscale optimization problems.2.In the application part of the algorithm,it is often encountered that in actual production,the manufacturer will have to delay the delivery time or advance delivery for some reasons which are unknown or unpredictable emergencies.while fuzzy sets theory is a useful tool to describe uncertain problems,combining fuzzy theory with satisfaction,this paper establishes a single commodity supply chain network equilibrium model with fuzzy supply time under stochastic demand.Finally,the improved whale algorithm is used to solve the example and verify.The reliability of the algorithm and the rationality of the model.