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1 – 2 of 2Zhensi Lin, Qishan Zhang and Hong Liu
The purpose of this paper is to enhance the forecast precision of GM(1,1) model using an improved artificial fish swarm algorithm.
Abstract
Purpose
The purpose of this paper is to enhance the forecast precision of GM(1,1) model using an improved artificial fish swarm algorithm.
Design/methodology/approach
An optimization model of GM(1,1) model about identifying the parameters is proposed, which takes the minimum of the average relative error as objective function and takes the development coefficient and grey action quantity as decision variables, then an improved artificial fish swarm algorithm is designed to solve the optimization model.
Findings
The results show that the proposed method may enhance the precision of GM(1,1) model, and have better performance than particle swarm optimization.
Practical implications
The method exposed in the paper can be used to optimize the parameters of GM(1,1) model, which is used frequently to solve the economic and management problem.
Originality/value
The paper succeeds in enhancing the forecast precision of GM(1,1) model using an improved artificial fish swarm algorithm.
Details