A Review of Metaheuristic Algorithms and Data Envelopment Analysis

Subject Areas : Data Envelopment Analysis

Mohsen  Vaez-ghasemi ¹ (Department of mathematics, Rasht IAu)
Zohreh  Moghaddas ² (Department of Electrical, Computer and Biomedical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran)
Hamid  Askari ³ (Ph.D. Candidate of Industrial Engineering, Qazvin Branch, Islamic Azad University, Rasht, Iran.)
Feloora  Valizadeh ⁴ (Islamic azad university,Rasht Branch)

Received: 2020-03-23 Accepted : 2020-06-20 Published : 2020-06-01

Keywords: Data envelopment analysis, Metaheuristic Algorithms, Optimization,

Abstract :

Today, many activities from businesses to engineering design, routing on the Internet, and even routing of foodstuff trucks require programming and optimization. Many of these problems have no determinate solution and cannot be solved easily. To solve these problems, algorithms have been developed inspired by nature based on particle intelligence, biological, physical, and chemical systems, and even human communities and have been named after their inspiration source. A metaheuristic optimization algorithm is an innovative way that can be applied to various optimization problems by slight modifications. These algorithms can improve the capability of finding high-quality answers for difficult optimization problems significantly. The present paper reviews the application of various metaheuristic algorithms and data envelopment analysis (DEA) to optimization problems in the literature published in recent years. Descriptions are provided about the application of metaheuristic algorithms in DEA along with their applications, the field of activity, overlaps, and the integration of these two robust methods to find the optimal answer.