MTDE: An effective multi-trial vector-based differential evolution algorithm and its applications for engineering design problems

No Thumbnail Available
File version
Author(s)
Nadimi-Shahraki, MH
Taghian, S
Mirjalili, S
Faris, H
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)
Date
2020
Size
File type(s)
Location
License
Abstract

In this article, an effective metaheuristic algorithm named multi-trial vector-based differential evolution (MTDE) is proposed. The MTDE is distinguished by introducing an adaptive movement step designed based on a new multi-trial vector approach named MTV, which combines different search strategies in the form of trial vector producers (TVPs). In the developed MTV approach, the TVPs are applied on their dedicated subpopulation, which are distributed by a winner-based distribution policy, and share their experiences efficiently by using a life-time archive. The MTV can be deployed by different types of TVPs, particularly, we use the MTV approach in the MTDE algorithm by three TVPs: representative based trial vector producer, local random based trial vector producer, and global best history based trial vector producer. Therefore, this study introduces the MTV approach to boost the performance of the MTDE and demonstrates its advantages in dealing with problems of different levels of complexity. The performance of the proposed MTDE algorithm is evaluated on CEC 2018 benchmark suite which include unimodal, multimodal, hybrid, and composition functions and four complex engineering design problems. The experimental and statistical results are compared with state-of-the-art metaheuristic algorithms: GWO, WOA, SSA, HHO, CoDE, EPSDE, QUATRE, and MKE. The results demonstrate that the MTDE algorithm shows improved performance and benefits from high accuracy of optimal solutions obtained.

Journal Title

Applied Soft Computing Journal

Conference Title
Book Title
Edition
Volume

97

Issue
Thesis Type
Degree Program
School
Publisher link
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement
Item Access Status
Note
Access the data
Related item(s)
Subject

Artificial intelligence

Applied mathematics

Numerical and computational mathematics

Persistent link to this record
Citation

Nadimi-Shahraki, MH; Taghian, S; Mirjalili, S; Faris, H, MTDE: An effective multi-trial vector-based differential evolution algorithm and its applications for engineering design problems, Applied Soft Computing Journal, 2020, 97, pp. 106761

Collections