Details

Autor(en) / Beteiligte

• Nadimi-Shahraki, Mohammad H.
• Taghian, Shokooh
• Mirjalili, Seyedali
• Faris, Hossam

Titel

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

Ist Teil von

• Applied soft computing, 2020-12, Vol.97, p.106761, Article 106761

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2020

Link zum Volltext

Quelle

ScienceDirect Journals (5 years ago - present)

Beschreibungen/Notizen

• 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. •Introducing a multi trial vector approach (MTV) to combine various search strategies.•Introducing a life-time archiving and winner-based distributing in MTV approach.•Proposing an effective differential evolution (MTDE) algorithm using MTV approach.•Evaluating and comparing MTDE with state-of-the-art algorithms on CEC 2018.•MTDE algorithm is very competitive and superior to the compared algorithms.