Multi-objective learner performance-based behavior algorithm with five multi-objective real-world engineering problems

No Thumbnail Available
File version
Author(s)
Rahman, CM
Rashid, TA
Ahmed, AM
Mirjalili, S
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)
Date
2022
Size
File type(s)
Location
License
Abstract

In this work, a new multi-objective optimization algorithm called multi-objective learner performance-based behavior algorithm is proposed. The proposed algorithm is based on the process of moving graduated students from high school to college. The proposed technique produces a set of non-dominated solutions. To test the ability and efficacy of the proposed multi-objective algorithm, it is applied to a group of benchmarks and five real-world engineering optimization problems. Several widely used metrics are employed in the quantitative statistical comparisons. The proposed algorithm is compared with three multi-objective algorithms: Multi-Objective Water Cycle Algorithm (MOWCA), Non-dominated Sorting Genetic Algorithm (NSGA-II), and Multi-Objective Dragonfly Algorithm (MODA). The produced results for the benchmarks and engineering problems show that in general the accuracy and diversity of the proposed algorithm are better compared to the MOWCA and MODA. However, the NSGA-II outperformed the proposed work in some of the cases and showed better accuracy and diversity. Nevertheless, in problems, such as coil compression spring design problem, the quality of solutions produced by the proposed algorithm outperformed all the participated algorithms. Moreover, in regard to the processing time, the proposed work provided better results compared with all the participated algorithms.

Journal Title
Neural Computing and Applications
Conference Title
Book Title
Edition
Volume
Issue
Thesis Type
Degree Program
School
Publisher link
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement
Item Access Status
Note
This publication has been entered as an advanced online version in Griffith Research Online.
Access the data
Related item(s)
Subject
Cognitive and computational psychology
Applied statistics
Optimisation
Engineering design
Artificial intelligence
Computer vision and multimedia computation
Machine learning
Persistent link to this record
Citation
Rahman, CM; Rashid, TA; Ahmed, AM; Mirjalili, S, Multi-objective learner performance-based behavior algorithm with five multi-objective real-world engineering problems, Neural Computing and Applications, 2022
Collections