An efficient parallel version of dynamic multi-objective evolutionary algorithm
Joint Authors
Bin Hirz Allah, Sabir
Jrid, Marwah
Bilaysh, Layla
Kahlul, Laid
Source
The International Arab Journal of Information Technology
Issue
Vol. 19, Issue 3A (s) (31 May. 2022), pp.422-431, 10 p.
Publisher
Zarqa University Deanship of Scientific Research
Publication Date
2022-05-31
Country of Publication
Jordan
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Abstract EN
Multi-Objective Optimization Evolutionary Algorithms (MOEAs) belong to heuristic methods proposed for solving Multi-objective Optimization Problems (MOPs).
In fact, MOEAs search for a uniformly distributed, near-optimal, and near-complete Pareto front for a given MOP.
However, several MOEAs fail to achieve their aim completely due to their fixed population size.
To overcome this shortcoming, Dynamic Multi-Objective Evolutionary Algorithm (DMOEA) [20] was proposed.
Although DMOEA has the distinction of dynamic population size, it still suffers from a long execution time.
To deal with the last disadvantage, we have proposed previously a Parallel Dynamic Multi-Objective Evolutionary Algorithm (PDMOEA) [10] to obtain efficient results in less execution time than the sequential counterparts, in order to tackle more complex problems.
This paper is an extended version of [10] and it aims to demonstrate the efficiency of PDMOEA through more experimentations and comparisons.
We firstly compare DMOEA with other multi-objective evolutionary algorithms Non-Dominated Sorting Genetic Algorithm (NSGA-II) and Strength Pareto Evolutionary Algorithm (SPEA-II), then we present an exhaustive comparison of PDMOEA versus DMOEA and discuss how the number of used processors influences the efficiency of PDMOEA.
As experimental results, PDMOEA enhances DMOEA in terms of three criteria: improving the objective space, minimizing the computational time, and converging to the desired population size.
Finally, the paper establishes a new formula relating the suitable number of processes, required in PDMOEA, and the number of necessary generations to converge to the optimal solutions.
American Psychological Association (APA)
Jrid, Marwah& Bilaysh, Layla& Kahlul, Laid& Bin Hirz Allah, Sabir. 2022. An efficient parallel version of dynamic multi-objective evolutionary algorithm. The International Arab Journal of Information Technology،Vol. 19, no. 3A (s), pp.422-431.
https://search.emarefa.net/detail/BIM-1437107
Modern Language Association (MLA)
Jrid, Marwah…[et al.]. An efficient parallel version of dynamic multi-objective evolutionary algorithm. The International Arab Journal of Information Technology Vol. 19, no. 3A (Special issue) (2022), pp.422-431.
https://search.emarefa.net/detail/BIM-1437107
American Medical Association (AMA)
Jrid, Marwah& Bilaysh, Layla& Kahlul, Laid& Bin Hirz Allah, Sabir. An efficient parallel version of dynamic multi-objective evolutionary algorithm. The International Arab Journal of Information Technology. 2022. Vol. 19, no. 3A (s), pp.422-431.
https://search.emarefa.net/detail/BIM-1437107
Data Type
Journal Articles
Language
English
Record ID
BIM-1437107