Paper Info
Title
Multipopulation-based multi-level parallel enhanced Jaya algorithms.
Abstract
To solve optimization problems, in the field of engineering optimization, an optimal value of a specific function must be found, in a limited time, within a constrained or unconstrained domain. Metaheuristic methods are useful for a wide range of scientific and engineering applications, which accelerate being able to achieve optimal or near-optimal solutions. The metaheuristic method called Jaya has generated growing interest because of its simplicity and efficiency. We present Jaya-based parallel algorithms to efficiently exploit cluster computing platforms (heterogeneous memory platforms). We propose a multi-level parallel algorithm, in which, to exploit distributed-memory architectures (or multiprocessors), the outermost layer of the Jaya algorithm is parallelized. Moreover, in internal layers, we exploit shared-memory architectures (or multicores) by adding two more levels of parallelization. This two-level internal parallel algorithm is based on both a multipopulation structure and an improved heuristic search path relative to the search path of the sequential algorithm. The multi-level parallel algorithm obtains average efficiency values of 84% using up to 120 and 135 processes, and slightly accelerates the convergence with respect to the sequential Jaya algorithm.
Year
DOI
Venue
2019
10.1007/s11227-019-02759-z
The Journal of Supercomputing
Keywords
Field
DocType
Jaya, Optimization, Metaheuristic, Multipopulation, Parallelism, MPI/OpenMP
Convergence (routing),Heuristic,Parallel algorithm,Computer science,Parallel computing,Algorithm,Sequential algorithm,Optimization problem,Engineering optimization,Computer cluster,Metaheuristic
Journal
Volume
Issue
ISSN
75
3
1573-0484
Citations 
PageRank 
References 
1
0.35
11
Authors
5
Name
Order
Citations
PageRank
Héctor Migallón1326.02
Antonio Jimeno-morenilla24711.14
Jose Luis Sánchez-Romero3357.57
H. Rico410.35
R. V. Rao510.35