Settings-Free Hybrid Metaheuristic General Optimization Methods

Héctor Migallón, Akram Belazi, José-Luis Sánchez-Romero, Héctor Rico and Antonio Jimeno-Morenilla
Additional contact information
Héctor Migallón: Department of Computer Engineering, Miguel Hernández University, E-03202 Elche, Alicante, Spain
Akram Belazi: Laboratory RISC-ENIT (LR-16-ES07), Tunis El Manar University, Tunis 1002, Tunisia
José-Luis Sánchez-Romero: Department of Computer Technology, University of Alicante, E-03071 Alicante, Spain
Héctor Rico: Department of Computer Technology, University of Alicante, E-03071 Alicante, Spain
Antonio Jimeno-Morenilla: Department of Computer Technology, University of Alicante, E-03071 Alicante, Spain

Mathematics, 2020, vol. 8, issue 7, 1-25

Abstract: Several population-based metaheuristic optimization algorithms have been proposed in the last decades, none of which are able either to outperform all existing algorithms or to solve all optimization problems according to the No Free Lunch (NFL) theorem. Many of these algorithms behave effectively, under a correct setting of the control parameter(s), when solving different engineering problems. The optimization behavior of these algorithms is boosted by applying various strategies, which include the hybridization technique and the use of chaotic maps instead of the pseudo-random number generators (PRNGs). The hybrid algorithms are suitable for a large number of engineering applications in which they behave more effectively than the thoroughbred optimization algorithms. However, they increase the difficulty of correctly setting control parameters, and sometimes they are designed to solve particular problems. This paper presents three hybridizations dubbed HYBPOP, HYBSUBPOP, and HYBIND of up to seven algorithms free of control parameters. Each hybrid proposal uses a different strategy to switch the algorithm charged with generating each new individual. These algorithms are Jaya, sine cosine algorithm (SCA), Rao’s algorithms, teaching-learning-based optimization (TLBO), and chaotic Jaya. The experimental results show that the proposed algorithms perform better than the original algorithms, which implies the optimal use of these algorithms according to the problem to be solved. One more advantage of the hybrid algorithms is that no prior process of control parameter tuning is needed.

Keywords: hybrid optimization algorithms; SCA algorithm; jaya; 2D chaotic map; TLBO; Rao’s algorithms (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/8/7/1092/pdf (application/pdf)
https://www.mdpi.com/2227-7390/8/7/1092/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:8:y:2020:i:7:p:1092-:d:379949

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().