Settings-Free Hybrid Metaheuristic General Optimization Methods
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,...
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| Format: | Article |
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MDPI AG
2020-07-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/8/7/1092 |
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| author | Héctor Migallón Akram Belazi José-Luis Sánchez-Romero Héctor Rico Antonio Jimeno-Morenilla |
| author_facet | Héctor Migallón Akram Belazi José-Luis Sánchez-Romero Héctor Rico Antonio Jimeno-Morenilla |
| author_sort | Héctor Migallón |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T18:43:09Z |
| format | Article |
| id | doaj.art-fef1e31b2f7741e081e72fefc0e59dee |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T18:43:09Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-fef1e31b2f7741e081e72fefc0e59dee2023-11-20T05:46:31ZengMDPI AGMathematics2227-73902020-07-0187109210.3390/math8071092Settings-Free Hybrid Metaheuristic General Optimization MethodsHéctor Migallón0Akram Belazi1José-Luis Sánchez-Romero2Héctor Rico3Antonio Jimeno-Morenilla4Department of Computer Engineering, Miguel Hernández University, E-03202 Elche, Alicante, SpainLaboratory RISC-ENIT (LR-16-ES07), Tunis El Manar University, Tunis 1002, TunisiaDepartment of Computer Technology, University of Alicante, E-03071 Alicante, SpainDepartment of Computer Technology, University of Alicante, E-03071 Alicante, SpainDepartment of Computer Technology, University of Alicante, E-03071 Alicante, SpainSeveral 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.https://www.mdpi.com/2227-7390/8/7/1092hybrid optimization algorithmsSCA algorithmjaya2D chaotic mapTLBORao’s algorithms |
| spellingShingle | Héctor Migallón Akram Belazi José-Luis Sánchez-Romero Héctor Rico Antonio Jimeno-Morenilla Settings-Free Hybrid Metaheuristic General Optimization Methods Mathematics hybrid optimization algorithms SCA algorithm jaya 2D chaotic map TLBO Rao’s algorithms |
| title | Settings-Free Hybrid Metaheuristic General Optimization Methods |
| title_full | Settings-Free Hybrid Metaheuristic General Optimization Methods |
| title_fullStr | Settings-Free Hybrid Metaheuristic General Optimization Methods |
| title_full_unstemmed | Settings-Free Hybrid Metaheuristic General Optimization Methods |
| title_short | Settings-Free Hybrid Metaheuristic General Optimization Methods |
| title_sort | settings free hybrid metaheuristic general optimization methods |
| topic | hybrid optimization algorithms SCA algorithm jaya 2D chaotic map TLBO Rao’s algorithms |
| url | https://www.mdpi.com/2227-7390/8/7/1092 |
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