Publication: Hybridized chaotic-oppositional based learning differential evolution and arithmetic optimization algorithm for global optimization
| datacite.subject.fos | oecd::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering::Electrical and electronic engineering | |
| dc.contributor.author | Mohamad Faiz, Ahmad Johari | |
| dc.date.accessioned | 2025-11-28T00:40:16Z | |
| dc.date.available | 2025-11-28T00:40:16Z | |
| dc.date.issued | 2024-12-01 | |
| dc.description.abstract | Differential evolution (DE) is a popular Metaheuristic Search Algorithms (MSAs) used to solve various optimization problems due to its simplicity and rapid convergence speed. However, its success in diverse optimization scenarios depends critically on the quality of the initial population and the ability to balance exploration and exploitation processes. Therefore, this research proposes two novel DE algorithms, namely, Chaotic Oppositional DE (CODE) and Multi-Chaotic Oppositional DE hybridized with the Arithmetic Optimization Algorithm (MCO- DEHAOA), to address the aforementioned challenges. In CODE, a modified initialization scheme leverages the benefits of both (i) chaotic map and (ii) Oppositional-Based Learning (OBL) strategy. Chaotic map is particularly useful for addressing premature convergence by producing initial solutions with higher diversity levels. Meanwhile, the OBL strategy aims to enhance the algorithm's convergence speed by exploring wider areas of the solution space during the initialization phase. On the other hand, MCO-DEHAOA, an extension from CODE, incorporates two key enhancements, (i) the Multi-Chaotic Oppositional (MCO) initialization technique and (ii) a modified mutation scheme. The MCO technique leverages multiple chaotic maps and the OBL strategy to generate an initial population with superior solution quality compared to CODE. Furthermore, the modified mutation scheme in MCO-DEHAOA synergizes the DE/rand/1 mutation strategy from DE with the Addition and Subtraction operators from Arithmetic Optimization Algorithm (AOA), thus fostering a more effective balance between exploration and exploitation processes. The overall performance of both proposed algorithms have been compared with several state-of- the-art algorithms on the Congress of Evolutionary Computation (CEC) benchmarks and three real-world engineering design problems. The proposed CODE demonstrates promising results, achieving the best mean error (Emean) in 26 out of 30 CEC 2014 benchmark problems. Meanwhile, the proposed MCO-DEHAOA emerges as a well- balanced optimizer, producing the best Emean in 28 out of 29 CEC 2017 benchmark problems. The enhancement of initial solution quality and the balance between the exploration and exploitation processes are proven to be crucial, as simulation results show significant improvements in the quality of the final solutions. | |
| dc.identifier.uri | https://erepo.usm.my/handle/123456789/23283 | |
| dc.language.iso | en | |
| dc.title | Hybridized chaotic-oppositional based learning differential evolution and arithmetic optimization algorithm for global optimization | |
| dc.type | Resource Types::text::thesis::doctoral thesis | |
| dspace.entity.type | Publication | |
| oairecerif.author.affiliation | Universiti Sains Malaysia |