Publication: Hybridized chaotic-oppositional based learning differential evolution and arithmetic optimization algorithm for global optimization
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Date
2024-12-01
Authors
Mohamad Faiz, Ahmad Johari
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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.