Improving Arithmetic Optimization Algorithm Through Modified Opposition-based Learning Mechanism

İzci, Davut; Ekinci, Serdar; Eker, Erdal; Dundar, Ahmet

doi:10.1109/ismsit52890.2021.9604531

Cited by 9 publications

(3 citation statements)

References 16 publications

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“…Researchers also incorporated OBL and its variants into AOA and found to be a nice efficacy improvement strategy. Classical OBL [71], quasi-reflection based OBL [76], chaotic quasi-reflection based OBL [81] had been efficiently utilized for the AOA's improvement. Izci et al [71] propose a new hybrid metaheuristic algorithm to improve the effectiveness of the AOA by applying a modified opposition-based learning mechanism (mOBL).…”

Section: Opposition Based Learning (Obl)mentioning

confidence: 99%

“…Classical OBL [71], quasi-reflection based OBL [76], chaotic quasi-reflection based OBL [81] had been efficiently utilized for the AOA's improvement. Izci et al [71] propose a new hybrid metaheuristic algorithm to improve the effectiveness of the AOA by applying a modified opposition-based learning mechanism (mOBL). Through test experiments on benchmark functions, the result of this study demonstrated that the suggested method produces a superior outcome compared to other tested algorithms and original AOA technique.…”

Section: Opposition Based Learning (Obl)mentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Survey on Arithmetic Optimization Algorithm

Dhal

Sasmal

Das

et al. 2023

Arch Computat Methods Eng

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Arithmetic Optimization Algorithm (AOA) is a recently developed population-based nature-inspired optimization algorithm (NIOA). AOA is designed under the inspiration of the distribution behavior of the main arithmetic operators in mathematics and hence, it also belongs to mathematics-inspired optimization algorithm (MIOA). MIOA is a powerful subset of NIOA and AOA is a proficient member of it. AOA is published in early 2021 and got a massive recognition from research fraternity due to its superior efficacy in different optimization fields. Therefore, this study presents an up-to-date survey on AOA, its variants, and applications.

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Section: Opposition Based Learning (Obl)mentioning

confidence: 99%

Section: Opposition Based Learning (Obl)mentioning

confidence: 99%

A Comprehensive Survey on Arithmetic Optimization Algorithm

Dhal

Sasmal

Das

et al. 2023

Arch Computat Methods Eng

View full text Add to dashboard Cite

show abstract

“…Izci et al [42] proposed an improved AOA by incorporating a modified OBL (mOBL) scheme with a probability. In this mOBL-AOA, the first basic operations of AOA are applied to update the position of search agents, and then mOBL is applied with a probability.…”

Section: Related Workmentioning

confidence: 99%

PCOBL: A Novel Opposition-Based Learning Strategy to Improve Metaheuristics Exploration and Exploitation for Solving Global Optimization Problems

Bhattacharya

Nayak

et al. 2023

IEEE Access

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Meta-heuristics are commonly applied to solve various global optimization problems. In order to make the meta-heuristics performing a global search, balancing their exploration and exploration ability is still an open avenue. This manuscript proposes a novel Opposition-based learning scheme, called ''PCOBL'' (Partial Centroid Opposition-based Learning), inspired by the partial centroid. PCOBL aims to improve meta-heuristics performance through maintaining an effective balance between the exploration and exploitation. PCOBL was incorporated in three different meta-heuristics, and a comparative study was conducted on 28 CEC2013 benchmark problems with 30, 50, and 100 dimensions. In addition, we assessed the PCOBL in the IEEE CEC2011 real-world problems. The empirical results demonstrate that PCOBL balances the exploration and exploitation ability of the meta-heuristics, positively impacting their performance and making them outperform the state-of-the-art algorithms in terms of best-error runs and convergence in most of the optimization problems. Moreover, the computational cost analysis illustrated that the inclusion of PCOBL in the meta-heuristic algorithm has a low impact on its efficiency.

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