Raccoon Optimization Algorithm

Population-based meta-heuristic is a high-level method intended to provide sufficient solution for problems with incomplete information among a massive volume of solutions. However, it does not guarantee to attain global optimum in a reasonable time. To improve the time and accuracy of the coverage...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.5383-5399
Main Authors: Zangbari Koohi, Sina, Abdul Hamid, Nor Asilah Wati, Othman, Mohamed, Ibragimov, Gafurjan
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
evolutionary algorithm
Evolutionary computation
Heuristic methods
History
meta-heuristic algorithm
nonlinear continuous optimization problems
Optimization
Optimization algorithms
Raccoon optimization algorithm (ROA)
Search problems
Sociology
Solvers
Space exploration
Statistics
structural optimization
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Summary:Population-based meta-heuristic is a high-level method intended to provide sufficient solution for problems with incomplete information among a massive volume of solutions. However, it does not guarantee to attain global optimum in a reasonable time. To improve the time and accuracy of the coverage in the population-based meta-heuristic, this paper presents a novel algorithm called the raccoon optimization algorithm (ROA). The ROA is inspired by the rummaging behaviors of real raccoons for food. Raccoons are successful animals because of their extraordinarily sensitive and dexterous paws and their ability to find solutions for foods and remember them for up to three years. These capabilities make raccoons expert problem solvers and allow them to purposefully seek optimum solutions. These behaviors exploited in the ROA to search the solution spaces of nonlinear continuous problems to find the global optimum with higher accuracy and lower time coverage. To evaluate the ROA's ability in addressing complicated problems, it has been tested on several benchmark functions. The ROA is then compared with nine other well-known optimization algorithms. These experiments show that the ROA achieves higher accuracy with lower coverage time.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2018.2882568