An improved remora optimization algorithm with autonomous foraging mechanism for global optimization problems

Rong Zheng; Heming Jia; Laith Abualigah; Shuang Wang; Di Wu; Rong Zheng; Heming Jia; Laith Abualigah; Shuang Wang; Di Wu

doi:10.3934/mbe.2022184

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 4: 3994-4037. doi: 10.3934/mbe.2022184

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An improved remora optimization algorithm with autonomous foraging mechanism for global optimization problems

1.
School of Information Engineering, Sanming University, Sanming 365004, China
2.
Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan
3.
School of Computer Science, Universiti Sains Malaysia, Penang 11800, Malaysia
4.
School of Education and Music, Sanming University, Sanming 365004, China
academic editor: Weining Wu

Received: 28 December 2021 Revised: 22 January 2022 Accepted: 25 January 2022 Published: 14 February 2022

The remora optimization algorithm (ROA) is a newly proposed metaheuristic algorithm for solving global optimization problems. In ROA, each search agent searches new space according to the position of host, which makes the algorithm suffer from the drawbacks of slow convergence rate, poor solution accuracy, and local optima for some optimization problems. To tackle these problems, this study proposes an improved ROA (IROA) by introducing a new mechanism named autonomous foraging mechanism (AFM), which is inspired from the fact that remora can also find food on its own. In AFM, each remora has a small chance to search food randomly or according to the current food position. Thus the AFM can effectively expand the search space and improve the accuracy of the solution. To substantiate the efficacy of the proposed IROA, twenty-three classical benchmark functions and ten latest CEC 2021 test functions with various types and dimensions were employed to test the performance of IROA. Compared with seven metaheuristic and six modified algorithms, the results of test functions show that the IROA has superior performance in solving these optimization problems. Moreover, the results of five representative engineering design optimization problems also reveal that the IROA has the capability to obtain the optimal results for real-world optimization problems. To sum up, these test results confirm the effectiveness of the proposed mechanism.
- remora optimization algorithm,
- arithmetic optimization algorithm,
- metaheuristic algorithm,
- swarm intelligence,
- global optimization
Citation: Rong Zheng, Heming Jia, Laith Abualigah, Shuang Wang, Di Wu. An improved remora optimization algorithm with autonomous foraging mechanism for global optimization problems[J]. Mathematical Biosciences and Engineering, 2022, 19(4): 3994-4037. doi: 10.3934/mbe.2022184

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Abstract

The remora optimization algorithm (ROA) is a newly proposed metaheuristic algorithm for solving global optimization problems. In ROA, each search agent searches new space according to the position of host, which makes the algorithm suffer from the drawbacks of slow convergence rate, poor solution accuracy, and local optima for some optimization problems. To tackle these problems, this study proposes an improved ROA (IROA) by introducing a new mechanism named autonomous foraging mechanism (AFM), which is inspired from the fact that remora can also find food on its own. In AFM, each remora has a small chance to search food randomly or according to the current food position. Thus the AFM can effectively expand the search space and improve the accuracy of the solution. To substantiate the efficacy of the proposed IROA, twenty-three classical benchmark functions and ten latest CEC 2021 test functions with various types and dimensions were employed to test the performance of IROA. Compared with seven metaheuristic and six modified algorithms, the results of test functions show that the IROA has superior performance in solving these optimization problems. Moreover, the results of five representative engineering design optimization problems also reveal that the IROA has the capability to obtain the optimal results for real-world optimization problems. To sum up, these test results confirm the effectiveness of the proposed mechanism.

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