Enhanced Remora Optimization Algorithm for Solving Constrained Engineering Optimization Problems

Shuang Wang, Abdelazim G. Hussien, Heming Jia, Laith Abualigah and Rong Zheng
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Shuang Wang: School of Information Engineering, Sanming University, Sanming 365004, China
Abdelazim G. Hussien: Department of Computer and Information Science, Linköping University, SE-581 83 Linköping, Sweden
Heming Jia: School of Information Engineering, Sanming University, Sanming 365004, China
Laith Abualigah: Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan
Rong Zheng: School of Information Engineering, Sanming University, Sanming 365004, China

Mathematics, 2022, vol. 10, issue 10, 1-32

Abstract: Remora Optimization Algorithm (ROA) is a recent population-based algorithm that mimics the intelligent traveler behavior of Remora. However, the performance of ROA is barely satisfactory; it may be stuck in local optimal regions or has a slow convergence, especially in high dimensional complicated problems. To overcome these limitations, this paper develops an improved version of ROA called Enhanced ROA (EROA) using three different techniques: adaptive dynamic probability, SFO with Levy flight, and restart strategy. The performance of EROA is tested using two different benchmarks and seven real-world engineering problems. The statistical analysis and experimental results show the efficiency of EROA.

Keywords: remora optimization algorithm; adaptive dynamic probability; restart strategy; metaheuristic algorithm; constrained engineering problems (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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