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.
Funding Agencies|Sanming University Introduces High-level Talents to Start Scientific Research Funding Support Project [20YG01, 20YG14]; Guiding Science and Technology Projects in Sanming City [2020-S-39]; Educational Research Projects of Young and Middle-aged Teachers in Fujian Province [JAT200638]; Scientific Research and Development Fund of Sanming University [B202029]; School level education and teaching reform project of Sanming University [J2010306]; Fujian Natural Science Foundation Project [2021J011128]; Sanming University National Natural Science Foundation Breeding Project [PYT2105]; Higher education research project of Sanming University [SHE2102]; 2021 project of the 14th Five-year Plan of Education science in Fujian Province [FJJKBK21-138]