Open AccessAn improved remora optimization algorithm with autonomous foraging mechanism for global optimization problems
Author(s) -
Rong Zheng,
AUTHOR_ID,
Heming Jia,
Laith Abualigah,
Shuang Wang,
Di Wu,
AUTHOR_ID,
AUTHOR_ID,
AUTHOR_ID
Publication year - 2022
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2022184
Subject(s) - metaheuristic , mathematical optimization , benchmark (surveying) , optimization problem , foraging , local optimum , global optimization , convergence (economics) , computer science , imperialist competitive algorithm , optimization algorithm , derivative free optimization , test functions for optimization , position (finance) , meta optimization , multi swarm optimization , algorithm , mathematics , ecology , geodesy , finance , economic growth , economics , biology , geography
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.