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Literature Research Optimizer: A New Human-Based Metaheuristic Algorithm for Optimization Problems.

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Název: Literature Research Optimizer: A New Human-Based Metaheuristic Algorithm for Optimization Problems.
Autoři: Ni, Lei, Ping, Yan, Yao, Na, Jiao, Jiao, Wang, Geng
Zdroj: Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ); Sep2024, Vol. 49 Issue 9, p12817-12865, 49p
Témata: METAHEURISTIC algorithms, OPTIMIZATION algorithms, MATHEMATICAL models, DETERMINISTIC algorithms, SET functions
Abstrakt: Traditional deterministic optimization algorithms are difficult to effectively solve many real-world nonlinear, complex, and high-dimensional optimization problems. The metaheuristic optimization algorithm has evolved in recent years into a kind of very popular algorithm due to its gradient-free and random nature. In this paper, a new metaheuristic algorithm, namely the literature research optimization algorithm (LRO), is proposed. In this algorithm, the literature research process is represented as a mathematical model, and new mechanisms are designed to help realize the global exploration and local exploitation. To evaluate the performance of LRO, three sets of test functions including the standard benchmark set, CEC2017, and CEC2019 are applied first, and then, six engineering problems are employed to further verify it. The Friedman test and Wilcoxon rank sum test are also used to statistically compare the proposed LRO algorithm with ten common existing metaheuristics. The results show that the LRO algorithm outperforms the comparison algorithms on most benchmark functions and that it also performs well in practical engineering applications. The LRO algorithm can provide superior solutions to most optimization problems compared to other metaheuristic algorithms and become a promising candidate for many real-life optimization problems. [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
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Abstrakt:Traditional deterministic optimization algorithms are difficult to effectively solve many real-world nonlinear, complex, and high-dimensional optimization problems. The metaheuristic optimization algorithm has evolved in recent years into a kind of very popular algorithm due to its gradient-free and random nature. In this paper, a new metaheuristic algorithm, namely the literature research optimization algorithm (LRO), is proposed. In this algorithm, the literature research process is represented as a mathematical model, and new mechanisms are designed to help realize the global exploration and local exploitation. To evaluate the performance of LRO, three sets of test functions including the standard benchmark set, CEC2017, and CEC2019 are applied first, and then, six engineering problems are employed to further verify it. The Friedman test and Wilcoxon rank sum test are also used to statistically compare the proposed LRO algorithm with ten common existing metaheuristics. The results show that the LRO algorithm outperforms the comparison algorithms on most benchmark functions and that it also performs well in practical engineering applications. The LRO algorithm can provide superior solutions to most optimization problems compared to other metaheuristic algorithms and become a promising candidate for many real-life optimization problems. [ABSTRACT FROM AUTHOR]
ISSN:2193567X
DOI:10.1007/s13369-024-08825-w