Dynamic Levy Flight Chimp Optimization

Autor: Mohammad Khishe, Wei Kaidi, Mokhtar Mohammadi
Rok vydání: 2022
Předmět:
Zdroj: Knowledge-Based Systems. 235:107625
ISSN: 0950-7051
DOI: 10.1016/j.knosys.2021.107625
Popis: Background: The Chimp Optimization Algorithm (ChOA) is a hunting-based model and can be utilized as a set of optimization rules to tackle optimization problems. Due to agents’ insufficient diversity in some complex problems, this algorithm is sometimes exposed to local optima stagnation. Objective: This paper introduces a Dynamic Levy Flight (DLF) technique to smoothly and gradually transit the search agents from the exploration phase to the exploitation phase. Methods: To investigate the efficiency of the DLFChOA, this paper evaluates the performance of DLFChOA on twenty-three standard benchmark functions, twenty challenging functions of CEC-2005, ten suit tests of IEEE CEC06-2019, and twelve real-world optimization problems. The results are compared to benchmark optimization algorithms, including CMA-ES, SHADE, ChOA, HGSO, LGWO and ALEP (as the best benchmark Levy-based algorithms), and eighteen state-of-the-art algorithms (as the winners of the CEC2019, the GECCO2019, and the SEMCCO2019). Result and conclusion: Among forty-three numerical test functions, DLFChOA and CMA-ES gain the first and second rank with thirty and eleven best results. In the 100-digit challenge, jDE100 with a score of 100 provides the best results, followed by DISHchain1e+12, and DLFChOA with a score of 85.68 is ranked fifth among eighteen state-of-the-art algorithms achieved the best score in seven out of ten problems. Finally, DLFChOA and CMA-ES respectively gain the best results in five and four real-world engineering problems.
Databáze: OpenAIRE
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