Dynamic Levy Flight Chimp Optimization

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. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study introduces the Dynamic Levy Flight technique to enhance the Chimp Optimization Algorithm, achieving good results in various standard and challenging functions as well as practical optimization problems. DLFChOA and CMA-ES perform well in most numerical test functions, and achieve the best results in some real-world engineering problems.