Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2022-06-06 Cooperative journals: 《计算机应用研究》
Abstract： The Aquila optimizer (AO) , although capable of robust global exploration, had problems with inadequate local development. In the study, a differential evolution mutation and tangent flight Aquila optimizer (DEtanAO) was proposed. Firstly, the mutation operation in the differential evolution algorithm can make the algorithm have strong development ability and make up for the shortcomings of the AO algorithm. Then, the tangent flight strategy in the tangent search algorithm has a strong ability to explore the search space and can make the algorithm jump out of the local optimal solution, which is used to replace the Levy flight in the AO algorithm. The combination of these two strategies effectively balanced the exploration and exploitation stage of the DEtanAO algorithm. Finally, in order to verify the optimization performance of the DEtanAO algorithm, the optimization ability of the improved algorithm was tested in 12 standard benchmark functions, high-dimensional functions, Wilcoxon rank-sum test, and engineering optimization problems. The results showed that the optimization ability and convergence speed of the DEtanAO algorithm was better, compared with other newly proposed intelligent algorithms.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2022-05-10 Cooperative journals: 《计算机应用研究》
Abstract： Aiming at the search stagnation and poor stability of the Slime Mould algorithm, propose an unscented sigma point guided quasi-opposite Slime Mould Algorithm. Firstly, use quasi-opposite learning and quasi-reflected learning to exploration and exploitation behaviors according to the original Slime Mould Algorithm to generate a comprehensive opposite population that includes both quasi-opposite learning and quasi-reflected learning, expand the search space. Secondly, according to the diversity of the population, decide whether to use the opposite population to regenerate a new population for subsequent calculations, avoid the continuous opposite process destroying the search characteristics of the population itself, and improve the search accuracy. Finally, use unscented transformation Sigma point to improve the basic movement mode of Slime Mould Algorithm, make the unscented sigma point guide the search, and accelerate the convergence speed. The experimental part uses the CEC2017 benchmark test functions, uses traditional statistical index and MAE ranking, Wilcoxon rank-sum test to verify the effectiveness of the algorithm, and use it to solve the car side impact design problem, compares and tests with the novel swarm intelligence algorithms, improved algorithms and incomplete algorithm. The experimental results show that the strategies is effective and combinations of strategies complement each other, and the improved algorithm's solution accuracy and robustness are more competitive.
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