Golden-Sine dynamic marine predator algorithm for addressing engineering design optimization

• Published in: Expert systems with applications 2022-12, Vol.210, p.118460, Article 118460
• Main Authors: Han, Muxuan, Du, Zunfeng, Zhu, Haitao, Li, Yancang, Yuan, Qiuyu, Zhu, Haiming
• Format: Article
• Language: English
• Subjects: Engineering design optimization; Evolutionary computation; Golden-Sine factor; Improvement strategy; Marine predator algorithm
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2022.118460

•A multi-strategy improved dynamic marine predator algorithm.•The optimization problem is multimodal, complex, and computationally expensive.•We have performed a thorough comparative study of 10 evolutionary algorithms.•The improved algorithm is applied to practical engineering optimization problems. In engineering design optimization problems, the optimal solution can improve the design quality of complex engineering system and reduce a lot of cost consumption, so it is of great practical significance to study the optimization algorithm of engineering design problems. Evolutionary computation is widely used to solve engineering design optimization problems, which are mostly mixed-integer nonlinear programming (MINLP) problems. As a newly developed evolutionary computing method, Marine Predator Algorithm (MPA) currently suffers from weak convergence and easily falls into local optimum. In order to overcome the disadvantage, this study proposed a Golden-Sine Dynamic Marine Predator Algorithm (GDMPA). Firstly, Logistic-Logistic (L-L) cascade chaos was used to adjust the initial position of the population to generate a high-quality initial prey population while ensuring ergodicity and randomness. Secondly, the dynamic adjustment transition probability strategy was added to improve the discriminant conditions when predators entered different stages, which effectively maintained the balance between global exploration and local exploitation. The adaptive inertial weight based on Sigmoid function was used in updating the step information of predators to avoid the problem of falling into local extrema. Finally, the Golden-Sine factor is employed to achieve a better balance between exploration and exploitation, further improve the premature convergence problem, enhance the population diversity, and improve the convergence rate. A series of validation studies were conducted over twelve standard test functions and the CEC2017 test set to verify the effectiveness and robustness of the improved GDMPA strategy. Mechanical optimization and size optimization study for truss structures was carried out using the proposed GDMPA, which yielded excellent results. The results of the 27-bar truss structure show that the proposed GDMPA reduces 3.24%, 27.18%, 39.38%, 27.65% and 9.67% compared to the total mass of MPA, BOA, SSA, SOA and HHO, respectively. In the other cases, the optimization results of GDMPA have been improved substantially compared with other algorithms. Therefore