Improved slime mould algorithm with elitist strategy and its application to structural optimization with natural frequency constraints. (May 2022) - Kaveh, Ali; Biabani Hamedani, Kiarash; Kamalinejad, Mohammad
Highlights: An improved slime mould algorithm (ISMA) is proposed. ISMA adopts an elitist selection strategy in the replacement phase. In ISMA, the exploration phase of the classical SMA is modified. ISMA is proposed for frequency-constrained optimization of truss structures. ISMA offers results comparable or superior to other state-of-the-art algorithms. Abstract: An improved slime mould algorithm (ISMA) is proposed and applied to the size optimization of truss structures with natural frequency constraints. The slime mould algorithm is a recently proposed metaheuristic inspired by the morphological changes of the acellular slime mould Physarum polycephalum while foraging. This algorithm has been successfully applied to some real-world optimization problems in science and industry. However, it is found that the classical SMA suffers from slow convergence and often converges prematurely to non-optimal solutions, especially for large-scale optimization problems. Compared to the classical SMA, two main improvements are introduced in the proposed ISMA: (1) In the replacement phase of the ISMA, an elitist strategy is adopted to replace the generational replacement strategy of the classical SMA. The elitist strategy aims to increase the convergence rate of the ISMA. (2) A slight modification is made to the exploration phase of the classical SMA to ensure a vast exploration of the search space. The suggested modification enables the ISMA to overcome the shortcoming of the premature convergence of the classical SMA. Finally, the efficiency and robustness of the ISMA are demonstrated through three large-scale benchmark dome trusses with natural frequency constraints. To our knowledge, this is the first time to apply SMA to structural optimization. It is shown that the proposed ISMA overcomes the problems of premature convergence and slow convergence rate of the classical SMA. Numerical results show that the ISMA outperforms the classical SMA and shows superior or comparable performance to other reported methods.