Abstract
In order to find a more effective method in structural optimization, an improved wolf pack optimization algorithm was proposed. In the traditional wolf pack algorithm, the problem of falling into local optimum and low precision often occurs. Therefore, the adaptive step size search and Levy's flight strategy theory were employed to overcome the premature flaw of the basic wolf pack algorithm. Firstly, the reasonable change of the adaptive step size improved the fineness of the search and effectively accelerated the convergence speed. Secondly, the search strategy of Levy's flight was adopted to expand the search scope and improved the global search ability of the algorithm. At last, to verify the performance of improved wolf pack algorithm, it was tested through simulation experiments and actual cases, and compared with other algorithms. Experiments show that the improved wolf pack algorithm has better global optimization ability. This study provides a more effective solution to structural optimization problems.
Highlights
An important derivative of the long-term development of structural design theory is structural design optimization [1]
Fish, ants, bees and others exhibit powerful swarm intelligence through constant adaptation and cooperation, which give us solutions to many complex new ideas and problems. These intelligent optimization algorithms have solved many complex and difficult problems, greatly increasing people's ability to deal with optimization problems, and they have effectively promoted the development of computational intelligence
In order to overcome the shortcomings of the wolf pack algorithm, based on the wolf pack algorithm, this paper proposes a search strategy based on Levi's flight strategy for the behavior of detecting the wolf, and puts forward an adaptive step size for the movement during the summoning and siege behavior
Summary
An important derivative of the long-term development of structural design theory is structural design optimization [1]. The algorithm has better performance in optimization when solving optimization problems, but it has some shortcomings, such as slow convergence speed, low convergence accuracy and low robustness, etc. Kaveh and Zakian (2017) proposed an improved gray wolf algorithm by adding a few tunable parameters to provide proper adaptability for the algorithm and to optimize the structures using fewer structural analyses, while obtaining finer solution. These improved algorithms had improved the accuracy and convergence accuracy of the algorithm to some extent, but they were still some shortcomings [23].
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