Abstract
To improve the efficiency of the structural optimization design in truss calculation, an improved chicken swarm optimization algorithm was proposed for truss structure optimization. The chicken swarm optimization is a novel swarm intelligence algorithm. In the basic chicken swarm optimization algorithm, the concept of combining chaos strategy and reverse learning strategy was introduced in the initialization to ensure the global search ability. And the inertia weighting factor and the learning factor were introduced into the chick position update process, so as to better combine the global and local search. Finally, the overall individual position of the algorithm was optimized by the differential evolution algorithm. The improved algorithm was tested by multipeak function and applied to the truss simulation experiment. The study provided a new method for the truss structure optimization.
Highlights
Engineering structure optimization is a problem that has plagued scholars for many years
Based on the above research, this paper improved the chicken swarm optimization and applied to truss structure optimization; the concept of combining chaos strategy and reverse learning strategy was introduced in the initialization to ensure the global search ability
The inertia weighting factor and the learning factor were introduced into the chick position update process, so as to better combine the global and local search
Summary
Engineering structure optimization is a problem that has plagued scholars for many years. E adaptive weight was introduced in the process of updating the individual position of the chicken, which solved the problem that the optimal search solution was easy to skip due to the large search space in the early stage of the algorithm; the late search space was small, the convergence was slow, and the learning part of the individual with the cock was added. Based on the above research, this paper improved the chicken swarm optimization and applied to truss structure optimization; the concept of combining chaos strategy and reverse learning strategy was introduced in the initialization to ensure the global search ability. (2) e chicken swarm divides several subchicken swarms and determines the fitness value of individuals on which cocks, hens, and chicks depend. Where m represents the hen corresponding to the i-th chick and F is the follow-up coefficient, which means that the chick follows the hen to find food
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