Abstract
To improve the efficiency of the structural optimization design in truss calculation, an improved fruit fly optimization algorithm was proposed for truss structure optimization. The fruit fly optimization algorithm was a novel swarm intelligence algorithm. In the standard fruit fly optimization algorithm, it is difficult to solve the high-dimensional nonlinear optimization problem and easy to fall into the local optimum. To overcome the shortcomings of the basic fruit fly optimization algorithm, the immune algorithm self–non-self antigen recognition mechanism and the immune system learn–memory–forgetting knowledge processing mechanism were employed. The improved algorithm was introduced to the structural optimization. Optimization results and comparison with other algorithms show that the stability of improved fruit fly optimization algorithm is apparently improved and the efficiency is obviously remarkable. This study provides a more effective solution to structural optimization problems.
Highlights
With the rapid development of computer technology, the efficiency of structural optimization is greatly improved, and structural designers can have more time and energy to consider how to get better structural design scheme
The intelligent optimization algorithm is widely applied to the structural optimization, and the modern structural optimization method is gradually applied to the engineering practice
After years of research and development, structural optimization has changed from the original optimization of structure size to the present optimization of topology and further optimization of material distribution
Summary
With the rapid development of computer technology, the efficiency of structural optimization is greatly improved, and structural designers can have more time and energy to consider how to get better structural design scheme. DSEFOA dynamically divided the fruit fly population into spermatogonium subgroups and ordinary subgroups, and adopted different strategies to update the evolution of drosophila at different levels of evolution, which improved the optimization ability of the whole population. Sheng [15] proposed that the length of fruit fly search for Uber should be dynamically changed according to the change rate of concentration difference, which can effectively balance the global optimization ability and the local optimization ability. Random value (RV) is to be the search distance, and the position of the population is updated simultaneously: Xi Yi. Step 3: since the exact location of the food is unknown, it is necessary to calculate the distance ( Disti ) between the fruit flies and the origin of the coordinate and calculate the taste concentration parameter ( Si): Disti = Xi2 + Yi2. Step 7: termination condition, judge whether the concentration of the best position is better than that of the previous generation, and reach the maximum number of iterations; otherwise, skip step 2 to enter the iterative optimization
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