An improved secretary bird optimization algorithm with hybrid improvement strategies

In practical engineering, the key performance of many control systems will vary with variation of some parameters in the system, which greatly affect the performance control of the entire system. If suitable parameters can be found within the applicable range, the key performance of the system can be greatly improved. Therefore, the above method can deal with an engineering by seeking for an optimal solution. With the development of artificial intelligence, a lot of intelligence algorithms are put forward to address scientific issues. It is gradually found that intelligence algorithms can’t solve all problems in engineering perfectly. Hence, different types of improvement strategies are applied to enhance intelligence algorithms. As a novel intelligence algorithm, Secretary bird optimization algorithm (SBOA) is proved to be preferable for dealing with optimization problems. However, SBOA can’t escape from randomness, local optimization and premature which reduce the global search ability of SBOA. In order to enhance SBOA efficiently, hybrid chaotic mutation strategy, improved attacking prey strategy and population reconstruction strategy are employed into SBOA. Thirty types of benchmark function of CEC2017 are tested on SBOA and the improved SBOA called ISBOA. The results show that the optimization ability of ISBOA is better than that of SBOA.