Boosting sparrow search algorithm for multi-strategy-assist engineering optimization problems

An improved optimization algorithm, namely, multi-strategy-sparrow search algorithm (MSSSA), is proposed to solve highly non-linear optimization problems. In MSSSA, a circle map is utilized to improve the quality of the population. Moreover, the adaptive survival escape strategy (ASES) is proposed to enhance the survival ability of sparrows. In the producer stage, the craziness factor integrated with ASES is introduced to enhance the search accuracy and survival ability. In the scout stage, the ASES facilitates sparrows successful escape from danger. Besides, opposition-based learning or Gaussian–Chachy variation helps optimal individuals escape from local solutions. The performance of the MSSSA is investigated on the well-known 23 basic functions and CEC2014 test suite. Furthermore, the MSSSA is applied to optimize the real-life engineering optimization problems. The results show that the algorithm presents excellent feasibility and practicality compared with other state-of-the-art optimization algorithms.