<p>Pied Kingfisher Optimizer(PKO) is a recently proposed novel swarm-based optimization algorithm inspired by the especially hunting behavior of pied kingfisher in the nature. PKO can greatly do well in tackling the common optimization problems, but it also face some issues like ineffectively escaping the local optimum in dealing with more complicated optimization problems, such as multi-modal, rotational transformation and noise problem. In order to address the aforementioned disadvantages, a variant version of the PKO algorithm is proposed. This version, which employs multi-strategies, is referred to as the MPKO algorithm. The leap strategy is introduced to enhance the ability of global search. Dynamic adjustment strategy is used to increase the diversity of population. And dynamic foraging strategy is used to escape the local optimum. Additionally, a new phase called the adventure exploration phase is added to the original three phases of the PKO algorithm, ensuring better avoidance of local optimum. To evaluate the effectiveness of MPKO algorithm, 12 benchmark test functions of CEC-2022 test suite and 5 engineering problems are used. Compared with one variant algorithm and 11 advanced algorithms, qualitative analysis, quantitative analysis and statistical analysis indicate that the comprehensive performance of the MPKO surpasses that of its counterparts.</p>