Optimization algorithms are proposed to maximize the desirable properties while simultaneously minimizing the undesirable characteristics. Particle Swarm Optimization (PSO) is a famous optimization algorithm, and it has undergone many variants since its inception in 1995. Though different topologies and relations among particles are used in some state-of-the-art PSO variants, the overall performance on high dimensional multimodal optimization problem is still not very good. In this paper, we present a new memetic optimization algorithm, named Monkey King Evolutionary (MKE) algorithm, and give a comparative view of the PSO variants, including the canonical PSO, Inertia Weighted PSO, Constriction Coefficients PSO, Fully-Informed Particle Sawrm, Cooperative PSO, Comprehensive Learning PSO and some variants proposed in recent years, such as Dynamic Neighborhood Learning PSO, Social Learning Particle Swarm Optimization etc. The proposed MKE algorithm is a further work of ebb-tide-fish algorithm and what’s more it performs very well not only on unimodal benchmark functions but also on multimodal ones on high dimensions. Comparison results under CEC2013 test suite for real parameter optimization show that the proposed MKE algorithm outperforms state-of-the-art PSO variants significantly. An application of the vehicle navigation optimization is also discussed in the paper, and the conducted experiment shows that the proposed approach to path navigation optimization saves travel time of real-time traffic navigation in a micro-scope traffic networks.