A new particle swarm optimization characterized by sensation is presented to improve the limited capability of dissipative particle swarm optimization in exploiting history experience. It guides individuals to behave reasonably with the capability of self-adaptation in activities of self-cognition and sociality via quantifying the sensation intension as a result of environmental stimulation according to the sensation model. Considering the complexity of a swarm intelligent system at the level of sensation brings about optimization of the comprehensive capability of global, local searching and cooperating with each other. The testing of three uni?multi-modal benchmark functions commonly used in the evolutionary computation proves the superiority and efficiency of the simple algorithm that can be easily implemented. Finally, the algorithm's parameters are also analyzed and discussed.