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A new particle swarm optimization characterized by sensation is presented to improve the limited capability of regular particle swarm optimization in exploiting history experience (iwPSO). It guides individuals to behave reasonably with the capability of self-adaptation in activities of self-cognition 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. It is compared with the regular particle swarm optimizer (PSO) invented by Kennedy and Eberhart in 1995 based on three different benchmark functions. In the iwPSO proposed here, each particle adjusting the inertia weight omega value when its position changes, it enhances the search capability of single particle. The strategy here is to avoid the local minimum problems of PSO algorithm. Under all test cases, simulation shows that the iwPSO always finds better solutions than PSO.