Differential evolution (DE) is one of the most powerful and effective evolutionary algorithms for the global optimization problems. However, the performance of DE highly depends on control parameters. To solve this problem, dissipative differential evolution with self-adaptive control parameters (DSDE) is proposed in this paper. In DSDE approach, the values of control parameters are adjusted by the fitness information between the target vector and trial vector. Because the population diversity is a key to avoid falling into the local optima, DSDE develops dissipative scheme to make the population far away equilibrium state. Experimental studies on comprehensive set of benchmark functions show DSDE achieves better results for the majority of test cases.