This paper proposes a coevolutionary optimization algorithm called DCOA.DCOA mainly focuses on how to adjust sub-population size self-adaptively so as to improvethe optimizing performance. To achieve this, a strategy is introduced which consistsof three rules: internal competition, external competition and spontaneous growthrules. These rules can control individual reproduction and elimination speed in each subpopulation.Furthermore, the adjustment can be proven globally asymptotically stable. Inthe experiments, we compare the performances of DCOA, macroevolutionary algorithm(MA) [13] and simple genetic algorithm (SGA) with typical test functions. The resultsshow that DCOA is able to find the global optimum on most difficult functions, nothingless than MA which uses simulated annealing technique. At the same time, DCOA convergesquickly, similar to SGA and faster than MA.