Many automated negotiation models have been developed to solve the conflict in many distributed computational systems. However, the problem of finding win–win outcome in multiattribute negotiation has not been tackled well. To address this issue, based on an evolutionary method of multiobjective optimization, this paper presents a negotiation model that can find win–win solutions of multiple attributes, but needs not to reveal negotiating agents’ private utility functions to their opponents or a third‐party mediator. Moreover, we also equip our agents with a general type of utility functions of interdependent multiattributes, which captures human intuitions well. In addition, we also develop a novel time‐dependent concession strategy model, which can help both sides find a final agreement among a set of win–win ones. Finally, lots of experiments confirm that our negotiation model outperforms the existing models developed recently. And the experiments also show our model is stable and efficient in finding fair win–win outcomes, which is seldom solved in the existing models.

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With the gradual shift from 2D maps to a 3D virtual environment, various visual artifacts were generated by overlaying 2D map symbols on 3D terrain models. This work proposes a novel screen-based method for rendering 2D vector lines with the accuracy of more than one pixel on the screen in real time. First, screen pixels are inversely projected onto a 3D terrain surface, and then onto the 2D vector plane. Next, these pixels are classified into three categories in terms of their intersection situation with the 2D lines. After that, a multiple sampling process is applied to the pixels that intersect with the 2D lines in order to eliminate visual artifacts, such as intermittence and aliasing (in pixel scale). Finally, a suitable point-inpolygon judgment is implemented to color each sample point quickly. The algorithm is realized in a heterogeneously parallel model so that the performance is improved and becomes acceptable.