惩罚激励双向优化促进结构变异策略应用于换热网络优化

Application of penalty incentive bi-directional optimization to promote structural variation strategy in heat exchanger network optimization

  • 摘要: 针对启发式算法优化换热网络时因贪婪特性导致的早期大换热单元主导负荷分配问题——即“先入为主”现象,该现象会导致结构变异困难,使搜索陷入局部最优。鉴于此,提出一种惩罚激励双向优化策略优化换热网络。首先对结构中的大换热单元施加阻滞惩罚,利用该惩罚延迟大换热单元的成长速度,同时也促进优化后期结构中优势换热单元地位的演替;同时为增大新生单元的竞争力,对其所在冷流股或者热流股上的既有换热单元施加进化惩罚,利用该惩罚促进新生单元的发展,实现流股上优势换热单元的更新。通过两个典型换热网络算例的验证,结果表明,该方法能有效规避局部最优,获得更优的换热网络结构,证实了其在提升启发式算法全局搜索能力方面的有效性。

     

    Abstract: Aiming at the dominant load distribution problem of early large heat exchange units caused by greedy characteristics when heuristic algorithm is used to optimize the heat exchange network, that is, the "preconceived" phenomenon, which will lead to the difficulty of structural variation and make the search fall into local optimization. In view of this, a penalty incentive bi-directional optimization strategy is proposed to optimize the heat exchanger network. Firstly, the block penalty is applied to the large heat exchange unit in the structure, which can delay the growth rate of the large heat exchange unit and promote the succession of the dominant heat exchange unit in the structure at the later stage of optimization; At the same time, in order to increase the competitiveness of the new unit, an evolutionary penalty is imposed on the existing heat exchange unit on the cold stream or hot stream, which is used to promote the development of the new unit and realize the renewal of the dominant heat exchange unit on the stream. Through the verification of two typical heat exchange network examples, the results show that the method can effectively avoid the local optimization and obtain a better heat exchange network structure, which proves its effectiveness in improving the global search ability of the heuristic algorithm.
     

     

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