本文提出了一种新的方法TNEP MOMA相结合的多目标元的启发式（MOMH）禁忌搜索（TS）获得更好的解决方案集。所提出的方法的有效性，成功地展示了在风电场的IEEE 24-bus系统。
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因此本文针对标的本身的结构提出了四种启发式信息及两种求解器：二元蚁群算法及贪婪算法。
By using the intrinsic characters of this model, we design four types of heuristic information for bid and two problem solvers: the binary ant colony algorithm and the greedy algorithm.
神经元网络启发式的并行、分布特征和可学习性为专家系统的知识表达和获取、不确定性推理提供了新的途径。
The parallelism, distribution and capability of learning of neural net heuristics provide a new way for knowledge acquisition, knowledge representation and uncertainty reasoning in expert systems.
该算法利用问题的邻域知识指导局部搜索，可克服元启发式算法随机性引起的盲目搜索。
The proposed algorithm utilizes neighborhood knowledge to direct its local search procedure which can overcome the blindness or randomness introduced by meta-heuristics.
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