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Reference point-based evolutionary multi-objective optimization for reversible logic circuit synthesis

机译:基于参考点的进化多目标优化,可逆逻辑电路合成

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In this paper, Reversible logic circuit synthesis is formulated as a quantum cost-minimization problem with equality constraint. A new reference-point based evolutionary multi-objective method R-EMO-RLC is specially designed to attack the equality constraint. First, the reference point is determined dynamically according the distribution of solutions. Then, a new crowding comparative operator is fabricated to adapt the uncertainty of constraint violation and objective value aroused by variable length encoding. Experimental results show that R-EMO-RLC can increase the feasible ratio and obtain savings in quantum cost for some benchmarks from recent publications comparing with previously known circuits.
机译:在本文中,可逆逻辑电路合成作为具有平等约束的量子成本最小化问题。基于新的参考点的进化多目标方法R-EMO-RLC专门设计用于攻击平等约束。首先,根据解决方案的分布动态地确定参考点。然后,制造了一种新的拥挤的比较运算符,以适应通过可变长度编码引起的约束违规和客观值的不确定性。实验结果表明,与先前已知的电路相比,R-EMO-RLC可以提高可行的比率,并在最近的出版物中获得一些基准的量子成本。

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