Reference Point-based Evolutionary Multi-objective Optimization for Reversible Logic Circuit Synthesis

Abstract

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. This paper aims at research on RLC synthesis using evolutionary algorithm. The problem is formulated as a QC- minimization problem with equality constraint. It is different from the existing evolutionary solutions for RLC synthesis, where the evaluation of an individual is according to the satisfaction on the benchmark specification or a weighted sum of the correctness and the cost of the circuit.