A Memetic Quantum-Inspired Evolutionary Algorithm for circuit bipartitioning problem

This paper proposes a new circuit bipartitioning algorithm based on a novel Memetic Quantum-Inspired Evolutionary Algorithm (MQEA) framework. The main idea is to embed a heuristic local search algorithm into QEA to improve its local tuning capability. We use the Fiduccia-Mattheyses (FM) algorithm for the local optimization, and to apply this to QEA, modify the operator of QEA called Q-gate. Experimental results show that our MQEA algorithm achieves significant improvement of quality over the conventional FM algorithm or QEA. Exploiting its intrinsic parallelism, we parallelize the MQEA to achieve 2.5X speedup on average on a multiprocessor machine.