基于 SMABC 算法的 FPRM 逻辑电路面积优化

The area optimization of fixed polarity Reed-Muller (FPRM) circuits is one of the most important research hotspots in the field of integrated circuit design. However, the existing area optimization methods have problems such as low optimization efficiency and poor optimization effect. Since the area optimization of FPRM logic circuits is a combinatorial optimization problem, a self-adaptive mixed artificial bee colony (SMABC) algorithm is proposed. The algorithm introduces chemotaxis behavior of bacterial foraging algorithm in the stage of the leader bee searching, which enables the leader bee to search in the direction toward good nectar sources, and improves the convergence speed of the algorithm. The algorithm also improves both the selection probability of the following bees for adaptive change, and the global search ability. The transformation conditions of scout bees are improved, and the disturbance amplitude in the evolution process of scout bees is increased. The elite retention strategy is then introduced to improve the population quality. In addition, a method of area optimization of FPRM logic circuits based on SMABC algorithm is proposed, which has the fastest convergence, and that the the maximum optimization rate of the area reaches 54.62% while the average area optimization rate is 15.33%.