基于 M-AFSA 的 MPRM 逻辑电路面积优化

The existing mixed polarity Reed-Muller (MPRM) circuit area optimization algorithms based on the traditional intelligent optimization algorithms have the problem of poor performance. The MPRM circuit’s area optimization is a combinatorial optimization issue, hence an artificial fish swarm algorithm with many strategies (M-AFSA) is initially suggested. In this algorithm, a population initialization strategy based on reverse learning is introduced to improve the population diversity and the quality of the initial population solution; the interactive strategies of foraging and rearing were introduced to enhance the information exchange between the artificial fish individuals and improve the convergence speed of the algorithm; Adaptive perturbation strategy is introduced to increase the randomness of location variation of artificial fish and avoid the algorithm falling into local optimum. Moreover, we present an area optimization method for MPRM logic circuits, which uses the proposed multi-strategy coevolutionary artificial fish swarm algorithm to search for the optimal polarity with the minimum circuit area. The experimental results based on the MCNC Benchmark circuit show that compared with the genetic algorithm, the maximum area saving percentage obtained by this algorithm is 57.24%, and the average area save percentage obtained by this algorithm is 39.57%. Compared with the artificial fish swarm algorithm, the maximum and average area saving percentages obtained by this algorithm are 33.53% and 14.54%, respectively. Compared with the improved artificial fish swarm algorithm, the maximum and average area saving percentages obtained by this algorithm are 30.25% and 13.86%, respectively.