| Citation: | SHAO Y X，HE Z X，ZHOU Y H，et al. Area optimization of MPRM circuits based on M-AFSA[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（3）：693-701 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0296
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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.
| [1] |
HE Z X, XIAO L M, GU F, et al. EDOA: An efficient delay optimization approach for mixed-polarity Reed-Muller logic circuits under the unit delay model[J]. Frontiers of Computer Science, 2019, 13(5): 1102-1115. doi: 10.1007/s11704-017-6279-2
|
| [2] |
HE Z X, PAN Y H, WANG K J, et al. Area optimization for MPRM logic circuits based on improved multiple disturbances fireworks algorithm[J]. Applied Mathematics and Computation, 2021, 399: 126008. doi: 10.1016/j.amc.2021.126008
|
| [3] |
HE Z X, LIU J, XIAO L M, et al. A polarity optimization algorithm taking into account polarity conversion sequence[J]. IEEE Access, 2019, 7: 54809-54818. doi: 10.1109/ACCESS.2019.2911355
|
| [4] |
卜登立. 基于混合遗传算法的MPRM最小化[J]. 浙江大学学报(理学版), 2016, 43(2): 184-189.
BU D L. Hybrid genetic algorithm for MPRM minimization[J]. Journal of Zhejiang University (Science Edition), 2016, 43(2): 184-189(in Chinese).
|
| [5] |
CHEN C D, LIN B, ZHU M, et al. Verification method for area optimization of mixed-polarity reed-muller logic circuits[J]. Journal of Engineering Science and Technology Review, 2018, 11(1): 28-34. doi: 10.25103/jestr.111.04
|
| [6] |
俞海珍, 汪鹏君, 张会红, 等. 基于三值多样性粒子群算法的MPRM电路综合优化[J]. 电子学报, 2017, 45(7): 1601-1607. doi: 10.3969/j.issn.0372-2112.2017.07.008
YU H Z, WANG P J, ZHANG H H, et al. Optimization of MPRM circuits based on ternary diversity particle swarm optimization[J]. Acta Electronica Sinica, 2017, 45(7): 1601-1607(in Chinese). doi: 10.3969/j.issn.0372-2112.2017.07.008
|
| [7] |
ZONG Y S, HUANG G Y. Application of artificial fish swarm optimization semi-supervised kernel fuzzy clustering algorithm in network intrusion[J]. Journal of Intelligent & Fuzzy Systems, 2020, 39(2): 1619-1626.
|
| [8] |
ZHOU X B, YU X, ZHANG Y M, et al. Trajectory planning and tracking strategy applied to an unmanned ground vehicle in the presence of obstacles[J]. IEEE Transactions on Automation Science and Engineering, 2021, 18(4): 1575-1589. doi: 10.1109/TASE.2020.3010887
|
| [9] |
LIU Q, ODAKA T, KUROIWA J, et al. An artificial fish swarm algorithm for the multicast routing problem[J]. IEICE Transactions on Communications, 2014, 97(5): 996-1011. doi: 10.1587/transcom.E97.B.996
|
| [10] |
FEI T, ZHANG L Y. Application of BFO-AFSA to location of distribution centre[J]. Cluster Computing, 2017, 20(4): 3459-3474. doi: 10.1007/s10586-017-1144-5
|
| [11] |
LI M, XU J. An AFSA-inspired vector energy routing algorithm based on fluid mechanics[J]. Tehnicki Vjesnik, 2020, 27(1): 290-296.
|
| [12] |
LIU Y, WANG J, SHAHBAZZADE S. The improved AFSA algorithm for the berth allocation and quay crane assignment problem[J]. Cluster Computing, 2019, 22(2): 3665-3672.
|
| [13] |
CHEN Y G, ZHU J Y, WAN L, et al. ACOA-AFSA fusion dynamic coded cooperation routing for different scale multi-hop underwater acoustic sensor networks[J]. IEEE Access, 2020, 8: 186773-186788. doi: 10.1109/ACCESS.2020.3029533
|
| [14] |
WANG S H, ZHANG Z S, REN Y P, et al. UAV photogrammetry and AFSA-Elman neural network in slopes displacement monitoring and forecasting[J]. KSCE Journal of Civil Engineering, 2020, 24(1): 19-29. doi: 10.1007/s12205-020-1697-3
|
| [15] |
LI J P, DONG P W, ZHENG C, et al. PSO-AFSA global maximum power point tracking algorithm with adaptive evolutionary strategy for PV system[C]// Recent Advances in Intelligent Information Hiding and Multimedia Signal Processing. Berlin: Springer, 2019: 60-67.
|
| [16] |
李迎, 张璟, 刘庆, 等. 求解大规模多背包问题的高级人工鱼群算法[J]. 系统工程与电子技术, 2018, 40(3): 710-716. doi: 10.3969/j.issn.1001-506X.2018.03.34
LI Y, ZHANG J, LIU Q, et al. Advanced artificial fish swarm algorithm for large scale multiple knapsack problem[J]. Systems Engineering and Electronics, 2018, 40(3): 710-716(in Chinese). doi: 10.3969/j.issn.1001-506X.2018.03.34
|
| [17] |
夏平凡, 倪志伟, 朱旭辉. 基于烟花进化人工鱼群算法和多重分形的属性选择方法在空气质量预测中应用[J]. 系统科学与数学, 2020, 40(7): 1157-1177. doi: 10.12341/jssms13917
XIA P F, NI Z W, ZHU X H. Attribute selection method based on fireworks evolution artificial fish swarm algorithm and multi-fractal dimension with its application in air quality prediction[J]. Journal of Systems Science and Mathematical Sciences, 2020, 40(7): 1157-1177(in Chinese). doi: 10.12341/jssms13917
|
| [18] |
李晓磊, 钱积新. 基于分解协调的人工鱼群优化算法研究[J]. 电路与系统学报, 2003(1): 1-6.
LI X L, QIAN J X. Studies on artificial fish swarm optimization algorithm based on decomposition and coordination techniques[J]. Journal of Circuits and Systems, 2003(1): 1-6(in Chinese).
|
| [19] |
RAHNAMAYAN S, TIZHOOSH H R, SALAMA M M A. Opposition-based differential evolution[J]. IEEE Transactions on Evolutionary Computation, 2008, 12(1): 64-79. doi: 10.1109/TEVC.2007.894200
|
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