结合Ising模型的AEIPSO算法优化数字组合逻辑电路
The AEIPSO Algorithm Based on Ising Model Optimizes the Digital Combinational Logic Circuit
DOI: 10.12677/mos.2025.142184, PDF,   
作者: 王瑞祥：南京邮电大学电子与光学工程(柔性电子、未来技术)学院，江苏 南京；曾红丽*：南京邮电大学理学院，江苏 南京
关键词: 组合逻辑电路；优化设计；伊辛模型；混合算法；Combinatorial Logic Circuit； Optimal Design； Ising Model； Hybrid Algorithm
摘要: 粒子群优化算法(PSO)源于人工生命和复杂的自适应系统，近年来在数字电路的设计和优化中得到了应用。人们通过参数调节来改进PSO算法，虽然PSO算法具有参数更少、收敛速度更快等优点，但在迭代进化过程中容易陷入局部最优解，从而导致计算资源的低效使用。针对这一局限性，我们引入了基于Ising模型的改进自适应增强粒子群优化算法(AEIPSO)。提高了进化迭代的效率并且增强了解的多样性。同时，它保留了从PSO算法的快速收敛和卓越的全局搜索能力。实验结果表明，AEIPSO算法在组合逻辑电路的设计和优化方面优于其他PSO算法。
Abstract: Particle swarm optimization (PSO), derived from artificial life and complex adaptive systems, has been applied in the design and optimization of digital circuits in recent years. PSO algorithm is improved by parameter adjustment. Although PSO algorithm has the advantages of fewer parameters and faster convergence, it is easy to fall into local optimal solution during iterative evolution, which leads to inefficient use of computing resources. To address this limitation, we introduce an improved adaptive enhanced particle swarm optimization algorithm (AEIPSO) based on Ising model. It improves the efficiency of evolutionary iterations and increases the diversity of understanding. At the same time, it retains the fast convergence and excellent global search capability from the PSO algorithm. Experimental results show that AEIPSO algorithm is superior to other PSO algorithms in combinatorial logic circuit design and optimization.
文章引用：王瑞祥, 曾红丽. 结合Ising模型的AEIPSO算法优化数字组合逻辑电路[J]. 建模与仿真, 2025, 14(2): 651-670. https://doi.org/10.12677/mos.2025.142184

参考文献

[1] Tocci, R., Widmer, N. and Moss, G. (2006) Digital Systems: Principles and Applications. 10th Edition, Prentice-Hall, Inc.
[2] Karnaugh, M. (1953) The Map Method for Synthesis of Combinational Logic Circuits. Transactions of the American Institute of Electrical Engineers, Part I: Communication and Electronics, 72, 593-599. [Google Scholar] [CrossRef] 
[3] Karakatic, S., Podgorelec, V. and Hericko, M. (2013) Optimization of Combinational Logic Circuits with Genetic Programming. Electronics and Electrical Engineering, 19, 86-89. [Google Scholar] [CrossRef] 
[4] Miller, J.F., Job, D. and Vassilev, V.K. (2000) Principles in the Evolutionary Design of Digital Circuits-Part I. Genetic Programming and Evolvable Machines, 1, 7-35. [Google Scholar] [CrossRef] 
[5] Moore, P.W. and Venayagamoorthy, G.K. (2006) Evolving Digital Circuits Using Hybrid Particle Swarm Optimization and Differential Evolution. International Journal of Neural Systems, 16, 163-177. [Google Scholar] [CrossRef] [PubMed]
[6] Soliman, A.T. and Abbas, H.M. (2003) Combinational Circuit Design Using Evolutionary Algorithms. CCECE 2003, Vol. 1, 251-254.
[7] Abd-El-Barr, M., Sait, S.M., Sarif, B.A.B. and Al-Saiari, U. (2003) A Modified Ant Colony Algorithm for Evolutionary Design of Digital Circuits. The 2003 Congress on Evolutionary Computation, Vol. 1, 708-715.
[8] Pavitra, Y., Arun, E., Jamuna, S. and Manikandan, J. (2018) Study and Evaluation of Digital Circuit Design Using Evolutionary Algorithm. 2018 15th IEEE India Council International Conference (INDICON), Coimbatore, 16-18 December 2018, 1-5. [Google Scholar] [CrossRef] 
[9] Eberhart, R. and Kennedy, J. (1995) A New Optimizer Using Particle Swarm Theory. Proceedings of the 6th International Symposium on Micro Machine and Human Science, Nayoga, 4-6 October 1995, 39-43. [Google Scholar] [CrossRef] 
[10] Coello Coello, C.A., Luna, E.H. and Aguirre, A.H. (2003) Use of Particle Swarm Optimization to Design Combinational Logic Circuits. In: Tyrrell, A.M., Haddow, P.C. and Torresen, J., Eds., Lecture Notes in Computer Science, Springer, 398-409. [Google Scholar] [CrossRef] 
[11] Sagar, K. and Vathsal, S. (2013) Design of Combinational Circuits Using Evolutionary Techniques. International Journal of Science and Modem Engineering, 1, 47-51.
[12] He, R., Wang, J.Y., Wang, Q., Zhou, H.J. and Hu, Y.C. (2005) An Improved Particle Swarm Optimization Based on Self-Adaptive Escape Velocity. Journal of Software, 16, 2036-2044. [Google Scholar] [CrossRef] 
[13] Sabat, S.L. and Ali, L. (2008) Accelerated Exploration Particle Swarm Optimizer-AEPSO. 2008 IEEE Region 10 Conference, Hyderabad, 19-21 November 2008, 1-6. [Google Scholar] [CrossRef] 
[14] Meng, X., Lin, Y. and Qui, D. (2017) Hybrid Algorithm of Adaptive Inertia Weight Particle Swarm and Simulated Annealing. International Journal of Computer Techniques, 4, 105-110.
[15] Atyabi, A., Phon-Amnuaisuk, S. and Ho, C.K. (2008) Cooperative Learning of Homogeneous and Heterogeneous Particles in Area Extension PSO. 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), Hong Kong, 1-6 June 2008, 3889-3896. [Google Scholar] [CrossRef] 
[16] Atyabi, A., Phon-Amnuaisuk, S. and Ho, C.K. (2009) Applying Area Extension PSO in Robotic Swarm. Journal of Intelligent and Robotic Systems, 58, 253-285. [Google Scholar] [CrossRef] 
[17] Yang, Q., Gao, H., Zhang, W. and Li, H. (2016) Simultaneous Hybrid Modeling of a Nosiheptide Fermentation Process Using Particle Swarm Optimization. Chinese Journal of Chemical Engineering, 24, 1631-1639. [Google Scholar] [CrossRef] 
[18] Li, Y., Zhao, P., Guo, B., Zhao, C., Liu, X., He, S., et al. (2021) Design of Combinational Digital Circuits Optimized with Ising Model and PSO Algorithm. 2021 IEEE 15th International Conference on Anti-counterfeiting, Security, and Identification (ASID), Xiamen, 29-31 October 2021, 31-35. [Google Scholar] [CrossRef] 
[19] Shi, Y. and Eberhart, R.C. (1998) Parameter Selection in Particle Swarm Optimization. In: Porto, V.W., Saravanan, N., Waagen, D. and Eiben, A.E., Eds., Evolutionary Programming VII, Springer, 591-600. [Google Scholar] [CrossRef] 

为你推荐