Multiobjective optimization for mixed polarity Reed‒Muller logic circuits based on a multi-strategy quantum wolf pack algorithm

Chengcheng CHEN; Yuhao ZHOU; Juncai HE; Dongge QIN; Zhanhui SHI; Wei ZHANG; Jicong DUAN; Zhenxue HE; Limin XIAO

doi:10.1631/ENG.ITEE.2026.0146

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Multiobjective optimization for mixed polarity Reed‒Muller logic circuits based on a multi-strategy quantum wolf pack algorithm

Regular Papers | Updated：2026-11-05

- Multiobjective optimization for mixed polarity Reed‒Muller logic circuits based on a multi-strategy quantum wolf pack algorithm
- Multiobjective optimization for mixed polarity Reed‒Muller logic circuits based on a multi-strategy quantum wolf pack algorithm
- ENGINEERING Information Technology & Electronic Engineering Vol. 27, Issue 10, Pages: 1-16(2026)
- Affiliations：
  
  School of Future Transportation, Guangzhou Maritime University, Guangzhou 510725, China
  School of Automation and Electrical Engineering, Linyi University, Linyi 276000, China
  Key Laboratory of Agricultural Big Data of Hebei Province, Hebei Agricultural University, Baoding 071001, China
  School of Integrated Circuits, Southeast University, Nanjing 210000, China
  College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310014, China
  School of Artificial Intelligence and Big Data, Suzhou University, Suzhou 234000, China
  School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212003, China
  School of Computer Science and Engineering, Beihang University, Beijing 100191, China
- Author bio：
  
  ✉ Zhanhui SHI, shizhanhui@zjut.edu.cn
- Funds：
- DOI：10.1631/ENG.ITEE.2026.0146
  CLC： TP18
- Received：15 May 2026，
  
  Revised：2026-08-04，
  
  Online First：06 October 2026，
  
  Published：01 October 2026
- Accepted：
Scan QR Code
Chengcheng CHEN, Yuhao ZHOU, Juncai HE, et al. Multiobjective optimization for mixed polarity Reed‒Muller logic circuits based on a multi-strategy quantum wolf pack algorithm[J]. ENGINEERING Information Technology & Electronic Engineering, 2026, 27(10): 1-16.
DOI：

Chengcheng CHEN, Yuhao ZHOU, Juncai HE, et al. Multiobjective optimization for mixed polarity Reed‒Muller logic circuits based on a multi-strategy quantum wolf pack algorithm[J]. ENGINEERING Information Technology & Electronic Engineering, 2026, 27(10): 1-16. DOI： 10.1631/ENG.ITEE.2026.0146.

摘要

Abstract

Area and power need to be considered at all stages of integrated circuit design. Area and power cooperative optimization for mixed polarity Reed‒Muller (MPRM) circuits aims to find the Pareto non-dominated sets with minimum area and power. Existing multiobjective optimization approaches for MPRM circuits have a long runtime and cannot handle large-scale circuits. In this paper

we propose a multi-strategy quantum wolf pack algorithm (MQWPA)

which includes quantum encoding

dual-population strategy

dynamic reverse learning strategy

mutation strategy

and termination criterion. Moreover

based on the proposed MQWPA

we propose an efficient area and power optimization approach (EAPOA)

which searches for XNOR/OR-based MPRM circuits with Pareto non-dominated solution sets. Experimental results show that compared with state-of-the-art evolutionary algorithms

MQWPA can search for optimal solutions in multiobjective test problems; compared with commonly used Boolean minimization methods

EAPOA has advantages in area and power consumption. After field-programmable gate array technology and standard cell mapping

by selecting area as the optimal cost implementation

we obtain average improvements in the actual area of 16.33% and 2.27%

respectively.

关键词

Keywords

references

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