Area and power optimization for Fixed Polarity Reed-Muller logic circuits based on Multi-strategy Multi-objective Artificial Bee Colony algorithm

Area and power optimization of Fixed Polarity Reed-Muller (FPRM) circuits has received a lot of attention. Polarity optimization for FPRM circuits is essentially a binary multi-objective optimization problem. However, the existing area and power optimization approaches for FPRM logic circuits rarely produce a frontier and a greater number of Pareto optimal solutions. In this paper, a Multi-strategy Multi-objective Artificial Bee Colony (MMABC) algorithm is proposed to solve the binary multi-objective optimization problem. The main innovation of MMABC can be summarized as follows: a flexible foraging behavior strategy for employed bees is proposed to improve the searching ability of the algorithm; a genetic retention evolution for onlooker bees is proposed to improve the quality of the population; an efficient transform strategy is proposed to help the algorithm to jump out the local optimal and increase convergence speed. Moreover, we propose an area and power optimization approach for FPRM logic circuits, which uses the MMABC to search for the polarities (i.e., Pareto optimal solutions) with smaller area and lower power. Experimental results demonstrated the effectiveness and superiority of our approach in optimizing area and power of FPRM logic circuits.

Automated summary

This paper presents a Multi-strategy Multi-objective Artificial Bee Colony (MMABC) algorithm to solve the binary multi-objective optimization problem. The algorithm includes a flexible foraging behavior strategy, a genetic retention evolution, and an efficient transform strategy. Additionally, an area and power optimization approach for FPRM logic circuits is proposed, using the MMABC to search for polarities with smaller area and lower power. Experimental results demonstrate the efficacy and superiority of this approach.