Power is an issue that must be considered in the design of logic circuits. Power optimization is a combinatorial optimization problem, since it is necessary to search for a logical expression that consumes the least amount of power from a large number of Reed-Muller (RM) logical expressions. The existing approach for optimizing the power of multi-output mixed polarity RM (MPRM) logic circuits suffer from poor optimization results. To solve this problem, a whale optimization algorithm with two-populations strategy and mutation strategy (TMWOA) is proposed in this paper. The two-populations strategy speeds up the convergence of the algorithm by exchanging information about the two-populations. The mutation strategy enhances the ability of the algorithm to jump out of the local optimal solutions by using the information of the current optimal solution. Based on the TMWOA, we propose a multi-output MPRM logic circuits power optimization approach (TMMPOA). Experiments based on the benchmark circuits of the Microelectronics Center of North Carolina (MCNC) validate the effectiveness and superiority of the proposed TMMPOA.

In this paper, we propose a multi-strategy synergy ant lion optimization (MSALO) algorithm to address the problems of insufficient optimization efficacy of existing mixed polarity Reed-Muller (MPRM) circuit area optimization methods. A two-strategy random tour mechanism is used in the algorithm’s random tour stage to address the issue of the ant lion optimization (ALO) algorithm’s poor global search ability. A breakout mechanism is used for elite ant lion individuals to address the issue of poor local exploration ability. To expedite the convergence rate of the algorithm, an adaptive ant position update strategy based on the sine function is introduced. The MPRM circuit area optimization approach based on MSALO is proposed to search for the best polarity corresponding to the MPRM logic circuit with the smallest circuit area by using MSALO. The experimental results based on the Microelectronics Center of North Carolina (MCNC) benchmark test circuit demonstrate that the average area savings rate of the area optimization strategy based on MSALO may be increased by an average of 27.96% when compared to the current state-of-the-art swarm intelligence optimization algorithms.