Micro multi-objective genetic algorithm with information fitting strategy for low-power microprocessor

Abstract

Micro multi-objective evolutionary algorithms ($\mu$MOEAs) are designed to address multi-objective optimization problems (MOPs), particularly in low-power microprocessor where computing resources are constrained. However, to compensate for the diversity loss resulting from using a micro population, existing optimization methods in numerous $\mu$MOEAs lead to diminished competitiveness over time due to the absence of targeted feedback on population states, hindering further performance improvement. To address this challenge, a micro multi-objective genetic algorithm with information fitting strategy for low-power microprocessor($\mu$MOGAIF) is proposed, which utilizes an information fitting strategy to monitor the evolutionary status of the population and to facilitate method selection. The status information is collected at each iteration and fitted regularly, and the evaluation indicator is adjusted by the fitted evaluation results. In addition, adaptive mating selection is used in the construction of the mating pool to enhance the exploitation of solutions in probable regions. To enhance the adaptability of $\mu$MOGAIF, dual archives are established, one archive compensates the output using various strategies to pursue convergence or diversity, while the other provides the final output set. $\mu$MOGAIF is compared with five state-of-the-art MOEAs and five $\mu$MOEAs on the DTLZ, WFG, MaF, and ZDT benchmark test suites, and the experimental results demonstrate that $\mu$MOGAIF has outstanding performance. Furthermore, simulations based on low-power microprocessor have been conducted to verify the feasibility of $\mu$MOGAIF.