Micro Many-Objective Evolutionary Algorithm With Knowledge Transfer

Computational effectiveness and limited resources in evolutionary algorithms are interdependently handled during the working of low-power microprocessors for real-world problems, particularly in many-objective evolutionary algorithms (MaOEAs). In this respect, the balance between them will be broken by evolutionary algorithms with a normal-sized population, but which doesn't include a micro population. To tackle this issue, this paper proposes a micro many-objective evolutionary algorithm with knowledge transfer ($\mu$MaOEA). To address the oversight that knowledge is often not considered enough between niches, the knowledge-transfer strategy is proposed to bolster each unoptimized niche through optimizing adjacent niches, which enables niches to generate better individuals. Meanwhile, a two-stage mechanism based on fuzzy logic is designed to settle the conflict between convergence and diversity in many-objective optimization problems. Through efficient fuzzy logic decision-making, the mechanism maintains different properties of the population at different stages. Different MaOEAs and micro multi-objective evolutionary algorithms were compared on benchmark test problems DTLZ, MaF, and WFG, and the results showed that $\mu$MaOEA has an excellent performance. In addition, it also conducted simulation on two real-world problems, MPDMP and MLDMP, based on a low-power microprocessor. The results indicated the applicability of $\mu$MaOEA for low-power microprocessor optimization.