A novel Physarum-inspired competition algorithm for discrete multi-objective optimisation problems

Abstract Many real-world problems can be naturally formulated as discrete multi-objective optimisation (DMOO) problems. We have proposed a novel Physarum-inspired competition algorithm (PCA) to tackle these DMOO Our is based on hexagonal cellular automata (CA) representation of problem search space and reaction–diffusion systems that control the Physarum motility. Physarum’s decision-making power properties CA made our perfectly suitable approach solve Each cell in grid will decoded solution (objective function) regarded food resource attract Physarum. The n-dimensional generalisation has allowed us extend solving capabilities PCA from only 2-D n-D implemented restart procedure select global Pareto frontier both personal experience shared information. Extensive experimental statistical analyses were conducted several benchmark functions assess performance against other evolutionary algorithms. As far we know, this study first attempt algorithms problems, with large number indicators. confirmed assumption individual skills competing are more efficient exploration increase diversity solutions. It achieved best for Spread indicator (diversity), similar results compared strength (SPEA2) even outperformed well-established genetic