Load-shedding　is　an　intentional　reduction　approach　which　can　maintain　the　stability　of　a　microgrid　system　effectively.　Recent　studies　have　shown　that　a　load-shedding　problem　can　be　solved　by　formulating　it　as　a　0/1　knapsack　problem　(KP).　Although　approximate　solutions　of　0/1　KP　can　be　given　by　ant　colony　optimization　(ACO)　algorithms,　adopting　them　requests　a　delicate　consideration　of　the　robustness,　convergence　rate　and　premature　convergence.　This　paper　proposes　a　new　kind　of　Physarum-based　hybrid　optimization　algorithm,　denoted　as　PM-ACO,　based　on　the　critical　paths　reserved　feature　of　Physaruminspired　mathematical　(PM)　model.　Through　adding　additional　pheromone　to　those　important　items　selected　by　the　PM　model,　PM-ACO　improves　the　selection　probability　of　important　items　and　emerge　a　positive　feedback　process　to　generate　optimal　solutions.　Comparing　with　other　0/1　KP　solving　algorithms,　our　experimental　results　demonstrate　that　PM-ACO　algorithms　have　a　stronger　robustness　and　a　higher　convergence　rate.　Moreover,　PM-ACO　provides　adaptable　solutions　for　the　load-shedding　problem　in　a　microgrid　system.　(C)　2017　Elsevier　B.V.　All　rights　reserved.