Despite　that　evolutionary　and　swarm　intelligence　algorithms　have　achieved　considerable　success　on　multi-objective　optimization　problems,　they　face　huge　challenges　when　dealing　with　many-objective　optimization　problems　(MaOPs).　There　is　an　urgent　call　for　effective　evolutionary　and　swarm　intelligence　algorithms　for　MaOPs.　Inspired　by　the　satisfactory　performance　of　bacterial　foraging　optimization　(BFO)　on　the　single-objective　optimization　problems,　this　paper　extends　BFO　to　deal　with　MaOPs　and　proposes　a　knowledge　guided　BFO　for　MaOPs　(called　as　KLBFO).　Firstly,　KLBFO　learns　promising　direction　knowledge　based　on　group　decision　making　idea　to　guide　the　population　to　converge　toward　proper　directions.　Secondly,　KLBFO　learns　elite　knowledge　by　a　new　biological　mechanism　to　accelerate　the　population　to　converge.　Thirdly,　KLBFO　learns　density　knowledge　by　a　new　diversity　management　strategy　based　on　orthogonal　grid　to　produce　well-distributed　solutions.　The　performance　of　KLBFO　is　comprehensively　evaluated　by　comparing　it　with　eight　state-of-the-art　algorithms　on　two　suites　of　test　problems　and　one　real-world　problem.　The　empirical　results　have　validated　the　superior　performance　of　KLBFO　for　MaOPs.