Bacterial　foraging　optimization　(BFO)　has　attracted　much　attention　and　been　widely　applied　in　a　variety　of　scientific　and　engineering　applications　since　its　inception.　However,　the　fixed　step　size　and　a　lack　of　information　communication　between　bacterial　individuals　during　the　optimization　process　have　significant　impacts　on　the　performance　of　BFO　To　address　these　issues　on　real-parameter　single　objective　optimization　problems,　this　paper　proposes　a　new　bacterial　foraging　optimizer　using　new　designed　chemotaxis　and　conjugation　strategies　(BFO-CC).　Via　the　new　chemotaxis　mechanism,　each　bacterium　randomly　selects　a　standard-basis-vector　direction　for　swimming　or　tumbling;　this　approach　may　obviate　calculating　a　random　unit　vector　and　could　effectively　get　rid　of　interfering　with　each　other　between　different　dimensions.　At　the　same　time,　the　step　size　of　each　bacterium　is　adaptively　adjusted　based　on　the　evolutionary　generations　and　the　information　of　the　globally　best　individual,　which　readily　makes　the　algorithm　keep　a　better　balance　between　a　local　search　and　global　search.　Moreover,　the　new　designed　conjugation　operator　is　employed　to　exchange　information　between　bacterial　individuals;　this　feature　can　significantly　improve　convergence.　The　performance　of　the　BFO-CC　algorithm　was　comprehensively　evaluated　by　comparing　it　with　several　other　competitive　algorithms　(based　on　swarm　intelligence)　on　both　benchmark　functions　and　real-world　problems.　Our　experimental　results　demonstrated　excellent　performance　of　BFO-CC　in　terms　of　solution　quality　and　computational　efficiency.　(C)　2016　Elsevier　Inc.　All　rights　reserved.