This　paper　proposed　an　improved　version　of　fruit　fly　optimization　(FOA)　to　solve　the　unmanned　aerial　vehicle　(UAV)　path　planning　problem.　The　improved　algorithm　combines　the　phase　angle-encoded　and　mutation　adaptation　mechanisms　into　the　basic　FOA　and　is　referred　to　as　theta-MAFOA.　Mutation　adaptation　mechanism　is　adopted　to　enhance　the　balance　of　FOA　in　terms　of　the　exploitation　and　exploration　ability,　while　phase　angle-based　encoded　strategy　for　fruit　fly　locations　helps　to　achieve　the　high　performance　in　the　convergence　process.　Then,　the　proposed　theta-MAFOA　is　used　to　find　the　optimal　flight　path　for　UAV　in　the　complex　three-dimensional　(3D)　terrain　environments　with　various　ground　defense　weapons.　B-Spline　curve　that　connects　the　start　and　target　points　is　employed　to　represent　a　smooth　path.　Several　performance　criteria　and　constraints　are　taken　into　consideration　to　evaluate　the　cost　of　UAV　paths.　Numerical　experiments　are　carried　out　on　various　test　scenarios　and　the　results　show　the　proposed　theta-MAFOA　is　superior　to　the　basic　and　other　two　modified　versions　of　FOA,　and　also　more　powerful　than　several　existing　state-of-the-art　optimization　algorithms.　(C)　2018　Elsevier　B.V.　All　rights　reserved.