The　particle　swarm　optimization　(PSO)　algorithm　is　simple　to　implement　and　converges　quickly,　but　it　easily　falls　into　a　local　optimum;　on　the　one　hand,　it　lacks　the　ability　to　balance　global　exploration　and　local　exploitation　of　the　population,　and　on　the　other　hand,　the　population　lacks　diversity.　To　solve　these　problems,　this　paper　proposes　an　improved　adaptive　inertia　weight　particle　swarm　optimization　(AIWPSO)　algorithm.　The　AIWPSO　algorithm　includes　two　strategies:　(1)　An　inertia　weight　adjustment　method　based　on　the　optimal　fitness　value　of　individual　particles　is　proposed,　so　that　different　particles　have　different　inertia　weights.　This　method　increases　the　diversity　of　inertia　weights　and　is　conducive　to　balancing　the　capabilities　of　global　exploration　and　local　exploitation.　(2)　A　mutation　threshold　is　used　to　determine　which　particles　need　to　be　mutated.　This　method　compensates　for　the　inaccuracy　of　random　mutation,　effectively　increasing　the　diversity　of　the　population.　To　evaluate　the　performance　of　the　proposed　AIWPSO　algorithm,　benchmark　functions　are　used　for　testing.　The　results　show　that　AIWPSO　achieves　satisfactory　results　compared　with　those　of　other　PSO　algorithms.　This　outcome　shows　that　the　AIWPSO　algorithm　is　conducive　to　balancing　the　abilities　of　the　global　exploration　and　local　exploitation　of　the　population,　while　increasing　the　diversity　of　the　population,　thereby　significantly　improving　the　optimization　ability　of　the　PSO　algorithm.