Conventional　algorithms　of　particle　swarm　optimization　(PSO)　are　often　trapped　in　local　optima　in　global　optimization.　In　this　paper,　following　an　analysis　of　the　main　causes　of　the　premature　convergence,　it　proposes　a　novel　PSO　algorithm,　which　is　called　InformPSO,　based　on　the　principles　of　adaptive　diffusion　and　hybrid　mutation.　Inspired　by　the　physics　of　information　diffusion,　a　function　is　designed　to　achieve　a　better　particle　diversity,　by　both　taking　into　account　their　distribution　and　the　number　of　evolutionary　generations　and　adjusting　their　＇social　cognitive＇　abilities.　Based　on　genetic　self-organization　and　chaos　evolution,　clonal　selection　is　built　into　InformPSO　to　implement　the　local　evolution　of　the　best　particle　candidate,　gBest,　and　make　use　of　a　Logistic　sequence　to　control　the　random　drift　of　gBest.　These　techniques　greatly　contribute　to　breaking　away　from　local　optima.　The　global　convergence　of　the　algorithm　is　proved　using　the　theorem　of　Markov　chain.　Experiments　on　optimization　of　unimodal　and　multimodal　benchmark　functions　show　that,　comparing　with　some　other　PSO　variants,　InformPSO　converges　faster,　results　in　better　optima,　is　more　robust,　and　prevents　more　effectively　the　premature　convergence.