In　this　article,　a　swarm-intelligence-based　value　iteration　(VI)　algorithm　is　constructed　to　resolve　the　optimal　control　issue　for　continuous-time　(CT)　nonlinear　systems.　By　leveraging　the　evolutionary　concept　of　particle　swarm　optimization　(PSO),　the　challenge　of　gradient　vanishing　is　effectively　overcome　compared　to　traditional　adaptive　dynamic　programming　(ADP).　Specifically,　a　PSO-based　action　network　is　implemented　to　perform　policy　improvement,　eliminating　the　reliance　on　gradient　information.　Furthermore,　within　the　ADP　framework,　the　swarm-intelligence-based　VI　algorithm　for　CT　systems　is　developed　to　address　the　challenges　associated　with　constraints　of　initial　admissible　conditions　and　the　difficulty　of　selecting　probing　signals　in　the　traditional　policy　iteration　method.　The　theoretical　analysis　is　provided　to　show　the　convergence　of　the　developed　VI　algorithm　and　the　stability　of　the　closed-loop　system,　respectively.　Finally,　under　affine　and　non-affine　backgrounds,　two　simulations　are　conducted　to　demonstrate　the　effectiveness　and　optimality　of　the　established　swarm-intelligence-based　VI　scheme　for　CT　systems.