Particle　swarm　optimization　is　a　novel　swarm-intelligence-based　algorithm　and　a　valid　optimization　technique.　However,　the　algorithm　suffers　from　the　premature　convergence　problem　when　facing　to　complex　optimization　problem.　In　order　to　keep　the　balance　between　the　global　exploration　and　the　local　exploitation　validly,　the　paper　develops　a　knowledge-based　cooperative　particle　swarm　optimization　(KCPSO).　KCPSO　mainly　simulates　the　self-cognitive　and　self-learning　process　of　evolutionary　agents　in　special　environment,　and　introduces　a　knowledge　billboard　to　record　varieties　of　search　information.　Moreover,　KCPSO　takes　advantage　of　multi-swarm　to　maintain　the　swarm　diversity　and　tries　to　guide　their　evolution　by　the　shared　information.　Under　the　guide　of　the　shared　information,　KCPSO　manipulates　each　sub-swarm　to　go　on　with　local　exploitation　in　different　local　area,　in　which　every　particle　follows　a　social　learning　behavior　mode;　at　the　same　time,　KCPSO　carries　out　the　global　exploration　through　the　escaping　behavior　and　the　cooperative　behavior　of　the　particles　in　different　sub-swarms.　KCPSO　can　maintain　appropriate　swarm　diversity　and　alleviate　the　premature　convergence　validly.　The　proposed　model　was　applied　to　some　well-known　benchmarks.　The　relative　experimental　results　show　KCPSO　is　a　robust　global　optimization　method　for　the　complex　multimodal　functions.　(C)　2008　Elsevier　Inc.　All　rights　reserved.