Particle　swarm　optimization　(PSO)　algorithm　is　a　well-known　optimization　approach　to　deal　with　discrete　problems.　There　are　two　models　proposed　for　the　operators　of　PSO　algorithm,　one　is　based　on　value　exchange　and　the　other　on　order　exchange,　accordingly　two　versions　of　PSO　algorithms　are　formed.　A　new　version　of　PSO　algorithm　based　on　order　exchange　has　been　presented　in　our　studies,　which　is　capable　of　converging　on　the　global　optimization　solution,　with　the　method　of　generating　the　stop　evolution　particle　over　again.　In　this　paper,　we　propose　another　version　of　PSO　algorithm　based　on　value　exchange　with　the　same　method.　There　exist,　thus,　totally　four　versions　of　PSO　algorithms,　which　is　given　a　brief　introduction　individually　and　the　performance　of　which　are　compared　in　solving　sequence　optimization　problems　through　fifty　runs.　The　performance　comparison　show　that　the　PSO　algorithm　with　global　convergence　characteristics　based　on　order　exchange　outperforms　the　other　versions　of　PSO　in　solving　sequence　optimization　problem.　©　2011　Elsevier　Ltd.　All　rights　reserved.