With　the　rapid　development　of　high-speed　rail　(HSR)　systems,　the　security　and　safety　of　these　huge　systems　are　becoming　the　primary　concerns　for　passengers.　HSR　infrastructure　plays　an　important　role　in　HSR　systems,　making　the　maintenance　of　security　and　safety　of　the　HSR　infrastructure　especially　important.　Meanwhile,　sensor　network　technologies　allow　the　realization　of　real-time　and　all-weather　monitoring　of　HSR　infrastructure.　This　paper　analyzes　the　application　requirements　and　characteristics　of　infrastructure　health　monitoring　sensor　network　(IHMSN)　through　construction　of　a　three-layer　IHMSN　which　is　composed　of　end　devices,　repeater　points,　and　access　points.　The　physical　topology　optimization　goal　of　IHMSN　is　to　set　the　optimal　number　of　network　nodes　(namely,　minimum　cost)　as　well　as　the　best　physical　connections.　Given　types　and　amount　of　the　end　devices,　a　multiple　knapsack　model　is　established　which　converts　the　physical　topology　optimization　problems　into　multiple　knapsack　problems.　Based　on　the　different　needs　of　practical　application,　three　different　cases　(basis　case,　adding　devices　case　and　weight-based　case)　are　proposed,　and　the　corresponding　models　are　built.　Some　artificial　intelligence　algorithms　and　a　traditional　dynamic　programming　algorithm　are　presented　to　solve　the　problems.　In　addition,　a　general　algorithmic　finite　state　machine　is　proposed　to　describe　the　solving　process.　After　comparing　these　algorithms　in　execution　time,　memory,　and　optimal　results,　the　genetic　algorithm　and　particle　swarm　optimization　algorithm　stand　out　when　used　to　solve　the　basic　case　as　well　as　the　extended　cases.　The　numerical　results　show　that　these　proposed　models　and　algorithms　can　effectively　solve　the　physical　topology　optimization　problem　of　IHMSN　for　HSR　systems.　Moreover,　these　methods　can　effectively　reduce　network　costs　and　provide　a　theoretical　basis　for　network　communication　link　optimization.　(C)　2015　Elsevier　Ltd.　All　rights　reserved.