For　high-dimensional　multi-objective　Permutation　Flow-shop　Scheduling　Problems　(PFSP),　an　Optimal　Foraging　Algorithm　(OFA)　based　on　Cumulative　Prospect　Theory　(CPT-OFA)　was　presented　to　minimize　the　makespan,　maximum　delay　time,　inventory　cost　and　delay　cost.　In　this　algorithm,　the　grey　relational　analysis,　the　entropy　theory　and　cumulative　prospect　theory　were　combined.　The　comprehensive-prospect-value　model　of　the　Pareto　solution　was　established　by　setting　reference　points,　defining　the　value　function　and　the　attribute　weight.　The　weights　of　different　objectives　were　evaluated　with　entropy　theory.　The　prospect　value　was　adopted　to　evaluate　the　quality　of　the　Pareto　solution　and　used　as　the　fitness　value　to　guide　the　evolution　of　the　algorithm.　Moreover,　the　opposite　search　mechanism　was　applied　in　OFA　to　avoid　the　entrapment　into　local　optima　and　to　enhance　the　search　ability.　The　simulation　experiments　were　carried　out　with　PFSP　benchmark　instances　and　a　practical　PFSP　to　check　the　validity　of　the　proposed　algorithm.　The　experimental　results　demonstrated　that　CPT-OFA　was　superior　to　three　relatively　novel　multi-objective　optimization　algorithms,　and　could　obtain　high-quality　Pareto　solution　for　the　multi-objective　PFSPs.　©　2022,　Editorial　Department　of　CIMS.　All　right　reserved.