Aiming　at　the　disadvantages　of　existing　optimization　methods　on　solving　high-dimensional　multi-objective　optimization　problem,　the　multi-objective　solution　was　mapped　into　fuzzy　set,　and　the　relative　entropy　of　fuzzy　sets　representing　the　similar　degree　between　fuzzy　sets　were　used　to　solve　multi-objective　optimization　problem.　A　multi-objective　optimization　method　based　on　relative　entropy　of　fuzzy　sets　was　presented,　and　the　size　of　fuzzy　relational　entropy　coefficient　was　used　to　measure　similar　degree　between　the　fuzzy　sets　of　Pareto　solutions　and　the　fuzzy　set　of　ideal　solution.　The　coefficient　used　as　Particle　Swarm　Optimization　(PSO)　fitness　was　applied　to　guide　algorithm　evolution,　therefore　the　multi-objective　PSO　based　on　relative　entropy　of　fuzzy　sets　was　established.　Experiments　showed　that　PSO　based　on　relative　entropy　of　fuzzy　sets　could　solve　high-dimensional　multi-objective　flow　shop　scheduling　problem　effectively.　The　optimization　solution　and　performance　indicators　of　the　algorithm　were　better　than　PSO　based　on　random　weight.　Especially　in　solving　large-scale　problems,　the　performance　of　PSO　based　on　relative　entropy　of　fuzzy　sets　was　much　better.　©　2015,　CIMS.　All　right　reserved.