A　particle　swarm　algorithm　is　proposed　based　on　the　gray　entropy　parallel　analysis　method　to　solve　the　multi-objective　optimization　problems.　The　gray　entropy　parallel　analysis　method　combines　the　characteristics　of　the　grey　correlation　analysis　method　and　information　entropy.　The　grey　correlation　coefficient　of　the　data　sequence　is　calculated,　meanwhile,　the　information　entropy　and　the　entropy　weight　are　also　calculated,　then　the　grey　entropy　parallel　correlation　degree　is　got　by　combining　the　grey　relational　coefficient　with　the　en-tropy　weight.　The　objective　value　sequence　of　the　multi-objective　optimization　problem　is　established　by　the　particle　swarm　algorithm　and　the　number　of　the　objective　value　sequence　equals　to　the　number　of　particles　in　the　algorithm.　The　value　of　grey　entropy　parallel　correlation　degree　of　each　sequence　is　calculated　and　used　as　the　distribution　strategy　of　the　fitness　value　to　guide　the　particle　evolution.　Ten　typical　job　shop　scheduling　problems　are　tested　by　the　proposed　method,　and　the　results　are　compared　with　results　gained　by　the　differential　evolution　algorithm　and　the　genetic　algorithm.　The　experimental　results　show　that　the　grey　entropy　parallel　analysis　method　can　guide　the　algorithm　evolution　effectively　with　good　convergence　and　distribution　performance,　and　the　optimization　results　of　particle　swarm　algorithm　are　better　than　those　of　the　other　two　algorithms.　©,　2014,　Chinese　Institute　of　Electronics.　All　right　reserved.