In　this　paper,　the　optimization　method　of　fuel-reloading　pattern　for　PWR　has　been　studied　based　on　the　improved　convolutional　neural　network　(CNN)　and　genetic　algorithm　(GA).　It　is　very　important　to　search　out　the　optimized　fuel-reloading　pattern　to　guarantee　the　safety　and　economy　of　the　nuclear　power　plants.　During　the　optimization,　large　number　of　fuel-reloading　patterns　should　be　evaluated,　providing　the　core　parameters　(including　the　cycle　length,　power-peak　factors　and　so　on)　to　the　optimization　algorithm　to　search　for　the　optimized　pattern.　In　our　study,　the　CNN　was　improved　with　the　advanced　Inception-ResNet　structure　and　applied　to　train　the　rapid-evaluation　model,　which　can　receive　the　fuel-reloading　patterns　and　feedback　corresponding　core　parameters　with　sufficient　accuracy　and　very-high　efficiency.　The　GA　was　applied　as　the　optimization　algorithm　to　search　for　the　optimized　fuel-reloading　pattern.　This　proposed　optimization　method　has　been　applied　to　the　optimization　of　fuel-reloading　pattern　for　the　CNP1000-type　PWR　reactor　operated　in　China.　It　can　be　observed　that　the　CNN　can　evaluated　the　core　parameters　of　one-single　fuel-reloading　pattern　in　about　0.0005　s　and　the　averaged　evaluation　errors　smaller　than　0.6%;　the　GA　can　search　the　optimized　fuel-reloading　pattern　in　about　20　min.　The　study　in　this　paper　indicated　that　the　combination　of　CNN　and　GA　can　provide　the　optimization　of　fuel-reloading　pattern　for　PWR　in　very-short　time,　which　can　be　applied　to　improve　the　safety　and　economy　of　the　nuclear　power　plants.　©　2022　Elsevier　Ltd