Training　optimization　plays　a　vital　role　in　the　development　of　convolution　neural　network　(CNN).　CNNs　are　hard　to　train　because　of　the　presence　of　multiple　local　minima.　The　optimization　problem　for　a　CNN　is　non-convex,　hence,　has　multiple　local　minima.　If　any　of　the　chosen　hyper-parameters　are　not　appropriate,　it　will　end　up　at　bad　local　minima,　which　leads　to　poor　performance.　Hence,　proper　optimization　of　the　training　algorithm　for　CNN　is　the　key　to　converge　to　a　good　local　minimum.　Therefore,　in　this　paper,　we　introduce　an　evolutionary　convolution　neural　network　(ModPSO-CNN)　algorithm.　The　proposed　algorithm　results　in　the　fusion　of　modified　particle　swarm　optimization　(ModPSO)　along　with　backpropagation　(BP)　and　convolution　neural　network　(CNN).　The　training　of　CNN　involves　ModPSO　along　with　backpropagation　(BP)　algorithm　to　encourage　performance　improvement　by　avoiding　premature　convergence　and　local　minima.　The　ModPSO　have　adaptive,　dynamic　and　improved　parameters,　to　handle　the　issues　in　training　CNN.　The　adaptive　and　dynamic　parameters　bring　a　proper　balance　between　the　global　and　local　search　ability,　while　an　improved　parameter　keeps　the　diversity　of　the　swarm.　The　proposed　ModPSO　algorithm　is　validated　on　three　standard　mathematical　test　functions　and　compared　with　three　variants　of　the　benchmark　PSO　algorithm.　Furthermore,　the　performance　of　the　proposed　ModPSO-CNN　is　also　compared　with　other　training　algorithms　focusing　on　the　analysis　of　computational　cost,　convergence　and　accuracy　based　on　a　standard　problem　specific　to　classification　applications,　such　as　CIFAR-10　dataset　and　face　and　skin　detection　dataset.