Multi-task　optimization　aims　to　solve　multiple　optimization　problems　in　parallel　utilizing　a　single　population.　However,　if　the　computing　resources　are　limited,　allocating　the　same　computing　resources　to　different　tasks　will　cause　resource　waste　and　make　complex　tasks　difficult　to　converge　to　the　optimal　solution.　To　address　this　issue,　a　multi-task　particle　swarm　optimization　with　a　dynamic　on-demand　allocation　strategy　(MTPSO-DA)　is　proposed　to　dynamically　allocate　computing　resources.　First,　a　task　complexity　index,　based　on　convergence　rate　and　contribution　rate,　is　designed　to　evaluate　the　difficulty　of　solving　different　tasks.　Then,　the　complexity　of　different　tasks　can　be　evaluated　in　real　time.　Second,　the　skill　factor　of　the　particle　is　extended　to　a　time-varying　matrix　according　to　the　task　complexity　index.　Then,　the　recently　captured　feedback　is　stored　to　determine　the　computational　resource　demands　of　the　task.　Third,　an　on-demand　allocation　strategy,　based　on　the　time-varying　matrix,　is　developed　to　obtain　the　skill　factor　probability　vector　utilizing　the　attenuation　accumulation　method.　Then,　computing　resources　can　be　allocated　dynamically　among　different　tasks.　Finally,　some　comparative　experiments　are　conducted　based　on　the　benchmark　problem　to　evaluate　the　superiority　of　the　MTPSO-DA　algorithm.　The　results　indicate　that　the　proposed　MTPSO-DA　algorithm　can　achieve　dynamic　resource　allocation.　IEEE