An　optimization　algorithm,　inspired　by　the　animal　Behavioral　Ecology　Theory—Optimal　Foraging　Theory,　named　the　Optimal　Foraging　Algorithm　(OFA)　has　been　developed.　As　a　new　stochastic　search　algorithm,　OFA　is　used　to　solve　the　global　optimization　problems　following　the　animal　foraging　behavior.　During　foraging,　animals　know　how　to　find　the　best　pitch　with　abundant　prey;　in　establishing　OFA,　the　basic　operator　of　OFA　was　constructed　following　this　foraging　strategy.　During　foraging,　an　individual　of　the　foraging　swarms　obtained　more　opportunities　to　capture　prey　through　recruitment;　in　OFA　the　recruitment　was　adopted　to　ensure　the　algorithm　has　a　higher　chance　to　receive　the　optimal　solution.　Meanwhile,　the　precise　model　of　prey　choices　proposed　by　Krebs　et　al.　was　modified　and　adopted　to　establish　the　optimal　solution　choosing　strategy　of　OFA.　The　OFA　was　tested　on　the　benchmark　functions　that　present　difficulties　common　to　many　global　optimization　problems.　The　performance　comparisons　among　the　OFA,　real　coded　genetic　algorithms　(RCGAs),　Differential　Evolution　(DE),　Particle　Swarm　Optimization　(PSO)　algorithm,　Bees　Algorithm　(BA),　Bacteria　Foraging　Optimization　Algorithm　(BFOA)　and　Shuffled　Frog-leaping　Algorithm　(SFLA)　are　carried　out　through　experiments.　The　parameter　of　OFA　and　the　dimensions　of　the　multi-functions　are　researched.　The　results　obtained　by　experiments　and　Kruskal-Wallis　test　indicate　that　the　performance　of　OFA　is　better　than　the　other　six　algorithms　in　terms　of　the　ability　to　converge　to　the　optimal　or　the　near-optimal　solutions,　and　the　performance　of　OFA　is　the　second-best　one　from　the　view　of　the　statistical　analysis.　©　2016　Elsevier　B.V.