A　new　optimization　algorithm,　inspired　by　the　animal　Behavioral　Ecology　Theory　-　Optimal　Foraging　Theory,　called　the　optimal　foraging　algorithm　(OFA),　has　been　developed.　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.　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　more　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　performance　comparisons　among　the　OFA,　real　coded　genetic　algorithms　(RCGAs),　Differential　Evolution　(DE)　and　Particle　Swarm　Optimization　algorithm　(PSO)　were　carried　out　through　experiments.　The　experiment　results　indicated　that　OFA　outperformed　the　other　three　algorithms　in　terms　of　the　ability　to　converge　to　the　optimal　or　near-optimal　solutions.　©　2011　ICIC　International.