The　optimal　foraging　algorithm　(OFA)　is　a　swarm-based　algorithm　motivated　by　animal　behavioral　ecology　theory.　When　solving　complex　optimization　problems　characterized　by　multiple　peaks,　OFA　is　easy　to　get　trapped　in　local　minima　and　encounters　slow　convergence.　Therefore,　this　paper　presents　an　improved　optimal　foraging　algorithm　with　social　behavior　based　on　quasi-opposition　(QOS-OFA)　to　address　these　problems.　First,　quasi-opposition-based　learning　(QOBL)　is　introduced　to　improve　the　overall　quality　of　the　population　in　the　initialization　phase.　Second,　an　efficient　cosine-based　scale　factor　is　designed　to　accelerate　the　exploration　of　the　search　space.　Third,　a　new　search　strategy　with　social　behavior　is　designed　to　enhance　local　exploitation.　The　cosine-based　scale　factor　is　used　as　a　regulator　to　achieve　a　balance　between　global　exploration　and　local　exploitation.　The　proposed　QOS-OFA　is　compared　with　seven　meta-heuristic　algorithms　on　a　CEC　benchmark　test　suite　and　three　real-world　optimization　problems.　The　experimental　results　show　that　QOS-OFA　is　better　than　other　competitors　on　most　of　the　test　problems.