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In this manuscript, we aim at using consensus in multi-agent systems as a tool for solving large-scale optimization tasks.<\/jats:p>\n              <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Method<\/jats:title>\n                  <jats:p>The model is based on consensus of opinions among agents interacting over a complex networked structure. For each optimization task, a number of agents are considered, each with an opinion value. These agents interact over a networked structure and update their opinions based on their best-matching neighbor in the network. A neighbor with the best value of the objective function (of the optimization task) is referred to as the best-matching neighbor for an agent. We use structures such as pure random, small-world and scale-free networks as interaction graph. The optimization algorithm is applied on a number of benchmark problems and its performance is compared with a number of classic methods including genetic algorithms, differential evolution and particle swarm optimization.<\/jats:p>\n              <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We show that the agents could solve various large-scale optimization tasks through collaborating with each other and getting into consensus in their opinions. Furthermore, we find pure random topology better than small-world and scale-free topologies in that it leads to faster convergence to the optimal solution. Our experiments show that the proposed consensus-based optimization method outperforms the classic optimization algorithms.<\/jats:p>\n              <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>Consensus in multi-agents systems can be efficiently used for large-scale optimization problems. Connectivity structure of the consensus network is effective in the convergence to the optimum solution where random structures show better performance as compared to heterogeneous networks.<\/jats:p>\n              <\/jats:sec>\n               <jats:sec>\n                  <jats:title>AMS subject classification<\/jats:title>\n                  <jats:p>15A04, 54A20, 60J20, 92D25<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/2194-3206-1-11","type":"journal-article","created":{"date-parts":[[2013,4,24]],"date-time":"2013-04-24T12:14:59Z","timestamp":1366805699000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Large-scale global optimization through consensus of opinions over complex networks"],"prefix":"10.1186","volume":"1","author":[{"given":"Omid","family":"Askari-Sichani","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mahdi","family":"Jalili","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2013,4,24]]},"reference":[{"key":"6_CR1","doi-asserted-by":"publisher","first-page":"037104","DOI":"10.1063\/1.2967738","volume":"18","author":"A Ajdari Rad","year":"2008","unstructured":"Ajdari Rad A, Jalili M, Hasler M: Efficient rewirings for enhancing synchronizability of dynamical networks. 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