changes and 2) for the problems in which the cost of switching the deployed solution and the benefits from deploying a candidate solution are taken into account. In addition to ROOT, this research investigates large-scale DOPs. In this thesis, the challenges of large-scale DOPs are studied, a scalable benchmark generator with controllable variable interaction structure is proposed, and a novel algorithm is designed. The proposed algorithm benefits from different components including a bi-level multi-population controlling, cooperative coevolution, and a bi-level computational resource allocation. The reported results in this thesis demonstrate the superiority of the proposed methods in comparison to the peer algorithms. Several publications are generated by the research reported in this thesis, including the ones published in the IEEE Transactions on Evolutionary Computation. FEBRUARY 2023 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 9http://www.ieee.org http://techrxiv.org