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Parabolic-trough solar collector fields are large-scale systems, so the application of centralized optimization-based control methods to these systems is often not suitable for real-time control. As such, this paper formulates a novel coalitional control approach as an appropriate alternative to the centralized scheme. The key idea is to split the overall solar collector field into smaller subsystems, each of them governed by a local controller. Then, controllers are clustered into coalitions to solve a local optimization-based problem related to the corresponding subset of subsystems, so that an approximate solution of the original centralized problem can be obtained in a decentralized fashion. However, the operational constraints of the solar collector field couple the optimization problems of the multiple coalitions, thus limiting the ability to solve them in a fully decentralized manner. To overcome this issue, a novel population-dynamics-assisted resource allocation strategy is proposed as a mechanism to decouple the local optimization problems of the multiple coalitions. The proposed coalitional methodology allows to solve the multiple local subproblems in parallel, hence reducing the overall computational burden, while guaranteeing the satisfaction of the operational constraints and without significantly compromising the overall performance. The effectiveness of proposed approach is shown through numerical simulations of a 10- and 100-loop version of the ACUREX solar collector field of Plataforma Solar de Almería , Spain. • Decentralized control of large-scale parabolic-trough solar collector fields. • Reducing the computational burden of model predictive controllers for solar plants. • Splitting large-scale solar plants by clustering model predictive controllers. • Improving the scalability of model predictive control for large-scale solar plants. • Evolutionary game theoretical assistance for model predictive control in solar plants. [ABSTRACT FROM AUTHOR]