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Economic Dispatch in Cogeneration Systems (EDCS) provides the optimal scheduling of heat and power of generation units. This can be achieved by minimizing the total cost of fuel (TCF) of the cogeneration units taking into consideration their operational limits. A manta ray foraging MRF optimizer, in this paper, is developed to solve the EDCS problem including the valve point impacts, and wind power. MRF optimizer is designed with adaptive penalty functions for acquiring the most feasible and best operational points for the EDCS problem. Infeasible solutions are handled with various degrees and penalized depending on their remoteness from the closest possible point. The overall power and heat loading are completely achieved by the equality constraints. Also, the cogeneration units' dynamic operating limits are not adversely affected since its concerning limitations of heat-only and power-only units are fulfilled. Two test systems of small 5 and large 96-units, are analyzed. In addition to this, an assessment of the recent optimization techniques, which are applied on to EDCS, has been developed and discussed. The applications are carried out for two scenarios at peak and daily variation in the power and heat loading condition. The wind power inclusion is assessed for each scenario in terms of the overall reduction in the total fuel costs. It was proven also; the inclusion of wind power achieves more economical solution at different scenarios with reduction up to 8%. It is crystal clear that the outputs obtained illustrate MRF optimizer efficiency, feasibility, and capability to obtain better solutions in minimizing the fuel cost compared to other optimization techniques at acceptable convergence rates. Moreover, the solutions demonstrate the ability of MRF optimizer application on the large-scale 96-unit systems. • A manta ray foraging (MRF) optimizer is developed to solve the EDCS problem. • The EDCS problem involves the valve point impacts, and wind power inclusion. • MRFO is designed with adaptive penalty for acquiring the best EDCS points. • Two scenarios at peak and daily variation in the power and heat loads. • Inclusion of wind power achieves economical solutions for different scenarios. [ABSTRACT FROM AUTHOR]