Optimization is an art that is best performed by a well-tuned algorithm. Nature – instead of being fully deterministic – is evolutionary, vibrant and resourceful. The nature-inspired algorithms use the best combination and evolution strategy in a given situation. In this work, a new metaheuristic algorithm is developed by using social behavior in human dynasties. The motivation, conceptual framework, mathematical model, pseudocode and working of the algorithm are described in this paper and the adjoining papers. The proposed dynastic optimization algorithm (DOA) has evolved with the wind turbine micrositing (WTM) problem in mind. The proposed DOA has been successfully applied to the traditional WTM and encouraging results have been obtained. It is demonstrated that the proposed approach is equally viable as other existing algorithms, like the Genetic algorithm (GA) and Differential evolution algorithm (DEA). The main advantage of the proposed DOA is that it is simple, unique, fast, unbiased and versatile in comparison with others. The validation of results has been made with respect to a few other mainstream algorithms in the literature, besides statistical sensitivity analysis is also performed. The 95% confidence interval forecasts for the power enhancement and cost reduction by using DOA against GA and DEA are encouraging and guarantee an adequate amount of mean increase in power output and a considerable average cost reduction. •The proposed metaheuristic optimization algorithm is inspired from human dynasties.•Full application is made to the Wind Turbine Micrositing (WTM) problem.•The mathematics, pseudocode, working, advantages and application are discussed.•The DOA reveals superior results as compared to the GA and DEA.•95% confidence bounds indicate that future forecasts are encouraging.