LAUSR

Abstract A novel suite of algorithms, exploiting the concept of Space And Variable decomposition based Evolutionary (SAVE) formulations, has been proposed while addressing the wind farm micro-siting. The efficacies of these algorithms are further enhanced by designing novel repair strategies for the evolutionary operators and adopting intermediate rejuvenation of solution pool with fresh seeding. Optimal placement of wind turbines towards achieving the target of maximization of power (in kW) and minimization of cost avoiding wake effects has been approached through the multi-objective cost-power formulation. Significant improvements in the power production (61–70%) and cost to power ratio (∼14%) have been recorded not only as compared to the most popular benchmark case study reported in the literature, but also as compared to many other improved results reported on the same case study. The decomposition based approaches have been demonstrated to provide better quality Pareto fronts, in terms of uniformity of spread as well as the convergence of the solutions, over their continuous variable solution methodologies. Moreover, the use of repair strategy helped to improve solution quality to the tune of 9% improvement in generated power on another realistic case study as compared to the case when repair is not used. The approach is extremely generic and can be applied to other optimization problems of interest.