Several optimization problems from various types of applications have been efficiently resolved using available meta-heuristic algorithms such as Particle Swarm Optimization and Genetic Algorithm. Recently, many meta-heuristic optimization techniques have been extensively reported in the literature. Nevertheless, there is still room for new optimization techniques and strategies since, according to the literature, there is no meta-heuristic optimization algorithm that may be considered as the best choice to cope with all modern optimization problems. This paper introduces a novel meta-heuristic optimization algorithm named Dynamic Group-based Optimization Algorithm (DGCO). The proposed algorithm is inspired by the cooperative behavior adopted by swarm individuals to achieve their global goals. DGCO has been validated and tested against twenty-three mathematical optimization problems, and the results have been verified by a comparative study with respect to state-of-the-art optimization algorithms that are already available. The results have shown the high exploration capabilities of DGCO as well as its ability to avoid local optima. Moreover, the performance of DGCO has also been verified against five constrained engineering design problems. The results demonstrate the competitive performance and capabilities of DGCO with respect to well-known state-of-the-art meta-heuristic optimization algorithms. Finally, a sensitivity analysis is performed to study the effect of different parameters on the performance of the DGCO algorithm.

The ability to predict solar radiation one-day-ahead is critical for the best management of renewable energy tied-grids. Several machine learning ensemble techniques have been proposed to enhance the short-term prediction of solar radiation strength. In general, finding an optimal ensemble model that consists of combining individual predictors is not trivial due to the need of tuning and other related issues. Few comparative studies have been presented to obtain optimal structures of machine learning ensemble that deal with predicting solar radiation. The contribution of the present research consists of a comparative study of various structures of stacking-based ensembles of data-driven machine learning predictors that are widely used nowadays to conclude the best stacking strategies in term of performance to combine predictors of solar radiation. The base individual predictors are arranged to predict solar radiation intensity using historical weather and solar radiation records. Three stacking techniques, namely, feed-forward neural networks, support vector regressors, and k-nearest neighbor regressors, have been examined and compared to combine the prediction outputs of base learners. Most of examined stacking models have been found capable to predict the solar radiation, but those related to combining heterogeneous models using Neural meta-models have shown superior performance. Furthermore, we have compared the performance of combined models against recurrent models. The solar radiation predictions of the surveyed models have been evaluated and compared over an entire year. The performance enhancements provided by each alternative ensemble have been discussed.

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