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Tree seed algorithm (TSA) is a heuristic search algorithm that deals with low-dimensional issues. To overcome the deficiency of TSA in solving environmental economic power dispatch (EED) problem with high-dimensional, nonlinear, and non-convex characteristics, this article proposes a novel multiobjective tree seed algorithm (MONTSA), which possesses superior ability to obtain Pareto optimal solution (POS) sets. For expanding the search space as well as balancing the ability of exploration and exploitation about TSA, three modification strategies, including dynamic search tendency (ST), Gaussian mutation mechanism and variation mechanism of differential evolution algorithm based on Cauchy mutation strategy are adopted to constitute MONTSA. In addition, a constraint handling technique to make sure zero constraint violation and non-inferior sorting approach based on dynamic crowding distance are also put forward. The above introduced strategies allow MONTSA to obtain POS with better distribution. Eight cases, containing four objectives of fuel cost, pollution emission, power loss and fuel cost with valve point effect, are experimented on IEEE 30 bus, IEEE 57 bus and IEEE 118 bus test systems. The results reveal that, compared with MOTSA and MOPSO/MODE, MONTSA is effectively capable of addressing EED problem, which is able to obtain uniformly distributed Pareto frontiers (PFs) and better best compromise solution (BCS). Eventually, the performance indicators GD and HV show that PFs and BCS obtained by MONTSA are more outstanding than the rest of algorithms, with uniform distribution, strong convergence and powerful stability. [ABSTRACT FROM AUTHOR]

In this paper, an improved marine predators algorithm (IMPA) is proposed to solve the short-term hydrothermal scheduling (STHS) problem. The marine predators algorithm (MPA) owns low diversity of the initial population and is easy to fall into local optima in the optimization process. Facing these challenges, three improvements are presented. Tent map is applied to initialize the population, which makes the population more uniformly distributed. An average fitness preferential strategy is adopted to improve the quality of population, which provides more possibility for MPA to find better solutions. By segmenting the probability factor in fish aggregating devices (FADs) effect on the optimization process, the premature convergence of MPA is improved. Moreover, a selective repair strategy and an economic priority strategy are proposed to handle dynamic water balance of reservoirs and the power balance, respectively. Three hydrothermal test cases are employed to verify the feasibility and effectiveness of the proposed method, and the results show that IMPA can obtain solutions of high quality. Compared with other methods, IMPA can get better results, which reflects its strong competitiveness in tackling the STHS problem. [ABSTRACT FROM AUTHOR]

With the rapid consumption of fossil fuels, traditional power generation methods not only cannot issue future energy needs, but also bring serious environmental problems. As a clean and renewable energy, wind energy plays an increasingly important role in energy supply structure. However, the wind speed itself is intermittent, unstable and random, which brings severe challenges to wind power generation. Aimed at improving the accuracy and reliability of short-term wind speed forecasting, this paper proposes a new hybrid model. The model includes time-varying filter, modal decomposition, permutation entropy, adaptive noise modal decomposition, adaptive neuro-fuzzy inference system (ANFIS), packet data processing method neural network (GMDH), and improved monarch butterfly optimization algorithm (IMBO). First, the original wind speed sequence is significantly decomposed twice to obtain the sub-sequence to be predicted. Then, the reconstructed data uses ANFIS and GMDH neural network models to predict sequences in different frequency domains to get prediction results. To further improve the performance of the model, the improved monarch butterfly optimization algorithm is used to modify the model parameters. Finally, the final prediction result is obtained by summing the prediction results of each component. In addition, for verifying the performance of the model, this paper designs six sets of comparative experiments from two dimensions to verify the model on three data sets. The results show that the model proposed in this paper has high prediction accuracy and good stability. [ABSTRACT FROM AUTHOR]