Abstract
Population-based evolution algorithms imitate lots of swarm-based behavior of creatures and even natural phenomena. In the entire evolutionary process, randomness and diversity make solution set convergence to the global optima quickly, and at the same time, make them jump out local optimums. However, this search mode based on the solution pool needs much memory space to store real solutions. The blindness of search will inevitably increase the time-consuming. Solving these disadvantages has been a hot topic in research, which is the goal of our work. In this paper, we propose a novel optimization algorithm based on Beta distribution. A known probability distribution, simulated by Beta distribution, transforms the solution set into a sampling sample. The specimen keeps updating toward the direction we expect, according to the characteristics of the probability distribution. This model reduces the memory space occupied by candidate solutions and also reduces the complexity of the algorithm. Finally, we analyzed and verified the performance of the proposed algorithm through numerical experiments, especially when dealing with large-dimensional problems. And its performance at power load forecasting has also proved its advantages in handling big data.
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Acknowledgments
This work is supported by the National Natural Science Foundations of China (No. 61872085), the Scientific Research Project of Fujian Education Department (JK2017029, JAT190069), and the Scientific Research and Development Foundation of Fujian University of Technology (GY-Z18181, GY-Z20068, XF-X19017).
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Wang, J., Chu, SC., Liu, SJ., Pan, JS. (2022). A Beta Distribution Based Optimization Algorithm and Its Application in Power Load Forecasting. In: Chu, SC., Lin, J.CW., Li, J., Pan, JS. (eds) Genetic and Evolutionary Computing. ICGEC 2021. Lecture Notes in Electrical Engineering, vol 833. Springer, Singapore. https://doi.org/10.1007/978-981-16-8430-2_3
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DOI: https://doi.org/10.1007/978-981-16-8430-2_3
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